diff --git a/01-spatial-data.html b/01-spatial-data.html
index 1816a3cf..cfd4595a 100644
--- a/01-spatial-data.html
+++ b/01-spatial-data.html
@@ -418,17 +418,17 @@ <h3 data-number="1.2.1" class="anchored" data-anchor-id="vector-data-classes"><s
 <section id="sec-vector-layers" class="level3" data-number="1.2.2">
 <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><span class="header-section-number">1.2.2</span> Vector layers</h3>
 <p>The most commonly used geographic vector data structure is the vector layer. There are several approaches for working with vector layers in Python, ranging from low-level packages (e.g., <strong>osgeo</strong>, <strong>fiona</strong>) to the relatively high-level <strong>geopandas</strong> package that is the focus of this section. Before writing and running code for creating and working with geographic vector objects, we need to import <strong>geopandas</strong> (by convention as <code>gpd</code> for more concise code) and <strong>shapely</strong>.</p>
-<div id="6447b590" class="cell" data-execution_count="4">
+<div id="fbbc6ca0" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> shapely</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> geopandas <span class="im">as</span> gpd</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We also limit the maximum number of printed rows to six, to save space, using the <code>'display.max_rows'</code> option of <strong>pandas</strong>.</p>
-<div id="5bfd1a41" class="cell" data-execution_count="5">
+<div id="8633234d" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>pd.set_option(<span class="st">'display.max_rows'</span>, <span class="dv">6</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Projects often start by importing an existing vector layer saved as a GeoPackage (<code>.gpkg</code>) file, an ESRI Shapefile (<code>.shp</code>), or other geographic file format. The function <code>gpd.read_file</code> imports a GeoPackage file named <code>world.gpkg</code> located in the <code>data</code> directory of Python’s working directory into a <code>GeoDataFrame</code> named <code>gdf</code>.</p>
-<div id="04201980" class="cell" data-execution_count="7">
+<div id="f0e53c56" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>gdf <span class="op">=</span> gpd.read_file(<span class="st">'data/world.gpkg'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The result is an object of type (class) <code>GeoDataFrame</code> with 177 rows (features) and 11 columns, as shown in the output of the following code:</p>
@@ -438,14 +438,14 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
 <pre><code>geopandas.geodataframe.GeoDataFrame</code></pre>
 </div>
 </div>
-<div id="7ccbb60c" class="cell" data-execution_count="9">
+<div id="6c695b55" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>gdf.shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="9">
 <pre><code>(177, 11)</code></pre>
 </div>
 </div>
 <p>The <code>GeoDataFrame</code> class is an extension of the <code>DataFrame</code> class from the popular <strong>pandas</strong> package <span class="citation" data-cites="pandas">(<a href="references.html#ref-pandas" role="doc-biblioref">McKinney 2010</a>)</span>. This means we can treat non-spatial attributes from a vector layer as a table, and process them using the ordinary, i.e., non-spatial, established function methods. For example, standard data frame subsetting methods can be used. The code below creates a subset of the <code>gdf</code> dataset containing only the country name and the geometry.</p>
-<div id="ee68b86d" class="cell" data-execution_count="10">
+<div id="c4de3af8" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>gdf <span class="op">=</span> gdf[[<span class="st">'name_long'</span>, <span class="st">'geometry'</span>]]</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>gdf</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
@@ -504,7 +504,7 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
 </div>
 </div>
 <p>The following expression creates a subdataset based on a condition, such as equality of the value in the <code>'name_long'</code> column to the string <code>'Egypt'</code>.</p>
-<div id="b58ac3a7" class="cell" data-execution_count="11">
+<div id="a5641574" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>gdf[gdf[<span class="st">'name_long'</span>] <span class="op">==</span> <span class="st">'Egypt'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="11">
 <div>
@@ -579,7 +579,7 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
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                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_532284f7269d428b0553fa806f00519a {
+                #map_392cb7214998cea393ff6625c6c66dac {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -609,14 +609,14 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_532284f7269d428b0553fa806f00519a&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_392cb7214998cea393ff6625c6c66dac&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_532284f7269d428b0553fa806f00519a = L.map(
-                &quot;map_532284f7269d428b0553fa806f00519a&quot;,
+            var map_392cb7214998cea393ff6625c6c66dac = L.map(
+                &quot;map_392cb7214998cea393ff6625c6c66dac&quot;,
                 {
                     center: [-3.1274349999999984, -5.000000001587068e-06],
                     crs: L.CRS.EPSG3857,
@@ -625,79 +625,79 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_532284f7269d428b0553fa806f00519a);
+            L.control.scale().addTo(map_392cb7214998cea393ff6625c6c66dac);
 
             
 
         
     
-            var tile_layer_713f186039099f55da0b69ae67503641 = L.tileLayer(
+            var tile_layer_3d49753f056bcd4d17b3a3496205d395 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_713f186039099f55da0b69ae67503641.addTo(map_532284f7269d428b0553fa806f00519a);
+            tile_layer_3d49753f056bcd4d17b3a3496205d395.addTo(map_392cb7214998cea393ff6625c6c66dac);
         
     
-            map_532284f7269d428b0553fa806f00519a.fitBounds(
+            map_392cb7214998cea393ff6625c6c66dac.fitBounds(
                 [[-89.9, -180.0], [83.64513000000001, 179.99999]],
                 {}
             );
         
     
-        function geo_json_b68770c5261c558221d729adc90b10a7_styler(feature) {
+        function geo_json_484856c7328cdac24c399d8d28e58862_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_b68770c5261c558221d729adc90b10a7_highlighter(feature) {
+        function geo_json_484856c7328cdac24c399d8d28e58862_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_b68770c5261c558221d729adc90b10a7_pointToLayer(feature, latlng) {
+        function geo_json_484856c7328cdac24c399d8d28e58862_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_b68770c5261c558221d729adc90b10a7_styler(feature)
+            let style = geo_json_484856c7328cdac24c399d8d28e58862_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_b68770c5261c558221d729adc90b10a7_onEachFeature(feature, layer) {
+        function geo_json_484856c7328cdac24c399d8d28e58862_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_b68770c5261c558221d729adc90b10a7.resetStyle(e.target);
+                            geo_json_484856c7328cdac24c399d8d28e58862.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_b68770c5261c558221d729adc90b10a7_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_484856c7328cdac24c399d8d28e58862_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_b68770c5261c558221d729adc90b10a7 = L.geoJson(null, {
-                onEachFeature: geo_json_b68770c5261c558221d729adc90b10a7_onEachFeature,
+        var geo_json_484856c7328cdac24c399d8d28e58862 = L.geoJson(null, {
+                onEachFeature: geo_json_484856c7328cdac24c399d8d28e58862_onEachFeature,
             
-                style: geo_json_b68770c5261c558221d729adc90b10a7_styler,
-                pointToLayer: geo_json_b68770c5261c558221d729adc90b10a7_pointToLayer,
+                style: geo_json_484856c7328cdac24c399d8d28e58862_styler,
+                pointToLayer: geo_json_484856c7328cdac24c399d8d28e58862_pointToLayer,
         });
 
-        function geo_json_b68770c5261c558221d729adc90b10a7_add (data) {
-            geo_json_b68770c5261c558221d729adc90b10a7
+        function geo_json_484856c7328cdac24c399d8d28e58862_add (data) {
+            geo_json_484856c7328cdac24c399d8d28e58862
                 .addData(data);
         }
-            geo_json_b68770c5261c558221d729adc90b10a7_add({&quot;bbox&quot;: [-180.0, -89.9, 179.99999, 83.64513000000001], &quot;features&quot;: [{&quot;bbox&quot;: [-180.0, -18.287990000000004, 179.99999, -16.02088225674122], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[-180.0, -16.555216566639196], [-179.9173693847653, -16.501783135649397], [-179.79332010904864, -16.02088225674122], [-180.0, -16.06713266364245], [-180.0, -16.555216566639196]]], [[[178.12557, -17.50481], [177.67087, -17.381140000000002], [177.28504, -17.72465], [177.38146, -18.164320000000004], [177.93266000000003, -18.287990000000004], [178.55271, -18.15059], [178.71806, -17.628460000000004], [178.3736, -17.33992], [178.12557, -17.50481]]], [[[179.99999, -16.555220437594134], [179.99999, -16.067137982083942], [179.4135093629971, -16.379054277547407], [179.0966093629971, -16.433984277547403], [178.59683859511716, -16.639150000000004], [178.72505936299711, -17.01204167436804], [179.36414266196414, 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[20.717310000000115, 41.84711000000004], [20.590246546680227, 41.855408919283626], [20.59024743010491, 41.855404161133606], [20.463175083099205, 41.51508901627535], [20.605181919037364, 41.086226304685226], [21.020040317476404, 40.84272695572588], [21.674160597426976, 40.93127452245796], [22.055377638444273, 41.14986583105269], [22.597308383889015, 41.1304871689432], [22.761770000000016, 41.30480000000006], [22.952377150166452, 41.337993882811155], [22.881373732197346, 41.99929718685035], [22.380525750424596, 42.32025950781509]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;171&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;Macedonia&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [18.829824792873946, 42.24522439706187, 22.986018507588483, 46.17172984474454], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[18.829824792873946, 45.90887235802529], [19.072768995854172, 45.52151113543209], [19.390475701584588, 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[21.91708000000011, 42.30363999999997], [22.380525750424596, 42.32025950781509], [22.54501183440962, 42.46136200618804], [22.43659467946128, 42.580321153323936], [22.60480146657133, 42.898518785161144], [22.986018507588483, 43.2111612005271], [22.500156691180223, 43.642814439461006], [22.410446404721597, 44.008063462899955], [22.65714969248299, 44.23492300066128], [22.4740084164406, 44.40922760678177], [22.705725538837356, 44.578002834647016], [22.45902225107594, 44.7025171982543], [22.14508792490281, 44.47842234962059], [21.562022739353605, 44.76894725196551], [21.483526238702215, 45.181170152357886], [20.87431277841335, 45.416375433934235], [20.762174920339984, 45.73457306577148], [20.22019249846284, 46.127468980486555], [19.596044549241583, 46.17172984474454], [18.829838087649964, 45.90887767189192], [18.829824792873946, 45.90887235802529]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;172&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;Serbia&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [18.45001688302086, 41.877550679783496, 20.339800000000082, 43.523840000000014], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[20.070700000000045, 42.58863000000008], [20.25758000000008, 42.81275000000011], [20.339800000000082, 42.89852000000002], [19.958570000000066, 43.10604000000006], [19.629999999999995, 43.213779970270544], [19.483889999999974, 43.35229000000004], [19.218519999999955, 43.523840000000014], [19.03165000000007, 43.43252999999999], [18.706480000000056, 43.200109999999995], [18.55999999999995, 42.649999999999984], [18.45001688302086, 42.47999224531218], [18.88214000000005, 42.281510000000026], [19.162459999999953, 41.95502000000005], [19.371768163347248, 41.877550679783496], [19.304486118250793, 42.19574514420782], [19.738051385179627, 42.688247382165564], [19.801613396898688, 42.50009349219084], [20.070700000000045, 42.58863000000008]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;173&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;Montenegro&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [20.070700000000045, 41.84711000000004, 21.77505000000002, 43.27205000000009], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[20.590246546680227, 41.855408919283626], [20.717310000000115, 41.84711000000004], [20.76215999999999, 42.05186000000003], [21.352700000000144, 42.20679999999999], [21.57663598940212, 42.24522439706187], [21.543320000000055, 42.3202500000001], [21.662920000000042, 42.439220000000034], [21.77505000000002, 42.68270000000001], [21.633020000000045, 42.67717000000005], [21.438659999999974, 42.86254999999994], [21.27421000000004, 42.909590000000094], [21.143395000000055, 43.06868500000012], [20.95650999999998, 43.13094000000006], [20.81448, 43.27205000000009], [20.63507999999996, 43.216709999999985], [20.496790000000036, 42.884690000000035], [20.25758000000008, 42.81275000000011], [20.070700000000045, 42.58863000000008], [20.283754510181893, 42.32025950781508], [20.522950000000037, 42.21787000000006], [20.590246546680227, 41.855408919283626]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;174&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;Kosovo&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-61.95, 9.999999999999998, -60.895, 10.89], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[-61.68000000000001, 10.760000000000002], [-61.660000000000004, 10.365000000000002], [-61.95, 10.090000000000002], [-61.77000000000001, 9.999999999999998], [-60.935, 10.11], [-60.895, 10.854999999999999], [-61.105000000000004, 10.89], [-61.68000000000001, 10.760000000000002]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;175&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;Trinidad and Tobago&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [23.88697958086067, 3.509171604222462, 35.29800711823298, 12.248007757149992], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[30.83385242171542, 3.509171604222462], [31.24556000000007, 3.7819000000000074], [31.88144999999998, 3.5582699999999936], [32.686420000000055, 3.7923200000000743], [33.3900000000001, 3.7899999999999636], [34.005, 4.249884947362048], [34.62019626785388, 4.8471227420819885], [35.29800711823298, 5.506], [34.70702, 6.594220000000122], [34.250320000000045, 6.8260700000000725], [34.07510000000001, 7.225950000000068], [33.568290000000104, 7.713340000000016], [32.95418000000006, 7.7849700000001], [33.29480000000012, 8.354580000000054], [33.82550000000015, 8.37916000000007], [33.97498000000007, 8.68455999999992], [33.963392794971185, 9.464285229420625], [33.82496348090751, 9.484060845715362], [33.842130853028145, 9.981914637215995], [33.72195924818311, 10.325262079630194], [33.206938084561784, 10.720111638406593], [33.086766479716744, 11.441141267476496], [33.206938084561784, 12.179338268667095], [32.74341903730255, 12.248007757149992], [32.67474954881965, 12.02483191958072], [32.073891524594785, 11.973329803218519], [32.31423473428475, 11.68148447716652], [32.40007159488834, 11.080626452941488], [31.85071568702551, 10.531270545078826], [31.35286189552488, 9.810240916008695], [30.837840731903377, 9.70723668328452], [29.996639497988554, 10.290927335388687], [29.61895731133285, 10.084918869940225], [29.515953078608614, 9.793073543888058], [29.000931914987174, 9.604232450560287], [28.966597170745786, 9.398223985111656], [27.970889587744352, 9.398223985111656], [27.83355061077879, 9.60423245056029], [27.112520981708883, 9.638567194801624], [26.752006167173818, 9.466893473594498], [26.477328213242515, 9.55273033419809], [25.96230704962102, 10.136420986302426], [25.790633328413946, 10.411098940233728], [25.069603699343986, 10.27375996326799], [24.794925745412684, 9.810240916008695], [24.537415163602024, 8.91753756573172], [24.19406772118765, 8.728696472403897], [23.88697958086067, 8.619729712933065], [24.567369012152085, 8.229187933785468], [25.114932488716793, 7.825104071479176], [25.12413089366473, 7.500085150579436], [25.79664798351118, 6.97931590415807], [26.213418409945117, 6.546603298362072], [26.46590945812324, 5.946717434101871], [27.21340905122517, 5.550953477394557], [27.37422610851749, 5.233944403500061], [27.97997724784281, 4.408413397637375], [28.42899376802691, 4.287154649264495], [28.696677687298802, 4.455077215996937], [29.159078403446504, 4.389267279473231], [29.71599531425602, 4.600804755060153], [29.953500197069474, 4.173699042167684], [30.83385242171542, 3.509171604222462]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;176&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;South Sudan&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_b68770c5261c558221d729adc90b10a7.bindTooltip(
+    geo_json_484856c7328cdac24c399d8d28e58862.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -721,7 +721,7 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_b68770c5261c558221d729adc90b10a7.addTo(map_532284f7269d428b0553fa806f00519a);
+            geo_json_484856c7328cdac24c399d8d28e58862.addTo(map_392cb7214998cea393ff6625c6c66dac);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -765,7 +765,7 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_8f8241d3ae2815dc43d9955949f7bc3a {
+                #map_fcbf28315ad87593efee7857e3cb8fdc {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -795,14 +795,14 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_8f8241d3ae2815dc43d9955949f7bc3a&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_fcbf28315ad87593efee7857e3cb8fdc&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_8f8241d3ae2815dc43d9955949f7bc3a = L.map(
-                &quot;map_8f8241d3ae2815dc43d9955949f7bc3a&quot;,
+            var map_fcbf28315ad87593efee7857e3cb8fdc = L.map(
+                &quot;map_fcbf28315ad87593efee7857e3cb8fdc&quot;,
                 {
                     center: [26.79284, 30.783149999999985],
                     crs: L.CRS.EPSG3857,
@@ -811,79 +811,79 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_8f8241d3ae2815dc43d9955949f7bc3a);
+            L.control.scale().addTo(map_fcbf28315ad87593efee7857e3cb8fdc);
 
             
 
         
     
-            var tile_layer_16f1400753d3dc0a8c5119f7d652d5a5 = L.tileLayer(
+            var tile_layer_be04c4e2bec232e4906da760c3467517 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_16f1400753d3dc0a8c5119f7d652d5a5.addTo(map_8f8241d3ae2815dc43d9955949f7bc3a);
+            tile_layer_be04c4e2bec232e4906da760c3467517.addTo(map_fcbf28315ad87593efee7857e3cb8fdc);
         
     
-            map_8f8241d3ae2815dc43d9955949f7bc3a.fitBounds(
+            map_fcbf28315ad87593efee7857e3cb8fdc.fitBounds(
                 [[22.0, 24.70007], [31.585680000000004, 36.86622999999997]],
                 {}
             );
         
     
-        function geo_json_626b49a77c31f09fd1139bc298937d3c_styler(feature) {
+        function geo_json_f51525b6c17b07186d4eda6073062b0a_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_626b49a77c31f09fd1139bc298937d3c_highlighter(feature) {
+        function geo_json_f51525b6c17b07186d4eda6073062b0a_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_626b49a77c31f09fd1139bc298937d3c_pointToLayer(feature, latlng) {
+        function geo_json_f51525b6c17b07186d4eda6073062b0a_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_626b49a77c31f09fd1139bc298937d3c_styler(feature)
+            let style = geo_json_f51525b6c17b07186d4eda6073062b0a_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_626b49a77c31f09fd1139bc298937d3c_onEachFeature(feature, layer) {
+        function geo_json_f51525b6c17b07186d4eda6073062b0a_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_626b49a77c31f09fd1139bc298937d3c.resetStyle(e.target);
+                            geo_json_f51525b6c17b07186d4eda6073062b0a.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_626b49a77c31f09fd1139bc298937d3c_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_f51525b6c17b07186d4eda6073062b0a_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_626b49a77c31f09fd1139bc298937d3c = L.geoJson(null, {
-                onEachFeature: geo_json_626b49a77c31f09fd1139bc298937d3c_onEachFeature,
+        var geo_json_f51525b6c17b07186d4eda6073062b0a = L.geoJson(null, {
+                onEachFeature: geo_json_f51525b6c17b07186d4eda6073062b0a_onEachFeature,
             
-                style: geo_json_626b49a77c31f09fd1139bc298937d3c_styler,
-                pointToLayer: geo_json_626b49a77c31f09fd1139bc298937d3c_pointToLayer,
+                style: geo_json_f51525b6c17b07186d4eda6073062b0a_styler,
+                pointToLayer: geo_json_f51525b6c17b07186d4eda6073062b0a_pointToLayer,
         });
 
-        function geo_json_626b49a77c31f09fd1139bc298937d3c_add (data) {
-            geo_json_626b49a77c31f09fd1139bc298937d3c
+        function geo_json_f51525b6c17b07186d4eda6073062b0a_add (data) {
+            geo_json_f51525b6c17b07186d4eda6073062b0a
                 .addData(data);
         }
-            geo_json_626b49a77c31f09fd1139bc298937d3c_add({&quot;bbox&quot;: [24.70007, 22.0, 36.86622999999997, 31.585680000000004], &quot;features&quot;: [{&quot;bbox&quot;: [24.70007, 22.0, 36.86622999999997, 31.585680000000004], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[36.86622999999997, 22.0], [36.69069000000001, 22.204849999999997], [35.52598, 23.102440000000005], [35.49372, 23.752370000000003], [35.69241, 23.926709999999996], [34.79507, 25.033749999999998], [34.47387000000001, 25.598560000000006], [34.10455000000001, 26.142269999999996], [33.34876, 27.69989], [32.73482, 28.70523], [32.32046, 29.760430000000003], [32.42323000000001, 29.851080000000003], [33.13676, 28.417650000000005], [33.58811, 27.97136], [33.92136000000001, 27.648700000000005], [34.15451000000001, 27.823300000000003], [34.42655, 28.343989999999998], [34.64174, 29.09942], [34.9226, 29.501330000000003], [34.823243288783814, 29.76108076171822], [34.265440000000005, 31.219359999999998], [34.26543474464621, 31.219357309520323], [33.7734, 30.967460000000003], [32.99392, 31.02407], [32.19247, 31.26034], [31.960410000000003, 30.933600000000002], [31.687960000000007, 31.429600000000008], [30.976930000000007, 31.555859999999992], [30.09503, 31.473400000000005], [29.683419999999998, 31.186860000000003], [28.913529999999998, 30.870050000000003], [28.450480000000006, 31.02577000000001], [27.457620000000002, 31.32126], [26.49533, 31.585680000000004], [25.16482, 31.569150000000008], [24.802869999999988, 31.089290000000005], [24.957620000000002, 30.6616], [24.70007, 30.04419000000001], [25.000000000000004, 29.23865452953346], [25.000000000000004, 25.682499996361], [25.0, 22.0], [29.019999999999982, 22.0], [32.899999999999984, 22.0], [36.86622999999997, 22.0]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;163&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;Egypt&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
+            geo_json_f51525b6c17b07186d4eda6073062b0a_add({&quot;bbox&quot;: [24.70007, 22.0, 36.86622999999997, 31.585680000000004], &quot;features&quot;: [{&quot;bbox&quot;: [24.70007, 22.0, 36.86622999999997, 31.585680000000004], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[36.86622999999997, 22.0], [36.69069000000001, 22.204849999999997], [35.52598, 23.102440000000005], [35.49372, 23.752370000000003], [35.69241, 23.926709999999996], [34.79507, 25.033749999999998], [34.47387000000001, 25.598560000000006], [34.10455000000001, 26.142269999999996], [33.34876, 27.69989], [32.73482, 28.70523], [32.32046, 29.760430000000003], [32.42323000000001, 29.851080000000003], [33.13676, 28.417650000000005], [33.58811, 27.97136], [33.92136000000001, 27.648700000000005], [34.15451000000001, 27.823300000000003], [34.42655, 28.343989999999998], [34.64174, 29.09942], [34.9226, 29.501330000000003], [34.823243288783814, 29.76108076171822], [34.265440000000005, 31.219359999999998], [34.26543474464621, 31.219357309520323], [33.7734, 30.967460000000003], [32.99392, 31.02407], [32.19247, 31.26034], [31.960410000000003, 30.933600000000002], [31.687960000000007, 31.429600000000008], [30.976930000000007, 31.555859999999992], [30.09503, 31.473400000000005], [29.683419999999998, 31.186860000000003], [28.913529999999998, 30.870050000000003], [28.450480000000006, 31.02577000000001], [27.457620000000002, 31.32126], [26.49533, 31.585680000000004], [25.16482, 31.569150000000008], [24.802869999999988, 31.089290000000005], [24.957620000000002, 30.6616], [24.70007, 30.04419000000001], [25.000000000000004, 29.23865452953346], [25.000000000000004, 25.682499996361], [25.0, 22.0], [29.019999999999982, 22.0], [32.899999999999984, 22.0], [36.86622999999997, 22.0]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;163&quot;, &quot;properties&quot;: {&quot;name_long&quot;: &quot;Egypt&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_626b49a77c31f09fd1139bc298937d3c.bindTooltip(
+    geo_json_f51525b6c17b07186d4eda6073062b0a.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -907,7 +907,7 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_626b49a77c31f09fd1139bc298937d3c.addTo(map_8f8241d3ae2815dc43d9955949f7bc3a);
+            geo_json_f51525b6c17b07186d4eda6073062b0a.addTo(map_fcbf28315ad87593efee7857e3cb8fdc);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -922,7 +922,7 @@ <h3 data-number="1.2.2" class="anchored" data-anchor-id="sec-vector-layers"><spa
 <section id="sec-geometry-columns" class="level3" data-number="1.2.3">
 <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><span class="header-section-number">1.2.3</span> Geometry columns</h3>
 <p>The geometry column of class <code>GeoSeries</code> is an essential column in a <code>GeoDataFrame</code>. It contains the geometric part of the vector layer, and is the basis for all spatial operations. This column can be accessed by name, which typically (e.g., when reading from a file) is <code>'geometry'</code>, as in <code>gdf['geometry']</code>. However, the recommendation is to use the fixed <code>.geometry</code> property, which refers to the geometry column regardless whether its name is <code>'geometry'</code> or not. In the case of the <code>gdf</code> object, the geometry column contains <code>'MultiPolygon'</code>s associated with each country.</p>
-<div id="74626441" class="cell" data-execution_count="19">
+<div id="1de04260" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>gdf.geometry</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="17">
 <pre><code>0      MULTIPOLYGON (((-180 -16.55522,...
@@ -936,7 +936,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 </div>
 </div>
 <p>The geometry column also contains the spatial reference information, if any (also accessible with the shortcut <code>gdf.crs</code>).</p>
-<div id="883c3a00" class="cell" data-execution_count="20">
+<div id="24f9f3e0" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>gdf.geometry.crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="18">
 <pre><code>&lt;Geographic 2D CRS: EPSG:4326&gt;
@@ -953,7 +953,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 </div>
 </div>
 <p>Many geometry operations, such as calculating the centroid, buffer, or bounding box of each feature, involve just the geometry. Applying this type of operation on a <code>GeoDataFrame</code> is therefore basically a shortcut to applying it on the <code>GeoSeries</code> object in the geometry column. For example, the two following commands return exactly the same result, a <code>GeoSeries</code> containing bounding box polygons (using the <code>.envelope</code> method).</p>
-<div id="061fb8bb" class="cell" data-execution_count="21">
+<div id="8cc2ad98" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>gdf.envelope</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="19">
 <pre><code>0      POLYGON ((-180 -18.28799, 179.9...
@@ -966,7 +966,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 Length: 177, dtype: geometry</code></pre>
 </div>
 </div>
-<div id="5d7e0e38" class="cell" data-execution_count="22">
+<div id="1bd43774" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>gdf.geometry.envelope</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="20">
 <pre><code>0      POLYGON ((-180 -18.28799, 179.9...
@@ -980,7 +980,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 </div>
 </div>
 <p>Note that <code>.envelope</code>, and other similar operators in <strong>geopandas</strong> such as <code>.centroid</code> (<a href="04-geometry-operations.html#sec-centroids" class="quarto-xref"><span>Section 4.2.2</span></a>), <code>.buffer</code> (<a href="04-geometry-operations.html#sec-buffers" class="quarto-xref"><span>Section 4.2.3</span></a>), or <code>.convex_hull</code>, return only the geometry (i.e., a <code>GeoSeries</code>), not a <code>GeoDataFrame</code> with the original attribute data. In case we want the latter, we can create a copy of the <code>GeoDataFrame</code> and then ‘overwrite’ its geometry (or, we can overwrite the geometries directly in case we do not need the original ones, as in <code>gdf.geometry=gdf.envelope</code>).</p>
-<div id="ed4187ad" class="cell" data-execution_count="23">
+<div id="881e92a4" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>gdf2 <span class="op">=</span> gdf.copy()</span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>gdf2.geometry <span class="op">=</span> gdf.envelope</span>
 <span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>gdf2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1040,7 +1040,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 </div>
 </div>
 <p>Another useful property of the geometry column is the geometry type, as shown in the following code. Note that the types of geometries contained in a geometry column (and, thus, a vector layer) are not necessarily the same for every row. It is possible to have multiple geometry types in a single <code>GeoSeries</code>. Accordingly, the <code>.type</code> property returns a <code>Series</code> (with values of type <code>str</code>, i.e., strings), rather than a single value (the same can be done with the shortcut <code>gdf.geom_type</code>).</p>
-<div id="eaac3d3b" class="cell" data-execution_count="24">
+<div id="32f34656" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>gdf.geometry.<span class="bu">type</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
 <pre><code>0      MultiPolygon
@@ -1054,7 +1054,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 </div>
 </div>
 <p>To summarize the occurrence of different geometry types in a geometry column, we can use the <strong>pandas</strong> <code>.value_counts</code> method. In this case, we see that the <code>gdf</code> layer contains only <code>'MultiPolygon'</code> geometries.</p>
-<div id="d76335ea" class="cell" data-execution_count="25">
+<div id="7e03971f" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>gdf.geometry.<span class="bu">type</span>.value_counts()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
 <pre><code>MultiPolygon    177
@@ -1062,7 +1062,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 </div>
 </div>
 <p>A <code>GeoDataFrame</code> can also have multiple <code>GeoSeries</code> columns, as demonstrated in the following code section.</p>
-<div id="51aee9f0" class="cell" data-execution_count="26">
+<div id="81e95db0" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>gdf[<span class="st">'bbox'</span>] <span class="op">=</span> gdf.envelope</span>
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>gdf[<span class="st">'polygon'</span>] <span class="op">=</span> gdf.geometry</span>
 <span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>gdf</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1101,7 +1101,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_47a4389eb26f5e11f5898a7d8b097eaa {
+                #map_aff9c08931c683bf1e940105728ea7a8 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -1131,14 +1131,14 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_47a4389eb26f5e11f5898a7d8b097eaa&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_aff9c08931c683bf1e940105728ea7a8&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_47a4389eb26f5e11f5898a7d8b097eaa = L.map(
-                &quot;map_47a4389eb26f5e11f5898a7d8b097eaa&quot;,
+            var map_aff9c08931c683bf1e940105728ea7a8 = L.map(
+                &quot;map_aff9c08931c683bf1e940105728ea7a8&quot;,
                 {
                     center: [-3.1274349999999984, -5.000000001587068e-06],
                     crs: L.CRS.EPSG3857,
@@ -1147,79 +1147,79 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_47a4389eb26f5e11f5898a7d8b097eaa);
+            L.control.scale().addTo(map_aff9c08931c683bf1e940105728ea7a8);
 
             
 
         
     
-            var tile_layer_d8f7b2b8c20951abae184c4f5daa7e7b = L.tileLayer(
+            var tile_layer_94bbda8a1f5a9389e7a87f2487ecc0f8 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_d8f7b2b8c20951abae184c4f5daa7e7b.addTo(map_47a4389eb26f5e11f5898a7d8b097eaa);
+            tile_layer_94bbda8a1f5a9389e7a87f2487ecc0f8.addTo(map_aff9c08931c683bf1e940105728ea7a8);
         
     
-            map_47a4389eb26f5e11f5898a7d8b097eaa.fitBounds(
+            map_aff9c08931c683bf1e940105728ea7a8.fitBounds(
                 [[-89.9, -180.0], [83.64513000000001, 179.99999]],
                 {}
             );
         
     
-        function geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_styler(feature) {
+        function geo_json_d6f83eecfaa4527b46d8018dd12d8478_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_highlighter(feature) {
+        function geo_json_d6f83eecfaa4527b46d8018dd12d8478_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_pointToLayer(feature, latlng) {
+        function geo_json_d6f83eecfaa4527b46d8018dd12d8478_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_styler(feature)
+            let style = geo_json_d6f83eecfaa4527b46d8018dd12d8478_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_onEachFeature(feature, layer) {
+        function geo_json_d6f83eecfaa4527b46d8018dd12d8478_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_4f7cd35b0bd779ebcc39fcb73587fa37.resetStyle(e.target);
+                            geo_json_d6f83eecfaa4527b46d8018dd12d8478.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_d6f83eecfaa4527b46d8018dd12d8478_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_4f7cd35b0bd779ebcc39fcb73587fa37 = L.geoJson(null, {
-                onEachFeature: geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_onEachFeature,
+        var geo_json_d6f83eecfaa4527b46d8018dd12d8478 = L.geoJson(null, {
+                onEachFeature: geo_json_d6f83eecfaa4527b46d8018dd12d8478_onEachFeature,
             
-                style: geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_styler,
-                pointToLayer: geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_pointToLayer,
+                style: geo_json_d6f83eecfaa4527b46d8018dd12d8478_styler,
+                pointToLayer: geo_json_d6f83eecfaa4527b46d8018dd12d8478_pointToLayer,
         });
 
-        function geo_json_4f7cd35b0bd779ebcc39fcb73587fa37_add (data) {
-            geo_json_4f7cd35b0bd779ebcc39fcb73587fa37
+        function geo_json_d6f83eecfaa4527b46d8018dd12d8478_add (data) {
+            geo_json_d6f83eecfaa4527b46d8018dd12d8478
                 .addData(data);
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-    geo_json_4f7cd35b0bd779ebcc39fcb73587fa37.bindTooltip(
+    geo_json_d6f83eecfaa4527b46d8018dd12d8478.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -1243,7 +1243,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_4f7cd35b0bd779ebcc39fcb73587fa37.addTo(map_47a4389eb26f5e11f5898a7d8b097eaa);
+            geo_json_d6f83eecfaa4527b46d8018dd12d8478.addTo(map_aff9c08931c683bf1e940105728ea7a8);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -1287,7 +1287,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_95356e06a982bf3bb41e45ee722bc596 {
+                #map_b8ee33de63551b53283a308ee7fb6a07 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -1317,14 +1317,14 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_95356e06a982bf3bb41e45ee722bc596&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_b8ee33de63551b53283a308ee7fb6a07&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_95356e06a982bf3bb41e45ee722bc596 = L.map(
-                &quot;map_95356e06a982bf3bb41e45ee722bc596&quot;,
+            var map_b8ee33de63551b53283a308ee7fb6a07 = L.map(
+                &quot;map_b8ee33de63551b53283a308ee7fb6a07&quot;,
                 {
                     center: [-3.1274349999999984, -5.000000001587068e-06],
                     crs: L.CRS.EPSG3857,
@@ -1333,79 +1333,79 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_95356e06a982bf3bb41e45ee722bc596);
+            L.control.scale().addTo(map_b8ee33de63551b53283a308ee7fb6a07);
 
             
 
         
     
-            var tile_layer_28ff68f1f7401166afbb958d303564b8 = L.tileLayer(
+            var tile_layer_0b9ea426359d8d807ef183810d2cc512 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_28ff68f1f7401166afbb958d303564b8.addTo(map_95356e06a982bf3bb41e45ee722bc596);
+            tile_layer_0b9ea426359d8d807ef183810d2cc512.addTo(map_b8ee33de63551b53283a308ee7fb6a07);
         
     
-            map_95356e06a982bf3bb41e45ee722bc596.fitBounds(
+            map_b8ee33de63551b53283a308ee7fb6a07.fitBounds(
                 [[-89.9, -180.0], [83.64513000000001, 179.99999]],
                 {}
             );
         
     
-        function geo_json_41c38effafcdce93681d07c87efe0fc9_styler(feature) {
+        function geo_json_b6d788cac7ed29526554954caea646de_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_41c38effafcdce93681d07c87efe0fc9_highlighter(feature) {
+        function geo_json_b6d788cac7ed29526554954caea646de_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_41c38effafcdce93681d07c87efe0fc9_pointToLayer(feature, latlng) {
+        function geo_json_b6d788cac7ed29526554954caea646de_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_41c38effafcdce93681d07c87efe0fc9_styler(feature)
+            let style = geo_json_b6d788cac7ed29526554954caea646de_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_41c38effafcdce93681d07c87efe0fc9_onEachFeature(feature, layer) {
+        function geo_json_b6d788cac7ed29526554954caea646de_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_41c38effafcdce93681d07c87efe0fc9.resetStyle(e.target);
+                            geo_json_b6d788cac7ed29526554954caea646de.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_41c38effafcdce93681d07c87efe0fc9_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_b6d788cac7ed29526554954caea646de_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_41c38effafcdce93681d07c87efe0fc9 = L.geoJson(null, {
-                onEachFeature: geo_json_41c38effafcdce93681d07c87efe0fc9_onEachFeature,
+        var geo_json_b6d788cac7ed29526554954caea646de = L.geoJson(null, {
+                onEachFeature: geo_json_b6d788cac7ed29526554954caea646de_onEachFeature,
             
-                style: geo_json_41c38effafcdce93681d07c87efe0fc9_styler,
-                pointToLayer: geo_json_41c38effafcdce93681d07c87efe0fc9_pointToLayer,
+                style: geo_json_b6d788cac7ed29526554954caea646de_styler,
+                pointToLayer: geo_json_b6d788cac7ed29526554954caea646de_pointToLayer,
         });
 
-        function geo_json_41c38effafcdce93681d07c87efe0fc9_add (data) {
-            geo_json_41c38effafcdce93681d07c87efe0fc9
+        function geo_json_b6d788cac7ed29526554954caea646de_add (data) {
+            geo_json_b6d788cac7ed29526554954caea646de
                 .addData(data);
         }
-            geo_json_41c38effafcdce93681d07c87efe0fc9_add({&quot;bbox&quot;: [-180.0, -89.9, 179.99999, 83.64513000000001], &quot;features&quot;: [{&quot;bbox&quot;: [-180.0, -18.287990000000004, 179.99999, -16.02088225674122], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[-180.0, -16.555216566639196], [-179.9173693847653, -16.501783135649397], [-179.79332010904864, -16.02088225674122], [-180.0, -16.06713266364245], [-180.0, -16.555216566639196]]], [[[178.12557, -17.50481], [177.67087, -17.381140000000002], [177.28504, -17.72465], [177.38146, -18.164320000000004], [177.93266000000003, -18.287990000000004], [178.55271, -18.15059], [178.71806, -17.628460000000004], [178.3736, -17.33992], [178.12557, -17.50481]]], [[[179.99999, -16.555220437594134], [179.99999, -16.067137982083942], [179.4135093629971, -16.379054277547407], [179.0966093629971, -16.433984277547403], [178.59683859511716, -16.639150000000004], [178.72505936299711, -17.01204167436804], [179.36414266196414, 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-    geo_json_41c38effafcdce93681d07c87efe0fc9.bindTooltip(
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     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -1429,7 +1429,7 @@ <h3 data-number="1.2.3" class="anchored" data-anchor-id="sec-geometry-columns"><
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_41c38effafcdce93681d07c87efe0fc9.addTo(map_95356e06a982bf3bb41e45ee722bc596);
+            geo_json_b6d788cac7ed29526554954caea646de.addTo(map_b8ee33de63551b53283a308ee7fb6a07);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -1462,7 +1462,7 @@ <h3 data-number="1.2.4" class="anchored" data-anchor-id="sec-simple-features"><s
 <section id="sec-geometries" class="level3" data-number="1.2.5">
 <h3 data-number="1.2.5" class="anchored" data-anchor-id="sec-geometries"><span class="header-section-number">1.2.5</span> Geometries</h3>
 <p>Each element in the geometry column (<code>GeoSeries</code>) is a geometry object of class <code>shapely</code> <span class="citation" data-cites="shapely">(<a href="references.html#ref-shapely" role="doc-biblioref">Gillies et al. 2007--</a>)</span>. For example, here is one specific geometry selected by implicit index (Canada, the 4<sup>th</sup> element in <code>gdf</code>’s geometry column).</p>
-<div id="6bd0ec75" class="cell" data-execution_count="33">
+<div id="31628d02" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>gdf.geometry.iloc[<span class="dv">3</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="29">
 <div>
@@ -1473,7 +1473,7 @@ <h3 data-number="1.2.5" class="anchored" data-anchor-id="sec-geometries"><span c
 </div>
 </div>
 <p>We can also select a specific geometry based on the <code>'name_long'</code> attribute (i.e., the 1<sup>st</sup> and only element in the subset of <code>gdf</code> where the country name is equal to <code>Egypt</code>):</p>
-<div id="c7d84791" class="cell" data-execution_count="34">
+<div id="e7f9e820" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>gdf[gdf[<span class="st">'name_long'</span>] <span class="op">==</span> <span class="st">'Egypt'</span>].geometry.iloc[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="30">
 <div>
@@ -1502,7 +1502,7 @@ <h3 data-number="1.2.5" class="anchored" data-anchor-id="sec-geometries"><span c
 </div>
 </div>
 <p>Alternatively, we can use <code>shapely.from_wkt</code> to transform a WKT string to a <code>shapely</code> geometry object. Here is an example of creating the same <code>'Point'</code> geometry from WKT (<a href="#fig-point" class="quarto-xref">Figure&nbsp;<span>1.7</span></a>).</p>
-<div id="a1e79112" class="cell" data-execution_count="36">
+<div id="4fadb671" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>point <span class="op">=</span> shapely.from_wkt(<span class="st">'POINT (5 2)'</span>)</span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>point</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -1601,7 +1601,7 @@ <h3 data-number="1.2.5" class="anchored" data-anchor-id="sec-geometries"><span c
 </div>
 </div>
 <p>Since the required input has four hierarchical levels, it may be more clear to create the single-part <code>'Polygon'</code> geometries in advance, using the respective function (<code>shapely.Polygon</code>), and then pass them to <code>shapely.MultiPolygon</code> (<a href="#fig-multipolygon1" class="quarto-xref">Figure&nbsp;<span>1.12</span></a>). (The same technique can be used with the other <code>shapely.Multi*</code> functions.)</p>
-<div id="eb2d3ae5" class="cell" data-execution_count="42">
+<div id="3cd13f1c" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>multipolygon <span class="op">=</span> shapely.MultiPolygon([</span>
 <span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a>    shapely.Polygon([(<span class="dv">1</span>,<span class="dv">5</span>), (<span class="dv">2</span>,<span class="dv">2</span>), (<span class="dv">4</span>,<span class="dv">1</span>), (<span class="dv">4</span>,<span class="dv">4</span>), (<span class="dv">1</span>,<span class="dv">5</span>)]),  <span class="co">## Polygon 1 </span></span>
 <span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a>    shapely.Polygon([(<span class="dv">0</span>,<span class="dv">2</span>), (<span class="dv">1</span>,<span class="dv">2</span>), (<span class="dv">1</span>,<span class="dv">3</span>), (<span class="dv">0</span>,<span class="dv">3</span>), (<span class="dv">0</span>,<span class="dv">2</span>)])   <span class="co">## Polygon 2, etc.</span></span>
@@ -1642,14 +1642,14 @@ <h3 data-number="1.2.5" class="anchored" data-anchor-id="sec-geometries"><span c
 </div>
 </div>
 <p>As demonstrated in the last few figures, a <code>shapely</code> geometry object is automatically evaluated to a small image of the geometry (when using an interface capable of displaying it, such as Jupyter Notebook). To print the WKT string instead, we can use the <code>print</code> function:</p>
-<div id="3c8a720f" class="cell" data-execution_count="45">
+<div id="43f81af4" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(linestring)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>LINESTRING (1 5, 4 4, 4 1, 2 2, 3 2)</code></pre>
 </div>
 </div>
 <p>Finally, it is important to note that raw coordinates of <code>shapely</code> geometries are accessible through a combination of the <code>.coords</code>, <code>.geoms</code>, <code>.exterior</code>, and <code>.interiors</code> properties (depending on the geometry type). These access methods are helpful when we need to develop our own spatial operators for specific tasks. For example, the following expression returns the <code>list</code> of all coordinates of the <code>polygon</code> geometry exterior:</p>
-<div id="fef629ca" class="cell" data-execution_count="46">
+<div id="561da7bb" class="cell" data-execution_count="46">
 <div class="sourceCode cell-code" id="cb48"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>(polygon.exterior.coords)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="42">
 <pre><code>[(1.0, 5.0), (2.0, 2.0), (4.0, 1.0), (4.0, 4.0), (1.0, 5.0)]</code></pre>
@@ -1700,7 +1700,7 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
 </div>
 </div>
 <p>Next, we create a <code>GeoSeries</code> (of length 1), containing the point and a CRS definition, in this case WGS84 (defined using its EPSG code <code>4326</code>). Also note that the <code>shapely</code> geometries go into a <code>list</code>, to illustrate that there can be more than one geometry unlike in this example.</p>
-<div id="c96aba63" class="cell" data-execution_count="48">
+<div id="f5b9e07d" class="cell" data-execution_count="48">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>lnd_geom <span class="op">=</span> gpd.GeoSeries([lnd_point], crs<span class="op">=</span><span class="dv">4326</span>)</span>
 <span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a>lnd_geom</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="44">
@@ -1709,7 +1709,7 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
 </div>
 </div>
 <p>Next, we combine the <code>GeoSeries</code> with other attributes into a <code>dict</code>. The geometry column is a <code>GeoSeries</code>, named <code>geometry</code>. The other attributes (if any) may be defined using <code>list</code> or <code>Series</code> objects. Here, for simplicity, we use the <code>list</code> option for defining the three attributes <code>name</code>, <code>temperature</code>, and <code>date</code>. Again, note that the <code>list</code> can be of length &gt;1, in case we are creating a layer with more than one feature (i.e., multiple rows).</p>
-<div id="e8d9207e" class="cell" data-execution_count="49">
+<div id="cce01409" class="cell" data-execution_count="49">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>lnd_data <span class="op">=</span> {</span>
 <span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a>  <span class="st">'name'</span>: [<span class="st">'London'</span>],</span>
 <span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a>  <span class="st">'temperature'</span>: [<span class="dv">25</span>],</span>
@@ -1718,7 +1718,7 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
 <span id="cb53-6"><a href="#cb53-6" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Finally, the <code>dict</code> can be converted to a <code>GeoDataFrame</code> object, as shown in the following code.</p>
-<div id="afbf387e" class="cell" data-execution_count="50">
+<div id="013c69f9" class="cell" data-execution_count="50">
 <div class="sourceCode cell-code" id="cb54"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>lnd_layer <span class="op">=</span> gpd.GeoDataFrame(lnd_data)</span>
 <span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a>lnd_layer</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="46">
@@ -1751,7 +1751,7 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
 </div>
 <p>What just happened? First, the coordinates were used to create the simple feature geometry (<code>shapely</code>). Second, the geometry was converted into a simple feature geometry column (<code>GeoSeries</code>), with a CRS. Third, attributes were combined with <code>GeoSeries</code>. This results in an <code>GeoDataFrame</code> object, named <code>lnd_layer</code>.</p>
 <p>To illustrate how does creating a layer with more than one feature looks like, here is an example where we create a layer with two points, London and Paris.</p>
-<div id="3c85ccc8" class="cell" data-execution_count="51">
+<div id="81d47e0b" class="cell" data-execution_count="51">
 <div class="sourceCode cell-code" id="cb55"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>lnd_point <span class="op">=</span> shapely.Point(<span class="fl">0.1</span>, <span class="fl">51.5</span>)</span>
 <span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a>paris_point <span class="op">=</span> shapely.Point(<span class="fl">2.3</span>, <span class="fl">48.9</span>)</span>
 <span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a>towns_geom <span class="op">=</span> gpd.GeoSeries([lnd_point, paris_point], crs<span class="op">=</span><span class="dv">4326</span>)</span>
@@ -1831,7 +1831,7 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_ce9725391d3c68712407317a36d4aad3 {
+                #map_75d70a247a2ddab7534164b5c5ad8728 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -1861,14 +1861,14 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_ce9725391d3c68712407317a36d4aad3&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_75d70a247a2ddab7534164b5c5ad8728&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_ce9725391d3c68712407317a36d4aad3 = L.map(
-                &quot;map_ce9725391d3c68712407317a36d4aad3&quot;,
+            var map_75d70a247a2ddab7534164b5c5ad8728 = L.map(
+                &quot;map_75d70a247a2ddab7534164b5c5ad8728&quot;,
                 {
                     center: [50.2, 1.2],
                     crs: L.CRS.EPSG3857,
@@ -1877,79 +1877,79 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_ce9725391d3c68712407317a36d4aad3);
+            L.control.scale().addTo(map_75d70a247a2ddab7534164b5c5ad8728);
 
             
 
         
     
-            var tile_layer_92b328d57541d9b7f5af770d5044a4d2 = L.tileLayer(
+            var tile_layer_951e0224415e8f3c9e2c45cd08b5cc55 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_92b328d57541d9b7f5af770d5044a4d2.addTo(map_ce9725391d3c68712407317a36d4aad3);
+            tile_layer_951e0224415e8f3c9e2c45cd08b5cc55.addTo(map_75d70a247a2ddab7534164b5c5ad8728);
         
     
-            map_ce9725391d3c68712407317a36d4aad3.fitBounds(
+            map_75d70a247a2ddab7534164b5c5ad8728.fitBounds(
                 [[48.9, 0.1], [51.5, 2.3]],
                 {}
             );
         
     
-        function geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_styler(feature) {
+        function geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;color&quot;: &quot;red&quot;, &quot;fillColor&quot;: &quot;red&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_highlighter(feature) {
+        function geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_pointToLayer(feature, latlng) {
+        function geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 10, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_styler(feature)
+            let style = geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_onEachFeature(feature, layer) {
+        function geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_1ae7dbf2c22b818676a3386aa2ee4c67.resetStyle(e.target);
+                            geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_1ae7dbf2c22b818676a3386aa2ee4c67 = L.geoJson(null, {
-                onEachFeature: geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_onEachFeature,
+        var geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44 = L.geoJson(null, {
+                onEachFeature: geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_onEachFeature,
             
-                style: geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_styler,
-                pointToLayer: geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_pointToLayer,
+                style: geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_styler,
+                pointToLayer: geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_pointToLayer,
         });
 
-        function geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_add (data) {
-            geo_json_1ae7dbf2c22b818676a3386aa2ee4c67
+        function geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_add (data) {
+            geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44
                 .addData(data);
         }
-            geo_json_1ae7dbf2c22b818676a3386aa2ee4c67_add({&quot;bbox&quot;: [0.1, 48.9, 2.3, 51.5], &quot;features&quot;: [{&quot;bbox&quot;: [0.1, 51.5, 0.1, 51.5], &quot;geometry&quot;: {&quot;coordinates&quot;: [0.1, 51.5], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;date&quot;: &quot;2013-06-21&quot;, &quot;name&quot;: &quot;London&quot;, &quot;temperature&quot;: 25}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [2.3, 48.9, 2.3, 48.9], &quot;geometry&quot;: {&quot;coordinates&quot;: [2.3, 48.9], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;1&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;date&quot;: &quot;2013-06-21&quot;, &quot;name&quot;: &quot;Paris&quot;, &quot;temperature&quot;: 27}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
+            geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44_add({&quot;bbox&quot;: [0.1, 48.9, 2.3, 51.5], &quot;features&quot;: [{&quot;bbox&quot;: [0.1, 51.5, 0.1, 51.5], &quot;geometry&quot;: {&quot;coordinates&quot;: [0.1, 51.5], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;date&quot;: &quot;2013-06-21&quot;, &quot;name&quot;: &quot;London&quot;, &quot;temperature&quot;: 25}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [2.3, 48.9, 2.3, 48.9], &quot;geometry&quot;: {&quot;coordinates&quot;: [2.3, 48.9], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;1&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;date&quot;: &quot;2013-06-21&quot;, &quot;name&quot;: &quot;Paris&quot;, &quot;temperature&quot;: 27}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_1ae7dbf2c22b818676a3386aa2ee4c67.bindTooltip(
+    geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -1973,7 +1973,7 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_1ae7dbf2c22b818676a3386aa2ee4c67.addTo(map_ce9725391d3c68712407317a36d4aad3);
+            geo_json_14f27b6c35aaaa171dc1cdabfa5c7a44.addTo(map_75d70a247a2ddab7534164b5c5ad8728);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -1985,7 +1985,7 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
 </div>
 </div>
 <p>A spatial (point) layer can be also created from a <code>DataFrame</code> object (package <strong>pandas</strong>) that contains columns with coordinates. To demonstrate, we hereby first create a <code>GeoSeries</code> object from the coordinates, and then combine it with the <code>DataFrame</code> to form a <code>GeoDataFrame</code>.</p>
-<div id="77ef717b" class="cell" data-execution_count="55">
+<div id="1ab4561b" class="cell" data-execution_count="55">
 <div class="sourceCode cell-code" id="cb57"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>towns_table <span class="op">=</span> pd.DataFrame({</span>
 <span id="cb57-2"><a href="#cb57-2" aria-hidden="true" tabindex="-1"></a>  <span class="st">'name'</span>: [<span class="st">'London'</span>, <span class="st">'Paris'</span>],</span>
 <span id="cb57-3"><a href="#cb57-3" aria-hidden="true" tabindex="-1"></a>  <span class="st">'temperature'</span>: [<span class="dv">25</span>, <span class="dv">27</span>],</span>
@@ -2001,19 +2001,19 @@ <h3 data-number="1.2.6" class="anchored" data-anchor-id="sec-vector-layer-from-s
 <section id="sec-area-length" class="level3" data-number="1.2.7">
 <h3 data-number="1.2.7" class="anchored" data-anchor-id="sec-area-length"><span class="header-section-number">1.2.7</span> Derived numeric properties</h3>
 <p>Vector layers are characterized by two essential derived numeric properties: <em>length</em> (<code>.length</code>)—applicable to lines, and <em>area</em> (<code>.area</code>)—applicable to polygons. Area and length can be calculated for any data structures discussed above, either a <code>shapely</code> geometry, in which case the returned value is a number, or for <code>GeoSeries</code> or <code>DataFrame</code>, in which case the returned value is a numeric <code>Series</code>.</p>
-<div id="1e0c56dd" class="cell" data-execution_count="56">
+<div id="19f78c57" class="cell" data-execution_count="56">
 <div class="sourceCode cell-code" id="cb58"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a>linestring.length</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="51">
 <pre><code>9.39834563766817</code></pre>
 </div>
 </div>
-<div id="7a55ee9e" class="cell" data-execution_count="57">
+<div id="c1764b08" class="cell" data-execution_count="57">
 <div class="sourceCode cell-code" id="cb60"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb60-1"><a href="#cb60-1" aria-hidden="true" tabindex="-1"></a>multipolygon.area</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="52">
 <pre><code>8.0</code></pre>
 </div>
 </div>
-<div id="9e6a9934" class="cell" data-execution_count="58">
+<div id="8d299d14" class="cell" data-execution_count="58">
 <div class="sourceCode cell-code" id="cb62"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb62-1"><a href="#cb62-1" aria-hidden="true" tabindex="-1"></a>gpd.GeoSeries([point, linestring, polygon, multipolygon]).area</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="53">
 <pre><code>0    0.0
@@ -2025,7 +2025,7 @@ <h3 data-number="1.2.7" class="anchored" data-anchor-id="sec-area-length"><span
 </div>
 <p>Like all numeric calculations in <strong>geopandas</strong>, the results assume a planar CRS and are returned in its native units. This means that length and area measurements for geometries in WGS84 (<code>crs=4326</code>) are returned in decimal degrees and essentially meaningless (to see the warning, try running <code>gdf.area</code>).</p>
 <p>To obtain meaningful length and area measurements for data in a geographic CRS, the geometries first need to be transformed to a projected CRS (see <a href="06-reproj.html#sec-reprojecting-vector-geometries" class="quarto-xref"><span>Section 6.7</span></a>) applicable to the area of interest. For example, the area of Slovenia can be calculated in the UTM zone 33N CRS (<code>crs=32633</code>). The result is in <span class="math inline">\(m^2\)</span>, the units of the UTM zone 33N CRS.</p>
-<div id="d4bc01c8" class="cell" data-execution_count="59">
+<div id="7008e1f5" class="cell" data-execution_count="59">
 <div class="sourceCode cell-code" id="cb64"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a>gdf[gdf[<span class="st">'name_long'</span>] <span class="op">==</span> <span class="st">'Slovenia'</span>].to_crs(<span class="dv">32633</span>).area</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="54">
 <pre><code>150    1.910410e+10
@@ -2066,7 +2066,7 @@ <h2 data-number="1.3" class="anchored" data-anchor-id="sec-raster-data"><span cl
 <section id="sec-using-rasterio" class="level3" data-number="1.3.1">
 <h3 data-number="1.3.1" class="anchored" data-anchor-id="sec-using-rasterio"><span class="header-section-number">1.3.1</span> Using <strong>rasterio</strong></h3>
 <p>To work with the <strong>rasterio</strong> package, we first need to import it. Additionally, as the raster data is stored within <strong>numpy</strong> arrays, we import the <strong>numpy</strong> package and make all its functions accessible for effective data manipulation. Finally, we import the <strong>rasterio.plot</strong> sub-module for its <code>rasterio.plot.show</code> function that allows for quick visualization of rasters.</p>
-<div id="dcd9e1cb" class="cell" data-execution_count="60">
+<div id="1efa62ef" class="cell" data-execution_count="60">
 <div class="sourceCode cell-code" id="cb66"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb66-2"><a href="#cb66-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio</span>
 <span id="cb66-3"><a href="#cb66-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio.plot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2078,7 +2078,7 @@ <h3 data-number="1.3.1" class="anchored" data-anchor-id="sec-using-rasterio"><sp
 </ul>
 <p>This type of separation is analogous to basic Python functions for reading from files, such as <code>open</code> and <code>.readline</code> to read from a text file. The rationale is that we do not always want to read all information from the file into memory, which is particularly important as rasters size can be larger than RAM size. Accordingly, the second step (<code>.read</code>) is selective, meaning that the user can fine-tune the subset of values (bands, rows/columns, resolution, etc.) that are actually being read. For example, we may want to read just one raster band rather than reading all bands.</p>
 <p>In the first step, we pass a file path to the <code>rasterio.open</code> function to create a <code>DatasetReader</code> file connection, hereby named <code>src</code>. For this example, we use a single-band raster representing elevation in Zion National Park, stored in <code>srtm.tif</code>.</p>
-<div id="371b8e23" class="cell" data-execution_count="61">
+<div id="7a1456ae" class="cell" data-execution_count="61">
 <div class="sourceCode cell-code" id="cb67"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>src <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/srtm.tif'</span>)</span>
 <span id="cb67-2"><a href="#cb67-2" aria-hidden="true" tabindex="-1"></a>src</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="56">
@@ -2086,7 +2086,7 @@ <h3 data-number="1.3.1" class="anchored" data-anchor-id="sec-using-rasterio"><sp
 </div>
 </div>
 <p>To get a first impression of the raster values, we can plot the raster using the <code>rasterio.plot.show</code> function (<a href="#fig-rasterio-plot" class="quarto-xref">Figure&nbsp;<span>1.21</span></a>):</p>
-<div id="cell-fig-rasterio-plot" class="cell" data-execution_count="62">
+<div id="cell-fig-rasterio-plot" class="cell" data-fig-width="4" data-execution_count="62">
 <div class="sourceCode cell-code" id="cb69"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a>rasterio.plot.show(src)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div id="fig-rasterio-plot" class="quarto-float quarto-figure quarto-figure-center anchored">
@@ -2102,7 +2102,7 @@ <h3 data-number="1.3.1" class="anchored" data-anchor-id="sec-using-rasterio"><sp
 </div>
 </div>
 <p>The <code>DatasetReader</code> contains the raster metadata, that is, all of the information other than the raster values. Let’s examine it with the <code>.meta</code> property.</p>
-<div id="6a723103" class="cell" data-execution_count="63">
+<div id="04448878" class="cell" data-execution_count="63">
 <div class="sourceCode cell-code" id="cb70"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb70-1"><a href="#cb70-1" aria-hidden="true" tabindex="-1"></a>src.meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="58">
 <pre><code>{'driver': 'GTiff',
@@ -2147,7 +2147,7 @@ <h3 data-number="1.3.1" class="anchored" data-anchor-id="sec-using-rasterio"><sp
 <li><em>Two</em> dimensions, when reading one specific layer (e.g., <code>.read(1)</code>). In such case, the dimensions pattern is <code>(rows, columns)</code></li>
 </ul>
 <p>Let’s read the first (and only) layer from the <code>srtm.tif</code> raster, using the file connection object <code>src</code> and the <code>.read</code> method.</p>
-<div id="354655c5" class="cell" data-execution_count="64">
+<div id="96eee7ae" class="cell" data-execution_count="64">
 <div class="sourceCode cell-code" id="cb73"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb73-1"><a href="#cb73-1" aria-hidden="true" tabindex="-1"></a>src.read(<span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="59">
 <pre><code>array([[1728, 1718, 1715, ..., 2654, 2674, 2685],
@@ -2181,7 +2181,7 @@ <h3 data-number="1.3.2" class="anchored" data-anchor-id="sec-raster-from-scratch
 <li>A CRS definition, specifying the association of that coordinate system with the surface of the earth (optional)</li>
 </ul>
 <p>Therefore, to create a raster, we first need to have an array with the values, and then supplement it with the georeferencing information. Let’s create the arrays <code>elev</code> and <code>grain</code>. The <code>elev</code> array is a <span class="math inline">\(6 \times 6\)</span> array with sequential values from <code>1</code> to <code>36</code>. It can be created as follows using the <code>np.arange</code> function and <code>.reshape</code> method from <strong>numpy</strong>.</p>
-<div id="f052a728" class="cell" data-execution_count="65">
+<div id="3ae1f70f" class="cell" data-execution_count="65">
 <div class="sourceCode cell-code" id="cb75"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb75-1"><a href="#cb75-1" aria-hidden="true" tabindex="-1"></a>elev <span class="op">=</span> np.arange(<span class="dv">1</span>, <span class="dv">37</span>, dtype<span class="op">=</span>np.uint8).reshape(<span class="dv">6</span>, <span class="dv">6</span>)</span>
 <span id="cb75-2"><a href="#cb75-2" aria-hidden="true" tabindex="-1"></a>elev</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="60">
@@ -2194,7 +2194,7 @@ <h3 data-number="1.3.2" class="anchored" data-anchor-id="sec-raster-from-scratch
 </div>
 </div>
 <p>The <code>grain</code> array represents a categorical raster with values <code>0</code>, <code>1</code>, <code>2</code>, corresponding to categories ‘clay’, ‘silt’, ‘sand’, respectively. We will create it from a specific arrangement of pixel values, using <strong>numpy</strong>’s <code>np.array</code> and <code>.reshape</code>.</p>
-<div id="9572a74f" class="cell" data-execution_count="66">
+<div id="4013eabe" class="cell" data-execution_count="66">
 <div class="sourceCode cell-code" id="cb77"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb77-1"><a href="#cb77-1" aria-hidden="true" tabindex="-1"></a>v <span class="op">=</span> [</span>
 <span id="cb77-2"><a href="#cb77-2" aria-hidden="true" tabindex="-1"></a>  <span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>, </span>
 <span id="cb77-3"><a href="#cb77-3" aria-hidden="true" tabindex="-1"></a>  <span class="dv">0</span>, <span class="dv">2</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">2</span>, <span class="dv">1</span>, </span>
@@ -2221,7 +2221,7 @@ <h3 data-number="1.3.2" class="anchored" data-anchor-id="sec-raster-from-scratch
 <li>The raster resolution (<span class="math inline">\(delta_{x}\)</span>, <span class="math inline">\(delta_{y}\)</span>) is <code>0.5,-0.5</code></li>
 </ul>
 <p>We can add this information using <code>rasterio.transform.from_origin</code>, and specifying <code>west</code>, <code>north</code>, <code>xsize</code>, and <code>ysize</code> parameters. The resulting transformation matrix object is hereby named <code>new_transform</code>.</p>
-<div id="f286e5b5" class="cell" data-execution_count="67">
+<div id="9cfe4233" class="cell" data-execution_count="67">
 <div class="sourceCode cell-code" id="cb79"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb79-1"><a href="#cb79-1" aria-hidden="true" tabindex="-1"></a>new_transform <span class="op">=</span> rasterio.transform.from_origin(</span>
 <span id="cb79-2"><a href="#cb79-2" aria-hidden="true" tabindex="-1"></a>    west<span class="op">=-</span><span class="fl">1.5</span>, </span>
 <span id="cb79-3"><a href="#cb79-3" aria-hidden="true" tabindex="-1"></a>    north<span class="op">=</span><span class="fl">1.5</span>, </span>
@@ -2279,7 +2279,7 @@ <h3 data-number="1.3.2" class="anchored" data-anchor-id="sec-raster-from-scratch
 <li>Close the connection</li>
 </ol>
 <p>Don’t worry if the code below is unclear; the concepts related to writing raster data to file will be explained in <a href="07-read-write.html#sec-data-output-raster" class="quarto-xref"><span>Section 7.6.2</span></a>. For now, for completeness, and also to use these rasters in subsequent chapters without having to re-create them from scratch, we just provide the code for exporting the <code>elev</code> and <code>grain</code> rasters into the <code>output</code> directory. In the case of <code>elev</code>, we do it as follows with the <code>rasterio.open</code>, <code>.write</code>, and <code>.close</code> functions and methods of the <strong>rasterio</strong> package.</p>
-<div id="be83e66d" class="cell" data-execution_count="70">
+<div id="0d6c3cb6" class="cell" data-execution_count="70">
 <div class="sourceCode cell-code" id="cb83"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb83-1"><a href="#cb83-1" aria-hidden="true" tabindex="-1"></a>new_dataset <span class="op">=</span> rasterio.<span class="bu">open</span>(</span>
 <span id="cb83-2"><a href="#cb83-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">'output/elev.tif'</span>, <span class="st">'w'</span>, </span>
 <span id="cb83-3"><a href="#cb83-3" aria-hidden="true" tabindex="-1"></a>    driver<span class="op">=</span><span class="st">'GTiff'</span>,</span>
@@ -2295,7 +2295,7 @@ <h3 data-number="1.3.2" class="anchored" data-anchor-id="sec-raster-from-scratch
 </div>
 <p>Note that the CRS we (arbitrarily) set for the <code>elev</code> raster is WGS84, defined using <code>crs=4326</code> according to the EPSG code.</p>
 <p>Exporting the <code>grain</code> raster is done in the same way, with the only differences being the file name and the array we write into the connection.</p>
-<div id="a6b885b4" class="cell" data-execution_count="71">
+<div id="327828a7" class="cell" data-execution_count="71">
 <div class="sourceCode cell-code" id="cb84"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb84-1"><a href="#cb84-1" aria-hidden="true" tabindex="-1"></a>new_dataset <span class="op">=</span> rasterio.<span class="bu">open</span>(</span>
 <span id="cb84-2"><a href="#cb84-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">'output/grain.tif'</span>, <span class="st">'w'</span>, </span>
 <span id="cb84-3"><a href="#cb84-3" aria-hidden="true" tabindex="-1"></a>    driver<span class="op">=</span><span class="st">'GTiff'</span>,</span>
@@ -2341,7 +2341,7 @@ <h3 data-number="1.4.2" class="anchored" data-anchor-id="sec-projected-coordinat
 <section id="sec-crs-python" class="level3" data-number="1.4.3">
 <h3 data-number="1.4.3" class="anchored" data-anchor-id="sec-crs-python"><span class="header-section-number">1.4.3</span> CRS in Python</h3>
 <p>Like most open-source geospatial software, the <strong>geopandas</strong> and <strong>rasterio</strong> packages use the PROJ software for CRS definition and calculations. The <strong>pyproj</strong> package is a low-level interface to PROJ. Using its functions, such as <code>get_codes</code> and <code>from_epsg</code>, we can examine the list of projections supported by PROJ.</p>
-<div id="46203b73" class="cell" data-execution_count="72">
+<div id="c117e721" class="cell" data-execution_count="72">
 <div class="sourceCode cell-code" id="cb85"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb85-1"><a href="#cb85-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pyproj</span>
 <span id="cb85-2"><a href="#cb85-2" aria-hidden="true" tabindex="-1"></a>epsg_codes <span class="op">=</span> pyproj.get_codes(<span class="st">'EPSG'</span>, <span class="st">'CRS'</span>)  <span class="co">## Supported EPSG codes</span></span>
 <span id="cb85-3"><a href="#cb85-3" aria-hidden="true" tabindex="-1"></a>epsg_codes[:<span class="dv">5</span>]  <span class="co">## Print first five supported EPSG codes</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2349,7 +2349,7 @@ <h3 data-number="1.4.3" class="anchored" data-anchor-id="sec-crs-python"><span c
 <pre><code>['10150', '10151', '10156', '10157', '10158']</code></pre>
 </div>
 </div>
-<div id="6fa5c14a" class="cell" data-execution_count="73">
+<div id="448d47ad" class="cell" data-execution_count="73">
 <div class="sourceCode cell-code" id="cb87"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb87-1"><a href="#cb87-1" aria-hidden="true" tabindex="-1"></a>pyproj.CRS.from_epsg(<span class="dv">4326</span>)  <span class="co">## Printout of WGS84 CRS (EPSG:4326)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="66">
 <pre><code>&lt;Geographic 2D CRS: EPSG:4326&gt;
@@ -2372,7 +2372,7 @@ <h3 data-number="1.4.3" class="anchored" data-anchor-id="sec-crs-python"><span c
 <li>CRSs of <strong>geopandas</strong> (vector layer or geometry column) and <strong>rasterio</strong> (raster) objects can be queried with the <code>.crs</code> property</li>
 </ul>
 <p>Here is a demonstration of the last bullet point, where we import a vector layer and figure out its CRS (in this case, a projected CRS, namely UTM Zone 12) using the <code>.crs</code> property.</p>
-<div id="9273baeb" class="cell" data-execution_count="74">
+<div id="5c4a091b" class="cell" data-execution_count="74">
 <div class="sourceCode cell-code" id="cb89"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb89-1"><a href="#cb89-1" aria-hidden="true" tabindex="-1"></a>zion <span class="op">=</span> gpd.read_file(<span class="st">'data/zion.gpkg'</span>)</span>
 <span id="cb89-2"><a href="#cb89-2" aria-hidden="true" tabindex="-1"></a>zion.crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="67">
@@ -2439,7 +2439,7 @@ <h3 data-number="1.4.3" class="anchored" data-anchor-id="sec-crs-python"><span c
 <h2 data-number="1.5" class="anchored" data-anchor-id="units"><span class="header-section-number">1.5</span> Units</h2>
 <p>An important feature of CRSs is that they contain information about spatial units. Clearly, it is vital to know whether a house’s measurements are in feet or meters, and the same applies to maps. It is a good cartographic practice to add a scale bar or some other distance indicator onto maps to demonstrate the relationship between distances on the page or screen and distances on the ground. Likewise, it is important for the user to be aware of the units in which the geometry coordinates are, to ensure that subsequent calculations are done in the right context.</p>
 <p>Python spatial data structures in <strong>geopandas</strong> and <strong>rasterio</strong> do not natively support the concept of measurement units. The coordinates of a vector layer or a raster are plain numbers, referring to an arbitrary plane. For example, according to the <code>.transform</code> matrix of <code>srtm.tif</code> we can see that the raster resolution is <code>0.000833</code> and that its CRS is WGS84 (EPSG: <code>4326</code>):</p>
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 <div class="sourceCode cell-code" id="cb92"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a>src.meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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diff --git a/02-attribute-operations.html b/02-attribute-operations.html
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@@ -356,7 +356,7 @@ <h1 class="title"><span id="sec-attr" class="quarto-section-identifier"><span cl
 <section id="prerequisites" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2>
 <p>This chapter requires importing the following packages:</p>
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 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -364,7 +364,7 @@ <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2
 <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>It also relies on the following data files:</p>
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 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>world <span class="op">=</span> gpd.read_file(<span class="st">'data/world.gpkg'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>src_elev <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/elev.tif'</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>src_grain <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/grain.tif'</span>)</span>
@@ -393,7 +393,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
 </ul>
 <p>An exception to this guideline is selecting columns using a list, which we do using shorter notation, as in <code>df[['a','b']]</code>, instead of <code>df.loc[:, ['a','b']]</code>, to select columns <code>'a'</code> and <code>'b'</code> from <code>df</code>.</p>
 <p>Here are few examples of subsetting the <code>GeoDataFrame</code> of world countries (<a href="01-spatial-data.html#fig-gdf-plot" class="quarto-xref">Figure&nbsp;<span>1.2</span></a>). First, we are subsetting rows by position. In the first example, we are using <code>[0:3,:]</code>, meaning ‘rows 1,2,3, all columns’. Keep in mind that indices in Python start from 0, and slices are inclusive of the start and exclusive of the end; therefore, <code>0:3</code> means indices <code>0</code>, <code>1</code>, <code>2</code>, i.e., first three rows in this example. <!-- md: IMHO this was too much basic pandas material, as suggested by one reviewer. Also was contradicting the previous paragraph where we advocate explicit approaches. --></p>
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 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>world.iloc[<span class="dv">0</span>:<span class="dv">3</span>, :]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -443,7 +443,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
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 </div>
 <p>Subsetting columns by position requires specifying that we want to keep all of the rows (<code>:</code>) and then the indexes of the columns we want to keep.</p>
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 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>world.iloc[:, <span class="dv">0</span>:<span class="dv">3</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -509,7 +509,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
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 </div>
 <p>To subset rows and columns by position we need to specify both row and column indices, separated by a comma.</p>
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 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>world.iloc[<span class="dv">0</span>:<span class="dv">3</span>, <span class="dv">0</span>:<span class="dv">3</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -550,7 +550,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
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 <p>Subsetting columns by name is not done with the <code>.iloc</code> method, but instead requires specifying the column names in <code>.loc</code>, or directly in a double square bracket <code>[[</code> notation.</p>
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 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>world[[<span class="st">'name_long'</span>, <span class="st">'geometry'</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <p>To select many successive columns, we can use the <code>:</code> (slice) notation, as in <code>world.loc[:, 'name_long':'pop']</code>, which selects all columns from <code>name_long</code> to <code>pop</code> (inclusive).</p>
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 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>world.loc[:, <span class="st">'name_long'</span>:<span class="st">'pop'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 </div>
 </div>
 <p>Removing rows or columns is done using the <code>.drop</code> method. We can remove specific rows by specifying their ids, e.g., dropping rows 2, 3, and 5 in the following example.</p>
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 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>world.drop([<span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -772,7 +772,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
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 </div>
 <p>To remove specific columns we need to add an extra argument, <code>axis=1</code> (i.e., columns).</p>
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 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>world.drop([<span class="st">'name_long'</span>, <span class="st">'continent'</span>], axis<span class="op">=</span><span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -854,7 +854,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
 </div>
 </div>
 <p>We can also rename columns using the <code>.rename</code> method, in which we pass a dictionary with items of the form <code>old_name:new_name</code> to the <code>columns</code> argument.</p>
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 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>world[[<span class="st">'name_long'</span>, <span class="st">'pop'</span>]].rename(columns<span class="op">=</span>{<span class="st">'pop'</span>: <span class="st">'population'</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 </figure>
 </div>
 <p>The following example demonstrates logical vectors for subsetting by creating a new <code>GeoDataFrame</code> object called <code>small_countries</code> that contains only those countries and other territories from the <code>world</code> object whose surface area is smaller than 10,000 <span class="math inline">\(km^2\)</span>. The first step is to create a logical vector (a <code>Series</code> object) that is <code>True</code> for countries with an area smaller than 10,000 <span class="math inline">\(km^2\)</span> and <code>False</code> otherwise. Then, we use this vector to subset the <code>world</code> dataset, which returns a new <code>GeoDataFrame</code> object containing only the small countries.</p>
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 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>idx_small <span class="op">=</span> world[<span class="st">'area_km2'</span>] <span class="op">&lt;</span> <span class="dv">10000</span>  <span class="co">## a logical 'Series'</span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>small_countries <span class="op">=</span> world[idx_small]</span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>small_countries</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1036,7 +1036,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
 </div>
 </div>
 <p>A more concise command, which omits the intermediary object by combining the two steps into one, generates the same result.</p>
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 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>small_countries <span class="op">=</span> world[world[<span class="st">'area_km2'</span>] <span class="op">&lt;</span> <span class="dv">10000</span>]</span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>small_countries</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -1119,7 +1119,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
 </div>
 </div>
 <p>We can also combine indexes using logical operators, such as <code>&amp;</code> (and), <code>|</code> (or), and <code>~</code> (not).</p>
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 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>idx_small <span class="op">=</span> world[<span class="st">'area_km2'</span>] <span class="op">&lt;</span> <span class="dv">10000</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>idx_asia <span class="op">=</span> world[<span class="st">'continent'</span>] <span class="op">==</span> <span class="st">'Asia'</span></span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>world.loc[idx_small <span class="op">&amp;</span> idx_asia, [<span class="st">'name_long'</span>, <span class="st">'continent'</span>, <span class="st">'area_km2'</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1162,7 +1162,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
 </div>
 </div>
 <p>The various methods shown above can be chained for any combination with several subsetting steps. For example, the following code selects only countries from Asia, keeps only the <code>name_long</code> and <code>continent</code> columns, and then selects the first five rows.</p>
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 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>world[world[<span class="st">'continent'</span>] <span class="op">==</span> <span class="st">'Asia'</span>]  <span class="op">\</span></span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>    .loc[:, [<span class="st">'name_long'</span>, <span class="st">'continent'</span>]]  <span class="op">\</span></span>
 <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>    .iloc[<span class="dv">0</span>:<span class="dv">5</span>, :]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 </div>
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 <p>Logical operators <code>&amp;</code>, <code>|</code>, and <code>~</code> (<a href="#tbl-comparison-operators" class="quarto-xref">Table&nbsp;<span>2.1</span></a>) can be used to combine multiple conditions. For example, here are all countries in North America or South America. Keep in mind that the parentheses around each condition (here, and in analogous cases using other operators) are crucial; otherwise, due to Python’s precedence rules<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>, the <code>|</code> operator is executed before <code>==</code> and we get an error.</p>
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 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>world[</span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>        (world[<span class="st">'continent'</span>] <span class="op">==</span> <span class="st">'North America'</span>) <span class="op">|</span> </span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>        (world[<span class="st">'continent'</span>] <span class="op">==</span>  <span class="st">'South America'</span>)</span>
@@ -1273,7 +1273,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
 </div>
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 <p>However, specifically, expressions combining multiple comparisons with <code>==</code> combined with <code>|</code> can be replaced with the <code>.isin</code> method and a <code>list</code> of values to compare with. The advantage of <code>.isin</code> is more concise and easy to manage code, especially when the number of comparisons is large. For example, the following expression gives the same result as above.</p>
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 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>world[world[<span class="st">'continent'</span>].isin([<span class="st">'North America'</span>, <span class="st">'South America'</span>])]  <span class="op">\</span></span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>    .loc[:, [<span class="st">'name_long'</span>, <span class="st">'continent'</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -1336,7 +1336,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-vector-attribute-su
 <h3 data-number="2.2.2" class="anchored" data-anchor-id="sec-vector-attribute-aggregation"><span class="header-section-number">2.2.2</span> Vector attribute aggregation</h3>
 <p>Aggregation involves summarizing data based on one or more <em>grouping variables</em> (typically values in a column; geographic aggregation is covered in <a href="03-spatial-operations.html#sec-vector-spatial-aggregation" class="quarto-xref"><span>Section 3.2.5</span></a>). A classic example of this attribute-based aggregation is calculating the number of people per continent based on country-level data (one row per country). The <code>world</code> dataset contains the necessary ingredients: the columns <code>pop</code> and <code>continent</code>, the population and the grouping variable, respectively. The aim is to find the <code>sum()</code> of country populations for each continent, resulting in a smaller table or vector layer (of continents). Since aggregation is a form of data reduction, it can be a useful early step when working with large datasets.</p>
 <p>Attribute-based aggregation can be achieved using a combination of <code>.groupby</code> and <code>.sum</code> (package <strong>pandas</strong>), where the former groups the data by the grouping variable(s) and the latter calculates the sum of the specified column(s). The <code>.reset_index</code> methods moves the grouping variable into an ordinary column, rather than an index (the default), which is something we typically want to do.</p>
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 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>world_agg1 <span class="op">=</span> world.groupby(<span class="st">'continent'</span>)[[<span class="st">'pop'</span>]].<span class="bu">sum</span>().reset_index()</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>world_agg1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 </div>
 <p>The result, in this case, is a (non-spatial) table with eight rows, one per unique value in <code>continent</code>, and two columns reporting the name and population of each continent.</p>
 <p>If we want to include the geometry in the aggregation result, we can use the <code>.dissolve</code> method. That way, in addition to the summed population, we also get the associated geometry per continent, i.e., the union of all countries. Note that we use the <code>by</code> parameter to choose which column(s) are used for grouping, and the <code>aggfunc</code> parameter to choose the aggregation function for non-geometry columns. Again, note that the <code>.reset_index</code> method is used (here, and elsewhere in the book) to turn <strong>pandas</strong> and <strong>geopandas</strong> row <em>indices</em>, which are automatically created for grouping variables in grouping operations such as <code>.dissolve</code>, ‘back’ into ordinary columns, which are more appropriate in the scope of this book.</p>
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+<div id="0251da77" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>world_agg2 <span class="op">=</span> world[[<span class="st">'continent'</span>, <span class="st">'pop'</span>, <span class="st">'geometry'</span>]] <span class="op">\</span></span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>    .dissolve(by<span class="op">=</span><span class="st">'continent'</span>, aggfunc<span class="op">=</span><span class="st">'sum'</span>) <span class="op">\</span></span>
 <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    .reset_index()</span>
@@ -1483,7 +1483,7 @@ <h3 data-number="2.2.2" class="anchored" data-anchor-id="sec-vector-attribute-ag
 </div>
 <p>Other options for the <code>aggfunc</code> parameter in <code>.dissolve</code> <a href="https://geopandas.org/en/stable/docs/user_guide/aggregation_with_dissolve.html">include</a><code>'first'</code>, <code>'last'</code>, <code>'min'</code>, <code>'max'</code>, <code>'sum'</code>, <code>'mean'</code>, <code>'median'</code>. Additionally, we can pass custom functions here.</p>
 <p>As a more complex example, the following code shows how we can calculate the total population, area, and count of countries, per continent. It is done by passing a dictionary to the <code>aggfunc</code> parameter, where the keys are the column names and the values are the aggregation functions. The result is a <code>GeoDataFrame</code> object with 8 rows (one per continent) and 4 columns (one for the continent name and one for each of the three aggregated attributes). The <code>rename</code> method is used to rename the <code>'name_long'</code> column into <code>'n'</code>, as it now expresses the count of names (i.e., the number of countries) rather than their names.</p>
-<div id="7f0610a3" class="cell" data-execution_count="23">
+<div id="c108bd09" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>world_agg3 <span class="op">=</span> world.dissolve(</span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>    by<span class="op">=</span><span class="st">'continent'</span>, </span>
 <span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>    aggfunc<span class="op">=</span>{</span>
@@ -1635,7 +1635,7 @@ <h3 data-number="2.2.2" class="anchored" data-anchor-id="sec-vector-attribute-ag
 <li>arrange continents by the number of countries each contains, and</li>
 <li>keep only the 3 most populous continents.</li>
 </ul>
-<div id="81bde6d2" class="cell" data-execution_count="25">
+<div id="df0ce6b1" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>world_agg4 <span class="op">=</span> world_agg3.drop(columns<span class="op">=</span>[<span class="st">'geometry'</span>])</span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>world_agg4[<span class="st">'density'</span>] <span class="op">=</span> world_agg4[<span class="st">'pop'</span>] <span class="op">/</span> world_agg4[<span class="st">'area_km2'</span>]</span>
 <span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>world_agg4 <span class="op">=</span> world_agg4.sort_values(by<span class="op">=</span><span class="st">'n'</span>, ascending<span class="op">=</span><span class="va">False</span>)</span>
@@ -1692,7 +1692,7 @@ <h3 data-number="2.2.2" class="anchored" data-anchor-id="sec-vector-attribute-ag
 <h3 data-number="2.2.3" class="anchored" data-anchor-id="sec-vector-attribute-joining"><span class="header-section-number">2.2.3</span> Vector attribute joining</h3>
 <p>Combining data from different sources is a common task in data preparation. Joins do this by combining tables based on a shared ‘key’ variable. <strong>pandas</strong> has a function named <code>pd.merge</code> for joining <code>(Geo)DataFrames</code> based on common column(s) that follows conventions used in the database language SQL <span class="citation" data-cites="grolemund_r_2016">(<a href="references.html#ref-grolemund_r_2016" role="doc-biblioref">Grolemund and Wickham 2016</a>)</span>. The <code>pd.merge</code> result can be either a <code>DataFrame</code> or a <code>GeoDataFrame</code> object, depending on the inputs.</p>
 <p>A common type of attribute join on spatial data is to join <code>DataFrames</code> to <code>GeoDataFrames</code>. To achieve this, we use <code>pd.merge</code> with a <code>GeoDataFrame</code> as the first argument and add columns to it from a <code>DataFrame</code> specified as the second argument. In the following example, we combine data on coffee production with the <code>world</code> dataset. The coffee data is in a <code>DataFrame</code> called <code>coffee_data</code> imported from a CSV file of major coffee-producing nations.</p>
-<div id="bbd939f2" class="cell" data-execution_count="26">
+<div id="f4e7a279" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>coffee_data <span class="op">=</span> pd.read_csv(<span class="st">'data/coffee_data.csv'</span>)</span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>coffee_data</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="26">
@@ -1760,7 +1760,7 @@ <h3 data-number="2.2.3" class="anchored" data-anchor-id="sec-vector-attribute-jo
 </div>
 <p>Its columns are <code>name_long</code>—country name, and <code>coffee_production_2016</code> and <code>coffee_production_2017</code>—estimated values for coffee production in units of 60-kg bags per year, for 2016 and 2017, respectively.</p>
 <p>A left join, which preserves the first dataset, merges <code>world</code> with <code>coffee_data</code>, based on the common <code>'name_long'</code> column:</p>
-<div id="837a4127" class="cell" data-execution_count="27">
+<div id="ac5a238c" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>world_coffee <span class="op">=</span> pd.merge(world, coffee_data, on<span class="op">=</span><span class="st">'name_long'</span>, how<span class="op">=</span><span class="st">'left'</span>)</span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>world_coffee</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
@@ -1876,7 +1876,7 @@ <h3 data-number="2.2.3" class="anchored" data-anchor-id="sec-vector-attribute-jo
 <p>In case where column names are not the same, you can use <code>left_on</code> and <code>right_on</code> to specify the respective columns.</p>
 <p>Note that the result <code>world_coffee</code> has the same number of rows as the original dataset <code>world</code>. Although there are only 47 rows in <code>coffee_data</code>, all 177 country records are kept intact in <code>world_coffee</code>. Rows in the original dataset with no match are assigned <code>np.nan</code> values for the new coffee production variables. This is a characteristic of a left join (specified with <code>how='left'</code>) and is what we typically want to do.</p>
 <p>What if we only want to keep countries that have a match in the key variable? In that case an inner join can be used, which keeps only rows with a match in both datasets. We can use it with the <code>how='inner'</code> argument.</p>
-<div id="820ef9cd" class="cell" data-execution_count="29">
+<div id="3f83c15c" class="cell" data-execution_count="29">
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>pd.merge(world, coffee_data, on<span class="op">=</span><span class="st">'name_long'</span>, how<span class="op">=</span><span class="st">'inner'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="29">
 <div>
@@ -1961,7 +1961,7 @@ <h3 data-number="2.2.3" class="anchored" data-anchor-id="sec-vector-attribute-jo
 <section id="sec-creating-attributes-and-removing-spatial-information" class="level3" data-number="2.2.4">
 <h3 data-number="2.2.4" class="anchored" data-anchor-id="sec-creating-attributes-and-removing-spatial-information"><span class="header-section-number">2.2.4</span> Creating attributes and removing spatial information</h3>
 <p>Often, we would like to create a new column based on already existing columns. For example, we want to calculate population density for each country. For this we need to divide a population column, here <code>pop</code>, by an area column, here <code>area_km2</code>. Note that we are working on a copy of <code>world</code> named <code>world2</code> so that we do not modify the original layer.</p>
-<div id="8b4883cd" class="cell" data-execution_count="30">
+<div id="edac2998" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>world2 <span class="op">=</span> world.copy()</span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>world2[<span class="st">'pop_dens'</span>] <span class="op">=</span> world2[<span class="st">'pop'</span>] <span class="op">/</span> world2[<span class="st">'area_km2'</span>]</span>
 <span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>world2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2045,7 +2045,7 @@ <h3 data-number="2.2.4" class="anchored" data-anchor-id="sec-creating-attributes
 </div>
 </div>
 <p>To paste (i.e., concatenate) together existing columns, we can use the ordinary Python string operator <code>+</code>, as if we are working with individual strings rather than <code>Series</code>. For example, we want to combine the <code>continent</code> and <code>region_un</code> columns into a new column named <code>con_reg</code>, using <code>':'</code> as a separator. Subsequently, we remove the original columns using <code>.drop</code>:</p>
-<div id="8093b5b5" class="cell" data-execution_count="31">
+<div id="3633d058" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>world2[<span class="st">'con_reg'</span>] <span class="op">=</span> world[<span class="st">'continent'</span>] <span class="op">+</span> <span class="st">':'</span> <span class="op">+</span> world2[<span class="st">'region_un'</span>]</span>
 <span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>world2 <span class="op">=</span> world2.drop([<span class="st">'continent'</span>, <span class="st">'region_un'</span>], axis<span class="op">=</span><span class="dv">1</span>)</span>
 <span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>world2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2129,7 +2129,7 @@ <h3 data-number="2.2.4" class="anchored" data-anchor-id="sec-creating-attributes
 </div>
 </div>
 <p>The resulting <code>GeoDataFrame</code> object has a new column called <code>con_reg</code> representing the continent and region of each country, e.g., <code>'South America:Americas'</code> for Argentina and other South America countries. The opposite operation, splitting one column into multiple columns based on a separator string, is done using the <code>.str.split</code> method. As a result, we go back to the previous state of two separate <code>continent</code> and <code>region_un</code> columns (only that their position is now last, since they are newly created). The <code>str.split</code> method returns a column of <code>list</code>s by default; to place the strings into separate <code>str</code> columns we use the <code>expand=True</code> argument.</p>
-<div id="7a5aae83" class="cell" data-execution_count="32">
+<div id="8a2a9c3b" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>world2[[<span class="st">'continent'</span>, <span class="st">'region_un'</span>]] <span class="op">=</span> world2[<span class="st">'con_reg'</span>] <span class="op">\</span></span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>    .<span class="bu">str</span>.split(<span class="st">':'</span>, expand<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>world2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2213,7 +2213,7 @@ <h3 data-number="2.2.4" class="anchored" data-anchor-id="sec-creating-attributes
 </div>
 </div>
 <p>Renaming one or more columns can be done using the <code>.rename</code> method combined with the <code>columns</code> argument, which should be a dictionary of the form <code>old_name:new_name</code>, as shown above (<a href="#sec-vector-attribute-subsetting" class="quarto-xref"><span>Section 2.2.1</span></a>). The following command, for example, renames the lengthy <code>name_long</code> column to simply <code>name</code>.</p>
-<div id="ac7919e1" class="cell" data-execution_count="33">
+<div id="cfec9b0e" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>world2.rename(columns<span class="op">=</span>{<span class="st">'name_long'</span>: <span class="st">'name'</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="33">
 <div>
@@ -2295,7 +2295,7 @@ <h3 data-number="2.2.4" class="anchored" data-anchor-id="sec-creating-attributes
 </div>
 </div>
 <p>To change all column names at once, we assign a <code>list</code> of the ‘new’ column names into the <code>.columns</code> property. The <code>list</code> must be of the same length as the number of columns (i.e., <code>world.shape[1]</code>). This is illustrated below, which outputs the same <code>world2</code> object, but with very short names.</p>
-<div id="1416e926" class="cell" data-execution_count="34">
+<div id="8f85d95d" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>new_names <span class="op">=</span> [<span class="st">'a'</span>, <span class="st">'b'</span>, <span class="st">'c'</span>, <span class="st">'d'</span>, <span class="st">'e'</span>, <span class="st">'f'</span>, <span class="st">'g'</span>, <span class="st">'h'</span>, <span class="st">'geom'</span>, <span class="st">'i'</span>, <span class="st">'j'</span>, <span class="st">'k'</span>, <span class="st">'l'</span>]</span>
 <span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>world2.columns <span class="op">=</span> new_names</span>
 <span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>world2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2379,7 +2379,7 @@ <h3 data-number="2.2.4" class="anchored" data-anchor-id="sec-creating-attributes
 </div>
 </div>
 <p>To reorder columns, we can pass a modified columns list to the subsetting operator <code>[</code>. For example, the following expressions reorder <code>world2</code> columns in reverse alphabetical order.</p>
-<div id="5421e35a" class="cell" data-execution_count="35">
+<div id="826b87fd" class="cell" data-execution_count="35">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>names <span class="op">=</span> <span class="bu">sorted</span>(world2.columns, reverse<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>world2 <span class="op">=</span> world2[names]</span>
 <span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>world2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2468,7 +2468,7 @@ <h3 data-number="2.2.4" class="anchored" data-anchor-id="sec-creating-attributes
 <li>Convert from <code>GeoDataFrame</code> into a <code>DataFrame</code></li>
 </ol>
 <p>For example, by the end of the following code section <code>world2</code> becomes a regular <code>DataFrame</code>.</p>
-<div id="e80763bb" class="cell" data-execution_count="36">
+<div id="073d2c11" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>world2 <span class="op">=</span> world2.drop(<span class="st">'geom'</span>, axis<span class="op">=</span><span class="dv">1</span>)</span>
 <span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>world2 <span class="op">=</span> pd.DataFrame(world2)</span>
 <span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>world2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2559,7 +2559,7 @@ <h2 data-number="2.3" class="anchored" data-anchor-id="sec-manipulating-raster-o
 <section id="sec-raster-subsetting" class="level3" data-number="2.3.1">
 <h3 data-number="2.3.1" class="anchored" data-anchor-id="sec-raster-subsetting"><span class="header-section-number">2.3.1</span> Raster subsetting</h3>
 <p>When using <strong>rasterio</strong>, raster values are accessible through a <strong>numpy</strong> array, which can be imported with the <code>.read</code> method (as we saw in <a href="01-spatial-data.html#sec-using-rasterio" class="quarto-xref"><span>Section 1.3.1</span></a>). As shown in <a href="01-spatial-data.html#sec-using-rasterio" class="quarto-xref"><span>Section 1.3.1</span></a>, reading a single raster layer (or the only layer of a single-band raster, such as here) results in a two-dimensional array:</p>
-<div id="f56b4932" class="cell" data-execution_count="37">
+<div id="91a80a38" class="cell" data-execution_count="37">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>elev <span class="op">=</span> src_elev.read(<span class="dv">1</span>)</span>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>elev</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="37">
@@ -2572,14 +2572,14 @@ <h3 data-number="2.3.1" class="anchored" data-anchor-id="sec-raster-subsetting">
 </div>
 </div>
 <p>Then, we can access any subset of cell values using <strong>numpy</strong> methods, keeping in mind that dimensions order is <code>(rows, columns)</code>. For example, <code>elev[1,2]</code> returns the value at row 2, column 3.</p>
-<div id="b2bf8aaa" class="cell" data-execution_count="38">
+<div id="a483d256" class="cell" data-execution_count="38">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>elev[<span class="dv">1</span>, <span class="dv">2</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="38">
 <pre><code>np.uint8(9)</code></pre>
 </div>
 </div>
 <p>Cell values can be modified by overwriting existing values in conjunction with a subsetting operation, e.g., <code>elev[1,2]=0</code> to set cell at row 2, column 3 of <code>elev</code> to <code>0</code>.</p>
-<div id="d9a6d50d" class="cell" data-execution_count="39">
+<div id="7348f085" class="cell" data-execution_count="39">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>elev[<span class="dv">1</span>, <span class="dv">2</span>] <span class="op">=</span> <span class="dv">0</span></span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>elev</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="39">
@@ -2592,7 +2592,7 @@ <h3 data-number="2.3.1" class="anchored" data-anchor-id="sec-raster-subsetting">
 </div>
 </div>
 <p>Multiple cells can also be modified in this way, e.g., <code>elev[0,0:3]=0</code> to set the first three cells in the first row to <code>0</code>.</p>
-<div id="d764d7b9" class="cell" data-execution_count="40">
+<div id="497c7d6f" class="cell" data-execution_count="40">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>elev[<span class="dv">0</span>, <span class="dv">0</span>:<span class="dv">3</span>] <span class="op">=</span> <span class="dv">0</span></span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>elev</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="40">
@@ -2605,7 +2605,7 @@ <h3 data-number="2.3.1" class="anchored" data-anchor-id="sec-raster-subsetting">
 </div>
 </div>
 <p>Alternatively, reading more than one layer, or all layers (even if there is just one, such as here) results in a three-dimensional array.</p>
-<div id="6ef96ac7" class="cell" data-execution_count="41">
+<div id="d269950f" class="cell" data-execution_count="41">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>elev3d <span class="op">=</span> src_elev.read()</span>
 <span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a>elev3d</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="41">
@@ -2631,7 +2631,7 @@ <h3 data-number="2.3.1" class="anchored" data-anchor-id="sec-raster-subsetting">
 </div>
 </div>
 <p>In three-dimensional arrays, we access cell values using three indices, keeping in mind that dimensions order is <code>(layers,rows, columns)</code> For example, to get the same value shown above, at row 2, column 3 (at band 1), we use <code>elev[0,1,2]</code> instead of <code>elev[1,2]</code>.</p>
-<div id="5c94f2a1" class="cell" data-execution_count="42">
+<div id="5c462049" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>elev3d[<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>] </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="42">
 <pre><code>np.uint8(9)</code></pre>
@@ -2641,14 +2641,14 @@ <h3 data-number="2.3.1" class="anchored" data-anchor-id="sec-raster-subsetting">
 <section id="sec-summarizing-raster-objects" class="level3" data-number="2.3.2">
 <h3 data-number="2.3.2" class="anchored" data-anchor-id="sec-summarizing-raster-objects"><span class="header-section-number">2.3.2</span> Summarizing raster objects</h3>
 <p>Global summaries of raster values can be calculated by applying <strong>numpy</strong> summary functions on the array with raster values, e.g., <code>np.mean</code>.</p>
-<div id="c999a943" class="cell" data-execution_count="43">
+<div id="f8aa356c" class="cell" data-execution_count="43">
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>np.mean(elev)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="43">
 <pre><code>np.float64(18.083333333333332)</code></pre>
 </div>
 </div>
 <p>Note that ‘No Data’-safe functions–such as <code>np.nanmean</code>—should be used in case the raster contains ‘No Data’ values which need to be ignored. Before we can demonstrate that, we must convert the array from <code>int</code> to <code>float</code>, as <code>int</code> arrays cannot contain <code>np.nan</code> (due to computer memory limitations).</p>
-<div id="25a5142d" class="cell" data-execution_count="44">
+<div id="c035cf00" class="cell" data-execution_count="44">
 <div class="sourceCode cell-code" id="cb48"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a>elev1 <span class="op">=</span> elev.copy()</span>
 <span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a>elev1 <span class="op">=</span> elev1.astype(<span class="st">'float64'</span>)</span>
 <span id="cb48-3"><a href="#cb48-3" aria-hidden="true" tabindex="-1"></a>elev1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2662,7 +2662,7 @@ <h3 data-number="2.3.2" class="anchored" data-anchor-id="sec-summarizing-raster-
 </div>
 </div>
 <p>Now we can insert an <code>np.nan</code> value into the array, for example to a cell located in the first row and third column. (Doing so in the original <code>elev</code> array raises an error, because an <code>int</code> array cannot accommodate <code>np.nan</code>, as mentioned above; try it to see for yourself.)</p>
-<div id="f81a2649" class="cell" data-execution_count="45">
+<div id="cdc660f8" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb50"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a>elev1[<span class="dv">0</span>, <span class="dv">2</span>] <span class="op">=</span> np.nan</span>
 <span id="cb50-2"><a href="#cb50-2" aria-hidden="true" tabindex="-1"></a>elev1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="45">
@@ -2675,14 +2675,14 @@ <h3 data-number="2.3.2" class="anchored" data-anchor-id="sec-summarizing-raster-
 </div>
 </div>
 <p>With the <code>np.nan</code> value inplace, the <code>np.mean</code> summary value becomes unknown (<code>np.nan</code>).</p>
-<div id="ef0ce696" class="cell" data-execution_count="46">
+<div id="f8505ccd" class="cell" data-execution_count="46">
 <div class="sourceCode cell-code" id="cb52"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a>np.mean(elev1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="46">
 <pre><code>np.float64(nan)</code></pre>
 </div>
 </div>
 <p>To get a summary of all non-missing values, we need to use one of the specialized <strong>numpy</strong> functions that ignore ‘No Data’ values, such as <code>np.nanmean</code>:</p>
-<div id="aa697926" class="cell" data-execution_count="47">
+<div id="9230e4e6" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb54"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>np.nanmean(elev1)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="47">
 <pre><code>np.float64(18.6)</code></pre>
@@ -2705,7 +2705,7 @@ <h3 data-number="2.3.2" class="anchored" data-anchor-id="sec-summarizing-raster-
 </div>
 </div>
 <p>To summarize the distribution of values in a categorical raster, we can calculate the frequencies of unique values, and draw them using a barplot. Let’s demonstrate using the <code>grain.tif</code> small categorical raster.</p>
-<div id="e21f8633" class="cell" data-execution_count="49">
+<div id="dbcdaffc" class="cell" data-execution_count="49">
 <div class="sourceCode cell-code" id="cb57"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>grain <span class="op">=</span> src_grain.read(<span class="dv">1</span>)</span>
 <span id="cb57-2"><a href="#cb57-2" aria-hidden="true" tabindex="-1"></a>grain</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="49">
@@ -2718,7 +2718,7 @@ <h3 data-number="2.3.2" class="anchored" data-anchor-id="sec-summarizing-raster-
 </div>
 </div>
 <p>To calculate the frequency of unique values in an array, we use the <code>np.unique</code> with the <code>return_counts=True</code> option. The result is a <code>tuple</code> with two corresponding arrays: the unique values, and their counts.</p>
-<div id="266cd328" class="cell" data-execution_count="50">
+<div id="6fbe0a0f" class="cell" data-execution_count="50">
 <div class="sourceCode cell-code" id="cb59"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a>freq <span class="op">=</span> np.unique(grain, return_counts<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb59-2"><a href="#cb59-2" aria-hidden="true" tabindex="-1"></a>freq</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="50">
diff --git a/03-spatial-operations.html b/03-spatial-operations.html
index 32f97895..8c2fa965 100644
--- a/03-spatial-operations.html
+++ b/03-spatial-operations.html
@@ -365,7 +365,7 @@ <h1 class="title"><span id="sec-spatial-operations" class="quarto-section-identi
 <section id="prerequisites" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2>
 <p>This chapter requires importing the following packages:</p>
-<div id="837f9488" class="cell" data-execution_count="3">
+<div id="b01c08cc" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
@@ -380,7 +380,7 @@ <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2
 <span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio.features</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>It also relies on the following data files:</p>
-<div id="19000acd" class="cell" data-execution_count="5">
+<div id="a0d9b83b" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>nz <span class="op">=</span> gpd.read_file(<span class="st">'data/nz.gpkg'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>nz_height <span class="op">=</span> gpd.read_file(<span class="st">'data/nz_height.gpkg'</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>world <span class="op">=</span> gpd.read_file(<span class="st">'data/world.gpkg'</span>)</span>
@@ -419,7 +419,7 @@ <h3 data-number="3.2.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 <!-- md: I don't think we considered it yet (the order as the moment is the same as in 'geocompr') -->
 <p>Spatial subsetting is the process of taking a spatial object and returning a new object containing only features that relate in space to another object. Analogous to attribute subsetting (covered in <a href="02-attribute-operations.html#sec-vector-attribute-subsetting" class="quarto-xref"><span>Section 2.2.1</span></a>), subsets of <code>GeoDataFrame</code>s can be created with square bracket (<code>[</code>) operator using the syntax <code>x[y]</code>, where <code>x</code> is an <code>GeoDataFrame</code> from which a subset of rows/features will be returned, and <code>y</code> is a boolean <code>Series</code>. The difference is, that, in spatial subsetting <code>y</code> is created based on another geometry and using one of the binary geometry relation methods, such as <code>.intersects</code> (see <a href="#sec-topological-relations" class="quarto-xref"><span>Section 3.2.2</span></a>), rather than based on comparison based on ordinary columns.</p>
 <p>To demonstrate spatial subsetting, we will use the <code>nz</code> and <code>nz_height</code> layers, which contain geographic data on the 16 main regions and 101 highest points in New Zealand, respectively (<a href="#fig-spatial-subset" class="quarto-xref">Figure&nbsp;<span>3.1</span></a> (a)), in a projected coordinate system. The following expression creates a new object, <code>canterbury</code>, representing only one region—Canterbury.</p>
-<div id="450b1b7e" class="cell" data-execution_count="6">
+<div id="eb03ee38" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>canterbury <span class="op">=</span> nz[nz[<span class="st">'Name'</span>] <span class="op">==</span> <span class="st">'Canterbury'</span>]</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>canterbury</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="6">
@@ -454,7 +454,7 @@ <h3 data-number="3.2.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </div>
 </div>
 <p>Then, we use the <code>.intersects</code> method to evaluate, for each of the <code>nz_height</code> points, whether they intersect with Canterbury. The result <code>canterbury_height</code> is a boolean <code>Series</code> with the ‘answers’.</p>
-<div id="e53d4fd8" class="cell" data-execution_count="7">
+<div id="96cf80a6" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>sel <span class="op">=</span> nz_height.intersects(canterbury.geometry.iloc[<span class="dv">0</span>])</span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>sel</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
@@ -469,7 +469,7 @@ <h3 data-number="3.2.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </div>
 </div>
 <p>Finally, we can subset <code>nz_height</code> using the obtained <code>Series</code>, resulting in the subset <code>canterbury_height</code> with only those points that intersect with Canterbury.</p>
-<div id="6bd52666" class="cell" data-execution_count="8">
+<div id="9da5bc1b" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>canterbury_height <span class="op">=</span> nz_height[sel]</span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>canterbury_height</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
@@ -580,7 +580,7 @@ <h3 data-number="3.2.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </div>
 <p>Like in attribute subsetting (<a href="02-attribute-operations.html#sec-vector-attribute-subsetting" class="quarto-xref"><span>Section 2.2.1</span></a>), we are using a boolean series (<code>sel</code>), of the same length as the number of rows in the filtered table (<code>nz_height</code>), created based on a condition applied on itself. The difference is that the condition is not a comparison of attribute values, but an evaluation of a spatial relation. Namely, we evaluate whether each geometry of <code>nz_height</code> intersects with the <code>canterbury</code> geometry, using the <code>.intersects</code> method.</p>
 <p>Various topological relations can be used for spatial subsetting which determine the type of spatial relationship that features in the target object must have with the subsetting object to be selected. These include touches, crosses, or within, as we will see shortly in <a href="#sec-topological-relations" class="quarto-xref"><span>Section 3.2.2</span></a>. Intersects (<code>.intersects</code>), which we used in the last example, is the most commonly used method. This is a ‘catch all’ topological relation, that will return features in the target that touch, cross or are within the source ‘subsetting’ object. As an example of another method, we can use <code>.disjoint</code> to obtain all points that <em>do not</em> intersect with Canterbury.</p>
-<div id="718cd90f" class="cell" data-execution_count="10">
+<div id="bb2f75b2" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>sel <span class="op">=</span> nz_height.disjoint(canterbury.geometry.iloc[<span class="dv">0</span>])</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>canterbury_height2 <span class="op">=</span> nz_height[sel]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -628,7 +628,7 @@ <h3 data-number="3.2.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </figure>
 </div>
 <p>In case we need to subset according to several geometries at once, e.g., find out which points intersect with both Canterbury and Southland, we can dissolve the filtering subset, using <code>.union_all</code>, before applying the <code>.intersects</code> (or any other) operator. For example, here is how we can subset the <code>nz_height</code> points which intersect with Canterbury or Southland. (Note that we are also using the <code>.isin</code> method, as demonstrated at the end of <a href="02-attribute-operations.html#sec-vector-attribute-subsetting" class="quarto-xref"><span>Section 2.2.1</span></a>.)</p>
-<div id="d865405f" class="cell" data-execution_count="12">
+<div id="d8a64bf8" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>canterbury_southland <span class="op">=</span> nz[nz[<span class="st">'Name'</span>].isin([<span class="st">'Canterbury'</span>, <span class="st">'Southland'</span>])]</span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>sel <span class="op">=</span> nz_height.intersects(canterbury_southland.union_all())</span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>canterbury_southland_height <span class="op">=</span> nz_height[sel]</span>
@@ -769,7 +769,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </figure>
 </div>
 <p>In <strong>shapely</strong>, methods testing for different types of topological relations are known as ‘relationships’. <strong>geopandas</strong> provides their wrappers (with the same method name) which can be applied on multiple geometries at once (such as <code>.intersects</code> and <code>.disjoint</code> applied on all points in <code>nz_height</code>, see <a href="#sec-spatial-subsetting-vector" class="quarto-xref"><span>Section 3.2.1</span></a>). To see how topological relations work in practice, let’s create a simple reproducible example, building on the relations illustrated in <a href="#fig-spatial-relations" class="quarto-xref">Figure&nbsp;<span>3.4</span></a> and consolidating knowledge of how vector geometries are represented from a previous chapter (<a href="01-spatial-data.html#sec-geometry-columns" class="quarto-xref"><span>Section 1.2.3</span></a> and <a href="01-spatial-data.html#sec-geometries" class="quarto-xref"><span>Section 1.2.5</span></a>).</p>
-<div id="d328832c" class="cell" data-execution_count="14">
+<div id="17f9c4fb" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>points <span class="op">=</span> gpd.GeoSeries([</span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>  shapely.Point(<span class="fl">0.2</span>,<span class="fl">0.1</span>), </span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>  shapely.Point(<span class="fl">0.7</span>,<span class="fl">0.2</span>), </span>
@@ -806,7 +806,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </div>
 </div>
 <p>A simple query is: which of the points in <code>points</code> intersect in some way with polygon <code>poly</code>? The question can be answered by visual inspection (points <code>0</code> and <code>2</code> are touching and are within the polygon, respectively). Alternatively, we can get the solution with the <code>.intersects</code> method, which reports whether or not each geometry in a <code>GeoSeries</code> (<code>points</code>) intersects with a single <code>shapely</code> geometry (<code>poly.iloc[0]</code>).</p>
-<div id="f324307e" class="cell" data-execution_count="16">
+<div id="0e9e63db" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>points.intersects(poly.iloc[<span class="dv">0</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="16">
 <pre><code>0     True
@@ -818,7 +818,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 <p>The result shown above is a boolean <code>Series</code>. Its contents should match our intuition: positive (<code>True</code>) results are returned for the points <code>0</code> and <code>2</code>, and a negative result (<code>False</code>) for point <code>1</code>. Each value in this <code>Series</code> represents a feature in the first input (<code>points</code>).</p>
 <p>All earlier examples in this chapter demonstrate the ‘many-to-one’ mode of <code>.intersects</code> and analogous methods, where the relation is evaluated between each of several geometries in a <code>GeoSeries</code>/<code>GeoDataFrame</code>, and an individual <code>shapely</code> geometry. A second mode of those methods (not demonstrated here) is when both inputs are <code>GeoSeries</code>/<code>GeoDataFrame</code> objects. In such case, a ‘pairwise’ evaluation takes place between geometries aligned by index (<code>align=True</code>, the default) or by position (<code>align=False</code>). For example, the expression <code>nz.intersects(nz)</code> returns a <code>Series</code> of 16 <code>True</code> values, indicating (unsurprisingly) that each geometry in <code>nz</code> intersects with itself.</p>
 <p>A third mode is when we are interested in a ‘many-to-many’ evaluation, i.e., obtaining a matrix of all pairwise combinations of geometries from two <code>GeoSeries</code> objects. At the time of writing, there is no built-in method to do this in <strong>geopandas</strong>. However, the <code>.apply</code> method (package <strong>pandas</strong>) can be used to repeat a ‘many-to-one’ evaluation over all geometries in the second layer, resulting in a matrix of <em>pairwise</em> results. We will create another <code>GeoSeries</code> with two polygons, named <code>poly2</code>, to demonstrate this.</p>
-<div id="1b593c49" class="cell" data-execution_count="17">
+<div id="920dd23d" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>poly2 <span class="op">=</span> gpd.GeoSeries([</span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>  shapely.Polygon([(<span class="dv">0</span>,<span class="dv">0</span>), (<span class="dv">0</span>,<span class="dv">1</span>), (<span class="dv">1</span>,<span class="dv">1</span>), (<span class="dv">1</span>,<span class="fl">0.5</span>), (<span class="dv">0</span>,<span class="dv">0</span>)]),</span>
 <span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>  shapely.Polygon([(<span class="dv">0</span>,<span class="dv">0</span>), (<span class="dv">1</span>,<span class="fl">0.5</span>), (<span class="dv">1</span>,<span class="dv">0</span>), (<span class="dv">0</span>,<span class="dv">0</span>)])</span>
@@ -847,7 +847,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </div>
 </div>
 <p>Now we can use <code>.apply</code> to get the intersection relations matrix. The result is a <code>DataFrame</code>, where each row represents a <code>points</code> geometry and each column represents a <code>poly2</code> geometry. We can see that the point <code>0</code> intersects with both polygons, while points <code>1</code> and <code>2</code> intersect with one of the polygons each.</p>
-<div id="e29789d0" class="cell" data-execution_count="19">
+<div id="283b0554" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>points.<span class="bu">apply</span>(<span class="kw">lambda</span> x: poly2.intersects(x))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="19">
 <div>
@@ -907,7 +907,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </div>
 <div class="callout-body-container callout-body">
 <p>Since the above result, like any pairwise matrix, (1) is composed of values of the same type, and (2) has no contrasting role for rows and columns, is may be more convenient to use a plain <strong>numpy</strong> array to work with it. In such case, we can use the <code>.to_numpy</code> method to go from <code>DataFrame</code> to <code>ndarray</code>.</p>
-<div id="e6d2120f" class="cell" data-execution_count="20">
+<div id="4c81f2aa" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>points.<span class="bu">apply</span>(<span class="kw">lambda</span> x: poly2.intersects(x)).to_numpy()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="20">
 <pre><code>array([[ True,  True],
@@ -918,7 +918,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </div>
 </div>
 <p>The <code>.intersects</code> method returns <code>True</code> even in cases where the features just touch: intersects is a ‘catch-all’ topological operation which identifies many types of spatial relations, as illustrated in <a href="#fig-spatial-relations" class="quarto-xref">Figure&nbsp;<span>3.4</span></a>. More restrictive questions include which points lie within the polygon, and which features are on or contain a shared boundary with it? The first question can be answered with <code>.within</code>, and the second with <code>.touches</code>.</p>
-<div id="cab8a1ff" class="cell" data-execution_count="21">
+<div id="fd2a0595" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>points.within(poly.iloc[<span class="dv">0</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="21">
 <pre><code>0    False
@@ -927,7 +927,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 dtype: bool</code></pre>
 </div>
 </div>
-<div id="b52223b3" class="cell" data-execution_count="22">
+<div id="d3574ccc" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>points.touches(poly.iloc[<span class="dv">0</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
 <pre><code>0     True
@@ -937,7 +937,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </div>
 </div>
 <p>Note that although the point <code>0</code> touches the boundary polygon, it is not within it; point <code>2</code> is within the polygon but does not touch any part of its border. The opposite of <code>.intersects</code> is <code>.disjoint</code>, which returns only objects that do not spatially relate in any way to the selecting object.</p>
-<div id="10222d46" class="cell" data-execution_count="23">
+<div id="1dc96099" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>points.disjoint(poly.iloc[<span class="dv">0</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
 <pre><code>0    False
@@ -947,7 +947,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </div>
 </div>
 <p>Another useful type of relation is ‘within distance’, where we detect features that intersect with the target buffered by particular distance. Buffer distance determines how close target objects need to be before they are selected. This can be done by literally buffering (<a href="01-spatial-data.html#sec-geometries" class="quarto-xref"><span>Section 1.2.5</span></a>) the target geometry, and evaluating intersection (<code>.intersects</code>). Another way is to calculate the distances using the <code>.distance</code> method, and then evaluate whether they are within a threshold distance.</p>
-<div id="623b271f" class="cell" data-execution_count="24">
+<div id="063d362a" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>points.distance(poly.iloc[<span class="dv">0</span>]) <span class="op">&lt;</span> <span class="fl">0.2</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="24">
 <pre><code>0    True
@@ -957,7 +957,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 </div>
 </div>
 <p>Note that although point <code>1</code> is more than <code>0.2</code> units of distance from the nearest vertex of <code>poly</code>, it is still selected when the distance is set to <code>0.2</code>. This is because distance is measured to the nearest edge, in this case, the part of the polygon that lies directly above point 2 in Figure <a href="#fig-spatial-relations" class="quarto-xref">Figure&nbsp;<span>3.4</span></a>. We can verify that the actual distance between point <code>1</code> and the polygon is <code>0.13</code>, as follows.</p>
-<div id="c782e727" class="cell" data-execution_count="25">
+<div id="6c4230c9" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>points.iloc[<span class="dv">1</span>].distance(poly.iloc[<span class="dv">0</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="25">
 <pre><code>0.13416407864998736</code></pre>
@@ -969,7 +969,7 @@ <h3 data-number="3.2.2" class="anchored" data-anchor-id="sec-topological-relatio
 <h3 data-number="3.2.3" class="anchored" data-anchor-id="sec-spatial-joining"><span class="header-section-number">3.2.3</span> Spatial joining</h3>
 <p>Joining two non-spatial datasets uses a shared ‘key’ variable, as described in <a href="02-attribute-operations.html#sec-vector-attribute-joining" class="quarto-xref"><span>Section 2.2.3</span></a>. Spatial data joining applies the same concept, but instead relies on spatial relations, described in the previous section. As with attribute data, joining adds new columns to the target object (the argument <code>x</code> in joining functions), from a source object (<code>y</code>).</p>
 <p>The following example illustrates the process: imagine you have ten points randomly distributed across the Earth’s surface and you ask, for the points that are on land, which countries are they in? Implementing this idea in a reproducible example will build your geographic data handling skills and show how spatial joins work. The starting point is to create points that are randomly scattered over the planar surface that represents Earth’s geographic coordinates, in decimal degrees (<a href="#fig-spatial-join" class="quarto-xref">Figure&nbsp;<span>3.7</span></a> (a)).</p>
-<div id="d7919b55" class="cell" data-execution_count="26">
+<div id="cf4340bb" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>np.random.seed(<span class="dv">11</span>)       <span class="co">## set seed for reproducibility</span></span>
 <span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>bb <span class="op">=</span> world.total_bounds  <span class="co">## the world's bounds</span></span>
 <span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>x <span class="op">=</span> np.random.uniform(low<span class="op">=</span>bb[<span class="dv">0</span>], high<span class="op">=</span>bb[<span class="dv">2</span>], size<span class="op">=</span><span class="dv">10</span>)</span>
@@ -1025,7 +1025,7 @@ <h3 data-number="3.2.3" class="anchored" data-anchor-id="sec-spatial-joining"><s
 </div>
 </div>
 <p>The scenario illustrated in <a href="#fig-spatial-join" class="quarto-xref">Figure&nbsp;<span>3.7</span></a> shows that the <code>random_points</code> object (top left) lacks attribute data, while the world (top right) has attributes, including country names that are shown for a sample of countries in the legend. Before creating the joined dataset, we use spatial subsetting to create <code>world_random</code>, which contains only countries that contain random points, to verify the number of country names returned in the joined dataset should be four (see the top right panel of <a href="#fig-spatial-join" class="quarto-xref">Figure&nbsp;<span>3.7</span></a> (b)).</p>
-<div id="45789e2e" class="cell" data-execution_count="27">
+<div id="de8f2601" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>world_random <span class="op">=</span> world[world.intersects(random_points.union_all())]</span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>world_random</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
@@ -1084,7 +1084,7 @@ <h3 data-number="3.2.3" class="anchored" data-anchor-id="sec-spatial-joining"><s
 </div>
 </div>
 <p>Spatial joins are implemented with <code>x.sjoin(y)</code>, as illustrated in the code chunk below. The output is the <code>random_joined</code> object which is illustrated in <a href="#fig-spatial-join" class="quarto-xref">Figure&nbsp;<span>3.7</span></a> (c).</p>
-<div id="7f2106e1" class="cell" data-execution_count="28">
+<div id="068c3db4" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>random_joined <span class="op">=</span> random_points.sjoin(world, how<span class="op">=</span><span class="st">'left'</span>)</span>
 <span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>random_joined</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="28">
@@ -1247,7 +1247,7 @@ <h3 data-number="3.2.4" class="anchored" data-anchor-id="non-overlapping-joins">
 </div>
 </div>
 <p>We can check if any of the points are the same by creating a pairwise boolean matrix of <code>.intersects</code> relations, then evaluating whether any of the values in it is <code>True</code>. Note that the <code>.to_numpy</code> method is applied to go from a <code>DataFrame</code> to an <code>ndarray</code>, for which <code>.any</code> gives a global rather than a row-wise summary. This is what we want in this case, because we are interested in whether any of the points intersect, not whether any of the points in each row intersect.</p>
-<div id="a5dc00a3" class="cell" data-execution_count="31">
+<div id="73435b9a" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>m <span class="op">=</span> cycle_hire.geometry.<span class="bu">apply</span>(</span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>  <span class="kw">lambda</span> x: cycle_hire_osm.geometry.intersects(x)</span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>)</span>
@@ -1257,7 +1257,7 @@ <h3 data-number="3.2.4" class="anchored" data-anchor-id="non-overlapping-joins">
 </div>
 </div>
 <p>Imagine that we need to join the capacity variable in <code>cycle_hire_osm</code> (<code>'capacity'</code>) onto the official ‘target’ data contained in <code>cycle_hire</code>, which looks as follows.</p>
-<div id="a744a262" class="cell" data-execution_count="32">
+<div id="da5aa684" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>cycle_hire</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="32">
 <div>
@@ -1339,7 +1339,7 @@ <h3 data-number="3.2.4" class="anchored" data-anchor-id="non-overlapping-joins">
 </div>
 </div>
 <p>This is when a non-overlapping join is needed. Spatial join (<code>gpd.sjoin</code>) along with buffered geometries (see <a href="04-geometry-operations.html#sec-buffers" class="quarto-xref"><span>Section 4.2.3</span></a>) can be used to do that, as demonstrated below using a threshold distance of 20 <span class="math inline">\(m\)</span>. Note that we transform the data to a projected CRS (<code>27700</code>) to use real buffer distances, in meters (see <a href="06-reproj.html#sec-geometry-operations-on-projected-and-unprojected-data" class="quarto-xref"><span>Section 6.4</span></a>).</p>
-<div id="28d7dd19" class="cell" data-execution_count="33">
+<div id="a5a8980e" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb39"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>crs <span class="op">=</span> <span class="dv">27700</span></span>
 <span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>cycle_hire_buffers <span class="op">=</span> cycle_hire.copy().to_crs(crs)</span>
 <span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>cycle_hire_buffers.geometry <span class="op">=</span> cycle_hire_buffers.<span class="bu">buffer</span>(<span class="dv">20</span>)</span>
@@ -1429,7 +1429,7 @@ <h3 data-number="3.2.4" class="anchored" data-anchor-id="non-overlapping-joins">
 </div>
 </div>
 <p>Note that the number of rows in the joined result is greater than the target. This is because some cycle hire stations in <code>cycle_hire_buffers</code> have multiple matches in <code>cycle_hire_osm</code>. To aggregate the values for the overlapping points and return the mean, we can use the aggregation methods shown in <a href="02-attribute-operations.html#sec-vector-attribute-aggregation" class="quarto-xref"><span>Section 2.2.2</span></a>, resulting in an object with the same number of rows as the target. We also go back from buffers to points using <code>.centroid</code> method.</p>
-<div id="b6b79da6" class="cell" data-execution_count="34">
+<div id="c26487a7" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>cycle_hire_buffers <span class="op">=</span> cycle_hire_buffers[[<span class="st">'id'</span>, <span class="st">'capacity'</span>, <span class="st">'geometry'</span>]] <span class="op">\</span></span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>    .dissolve(by<span class="op">=</span><span class="st">'id'</span>, aggfunc<span class="op">=</span><span class="st">'mean'</span>) <span class="op">\</span></span>
 <span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>    .reset_index()</span>
@@ -1551,7 +1551,7 @@ <h3 data-number="3.2.5" class="anchored" data-anchor-id="sec-vector-spatial-aggr
 <li>Joining the result back to <code>nz</code>—using <code>pd.merge</code></li>
 </ol>
 <p>First, we ‘attach’ the region classification of each point, using spatial join (<a href="#sec-spatial-joining" class="quarto-xref"><span>Section 3.2.3</span></a>). Note that we are using the minimal set of columns required: the geometries (for the spatial join to work), the point elevation (to later calculate an average), and the region name (to use as key when joining the results back to <code>nz</code>). The result tells us which <code>nz</code> region each elevation point falls in.</p>
-<div id="6fe2044a" class="cell" data-execution_count="36">
+<div id="3d01b373" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>nz_height2 <span class="op">=</span> gpd.sjoin(</span>
 <span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a>  nz_height[[<span class="st">'elevation'</span>, <span class="st">'geometry'</span>]], </span>
 <span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a>  nz[[<span class="st">'Name'</span>, <span class="st">'geometry'</span>]], </span>
@@ -1630,7 +1630,7 @@ <h3 data-number="3.2.5" class="anchored" data-anchor-id="sec-vector-spatial-aggr
 </div>
 </div>
 <p>Second, we calculate the average elevation, using ordinary (non-spatial) aggregation (<a href="02-attribute-operations.html#sec-vector-attribute-aggregation" class="quarto-xref"><span>Section 2.2.2</span></a>). This result tells us the average elevation of all <code>nz_height</code> points located within each <code>nz</code> region.</p>
-<div id="b2a87ed4" class="cell" data-execution_count="37">
+<div id="9247ba1d" class="cell" data-execution_count="37">
 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>nz_height2 <span class="op">=</span> nz_height2.groupby(<span class="st">'Name'</span>)[[<span class="st">'elevation'</span>]].mean().reset_index()</span>
 <span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a>nz_height2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="37">
@@ -1689,7 +1689,7 @@ <h3 data-number="3.2.5" class="anchored" data-anchor-id="sec-vector-spatial-aggr
 </div>
 </div>
 <p>The third and final step is joining the averages back to the <code>nz</code> layer.</p>
-<div id="fa5bee75" class="cell" data-execution_count="38">
+<div id="0e317940" class="cell" data-execution_count="38">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>nz2 <span class="op">=</span> pd.merge(nz[[<span class="st">'Name'</span>, <span class="st">'geometry'</span>]], nz_height2, on<span class="op">=</span><span class="st">'Name'</span>, how<span class="op">=</span><span class="st">'left'</span>)</span>
 <span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a>nz2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="38">
@@ -1782,7 +1782,7 @@ <h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-joining-incongruent
 <p>Spatial congruence is an important concept related to spatial aggregation. An aggregating object (which we will refer to as <code>y</code>) is congruent with the target object (<code>x</code>) if the two objects have shared borders. Often this is the case for administrative boundary data, whereby larger units—such as Middle Layer Super Output Areas (MSOAs) in the UK, or districts in many other European countries—are composed of many smaller units.</p>
 <p>Incongruent aggregating objects, by contrast, do not share common borders with the target <span class="citation" data-cites="qiu_development_2012">(<a href="references.html#ref-qiu_development_2012" role="doc-biblioref">Qiu, Zhang, and Zhou 2012</a>)</span>. This is problematic for spatial aggregation (and other spatial operations) illustrated in <a href="#fig-nz-and-grid" class="quarto-xref">Figure&nbsp;<span>3.11</span></a>: aggregating the centroid of each sub-zone will not return accurate results. Areal interpolation overcomes this issue by transferring values from one set of areal units to another, using a range of algorithms including simple area-weighted approaches and more sophisticated approaches such as ‘pycnophylactic’ methods <span class="citation" data-cites="tobler_smooth_1979">(<a href="references.html#ref-tobler_smooth_1979" role="doc-biblioref">Tobler 1979</a>)</span>.</p>
 <p>To demonstrate joining incongruent layers, we will create a ‘synthetic’ layer comprising a regular grid of rectangles of size <span class="math inline">\(100\times100\)</span> <span class="math inline">\(km\)</span>, covering the extent of the <code>nz</code> layer. This recipe can be used to create a regular grid covering any given layer (other than <code>nz</code>), at the specified resolution (<code>res</code>). Most of the functions have been explained in previous chapters; we leave it as an exercise for the reader to explore how the code works.</p>
-<div id="80f5d890" class="cell" data-execution_count="40">
+<div id="caf980d5" class="cell" data-execution_count="40">
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Settings: grid extent, resolution, and CRS</span></span>
 <span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a>bounds <span class="op">=</span> nz.total_bounds</span>
 <span id="cb46-3"><a href="#cb46-3" aria-hidden="true" tabindex="-1"></a>crs <span class="op">=</span> nz.crs</span>
@@ -1887,7 +1887,7 @@ <h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-joining-incongruent
 </div>
 <p>Our goal, now, is to ‘transfer’ the <code>'Population'</code> attribute (<a href="#fig-nz-and-grid" class="quarto-xref">Figure&nbsp;<span>3.11</span></a>) to the rectangular grid polygons, which is an example of a join between incongruent layers. To do that, we basically need to calculate–for each <code>grid</code> cell—the weighted sum of the population in <code>nz</code> polygons coinciding with that cell. The weights in the weighted sum calculation are the ratios between the area of the coinciding ‘part’ out of the entire <code>nz</code> polygon. That is, we (inevitably) assume that the population in each <code>nz</code> polygon is equally distributed across space, therefore a partial <code>nz</code> polygon contains the respective partial population size.</p>
 <p>We start by calculating the entire area of each <code>nz</code> polygon, as follows, using the <code>.area</code> method (<a href="01-spatial-data.html#sec-area-length" class="quarto-xref"><span>Section 1.2.7</span></a>).</p>
-<div id="574a7215" class="cell" data-execution_count="42">
+<div id="84b4a8df" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb48"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a>nz[<span class="st">'area'</span>] <span class="op">=</span> nz.area</span>
 <span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a>nz</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="42">
@@ -1970,7 +1970,7 @@ <h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-joining-incongruent
 </div>
 </div>
 <p>Next, we use the <code>.overlay</code> method to calculate the pairwise intersections between <code>nz</code> and <code>grid</code>. As a result, we now have a layer where each <code>nz</code> polygon is split according to the <code>grid</code> polygons, hereby named <code>nz_grid</code>.</p>
-<div id="7202ab0d" class="cell" data-execution_count="43">
+<div id="eaab6c83" class="cell" data-execution_count="43">
 <div class="sourceCode cell-code" id="cb49"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>nz_grid <span class="op">=</span> nz.overlay(grid)</span>
 <span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a>nz_grid <span class="op">=</span> nz_grid[[<span class="st">'id'</span>, <span class="st">'area'</span>, <span class="st">'Population'</span>, <span class="st">'geometry'</span>]]</span>
 <span id="cb49-3"><a href="#cb49-3" aria-hidden="true" tabindex="-1"></a>nz_grid</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2062,7 +2062,7 @@ <h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-joining-incongruent
 </div>
 </div>
 <p>We also need to calculate the areas of the intersections, here into a new attribute <code>'area_sub'</code>. If an <code>nz</code> polygon was completely within a single <code>grid</code> polygon, then <code>area_sub</code> is going to be equal to <code>area</code>; otherwise, it is going to be smaller.</p>
-<div id="d74b348e" class="cell" data-execution_count="45">
+<div id="ab867db3" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>nz_grid[<span class="st">'area_sub'</span>] <span class="op">=</span> nz_grid.area</span>
 <span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a>nz_grid</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="45">
@@ -2168,7 +2168,7 @@ <h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-joining-incongruent
 </div>
 </div>
 <p>Note that each of the intersections still holds the <code>Population</code> attribute of its ‘origin’ feature of <code>nz</code>, i.e., each portion of the <code>nz</code> area is associated with the original complete population count for that area. The real population size of each <code>nz_grid</code> feature, however, is smaller, or equal, depending on the geographic area proportion that it occupies out of the original <code>nz</code> feature. To make the correction, we first calculate the ratio (<code>area_prop</code>) and then multiply it by the population. The new (lowercase) attribute <code>population</code> now has the correct estimate of population sizes in <code>nz_grid</code>:</p>
-<div id="e4f0163e" class="cell" data-execution_count="47">
+<div id="05301569" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>nz_grid[<span class="st">'area_prop'</span>] <span class="op">=</span> nz_grid[<span class="st">'area_sub'</span>] <span class="op">/</span> nz_grid[<span class="st">'area'</span>]</span>
 <span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a>nz_grid[<span class="st">'population'</span>] <span class="op">=</span> nz_grid[<span class="st">'Population'</span>] <span class="op">*</span> nz_grid[<span class="st">'area_prop'</span>]</span>
 <span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a>nz_grid</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2252,7 +2252,7 @@ <h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-joining-incongruent
 </div>
 </div>
 <p>What is left to be done is to sum (see <a href="02-attribute-operations.html#sec-vector-attribute-aggregation" class="quarto-xref"><span>Section 2.2.2</span></a>) the population in all parts forming the same grid cell and join (see <a href="02-attribute-operations.html#sec-vector-attribute-joining" class="quarto-xref"><span>Section 2.2.3</span></a>) them back to the <code>grid</code> layer. Note that many of the grid cells have ‘No Data’ for population, because they have no intersection with <code>nz</code> at all (<a href="#fig-nz-and-grid" class="quarto-xref">Figure&nbsp;<span>3.11</span></a>).</p>
-<div id="a0f789d8" class="cell" data-execution_count="48">
+<div id="3fc7db45" class="cell" data-execution_count="48">
 <div class="sourceCode cell-code" id="cb54"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>nz_grid <span class="op">=</span> nz_grid.groupby(<span class="st">'id'</span>)[<span class="st">'population'</span>].<span class="bu">sum</span>().reset_index()</span>
 <span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a>grid <span class="op">=</span> pd.merge(grid, nz_grid[[<span class="st">'id'</span>, <span class="st">'population'</span>]], on<span class="op">=</span><span class="st">'id'</span>, how<span class="op">=</span><span class="st">'left'</span>)</span>
 <span id="cb54-3"><a href="#cb54-3" aria-hidden="true" tabindex="-1"></a>grid</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2342,13 +2342,13 @@ <h3 data-number="3.2.6" class="anchored" data-anchor-id="sec-joining-incongruent
 </div>
 </div>
 <p>We can demonstrate that, expectedly, the summed population in <code>nz</code> and <code>grid</code> is identical, even though the geometry is different (since we created <code>grid</code> to completely cover <code>nz</code>), by comparing the <code>.sum</code> of the <code>population</code> attribute in both layers.</p>
-<div id="1e2be4be" class="cell" data-execution_count="50">
+<div id="a01457dd" class="cell" data-execution_count="50">
 <div class="sourceCode cell-code" id="cb56"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a>nz[<span class="st">'Population'</span>].<span class="bu">sum</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="50">
 <pre><code>np.float64(4787200.0)</code></pre>
 </div>
 </div>
-<div id="7f8c4839" class="cell" data-execution_count="51">
+<div id="9c5104e7" class="cell" data-execution_count="51">
 <div class="sourceCode cell-code" id="cb58"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a>grid[<span class="st">'population'</span>].<span class="bu">sum</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="51">
 <pre><code>np.float64(4787199.999999998)</code></pre>
@@ -2381,7 +2381,7 @@ <h3 data-number="3.2.7" class="anchored" data-anchor-id="sec-distance-relations"
 </div>
 </div>
 <p>To illustrate the <code>.distance</code> method, let’s take the three highest points in New Zealand with <code>.sort_values</code> and <code>.iloc</code>.</p>
-<div id="06129abe" class="cell" data-execution_count="52">
+<div id="9082c642" class="cell" data-execution_count="52">
 <div class="sourceCode cell-code" id="cb60"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb60-1"><a href="#cb60-1" aria-hidden="true" tabindex="-1"></a>nz_highest <span class="op">=</span> nz_height.sort_values(by<span class="op">=</span><span class="st">'elevation'</span>, ascending<span class="op">=</span><span class="va">False</span>).iloc[:<span class="dv">3</span>, :]</span>
 <span id="cb60-2"><a href="#cb60-2" aria-hidden="true" tabindex="-1"></a>nz_highest</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="52">
@@ -2423,11 +2423,11 @@ <h3 data-number="3.2.7" class="anchored" data-anchor-id="sec-distance-relations"
 </div>
 </div>
 <p>Additionally, we need the geographic centroid of the Canterbury region (<code>canterbury</code>, created in <a href="#sec-spatial-subsetting-vector" class="quarto-xref"><span>Section 3.2.1</span></a>).</p>
-<div id="7e48e306" class="cell" data-execution_count="53">
+<div id="dccd28dd" class="cell" data-execution_count="53">
 <div class="sourceCode cell-code" id="cb61"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a>canterbury_centroid <span class="op">=</span> canterbury.centroid.iloc[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now we are able to apply <code>.distance</code> to calculate the distances from each of the three elevation points to the centroid of the Canterbury region.</p>
-<div id="c6b7a44f" class="cell" data-execution_count="54">
+<div id="1c05f5e3" class="cell" data-execution_count="54">
 <div class="sourceCode cell-code" id="cb62"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb62-1"><a href="#cb62-1" aria-hidden="true" tabindex="-1"></a>nz_highest.distance(canterbury_centroid)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="54">
 <pre><code>64    115539.995747
@@ -2437,7 +2437,7 @@ <h3 data-number="3.2.7" class="anchored" data-anchor-id="sec-distance-relations"
 </div>
 </div>
 <p>To obtain a distance matrix, i.e., a pairwise set of distances between all combinations of features in objects <code>x</code> and <code>y</code>, we need to use the <code>.apply</code> method (analogous to the way we created the <code>.intersects</code> boolean matrix in <a href="#sec-topological-relations" class="quarto-xref"><span>Section 3.2.2</span></a>). To illustrate this, let’s now take two regions in <code>nz</code>, Otago and Canterbury, represented by the object <code>co</code>.</p>
-<div id="c2da47dd" class="cell" data-execution_count="55">
+<div id="32e7b050" class="cell" data-execution_count="55">
 <div class="sourceCode cell-code" id="cb64"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a>sel <span class="op">=</span> nz[<span class="st">'Name'</span>].<span class="bu">str</span>.contains(<span class="st">'Canter|Otag'</span>)</span>
 <span id="cb64-2"><a href="#cb64-2" aria-hidden="true" tabindex="-1"></a>co <span class="op">=</span> nz[sel]</span>
 <span id="cb64-3"><a href="#cb64-3" aria-hidden="true" tabindex="-1"></a>co</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2481,7 +2481,7 @@ <h3 data-number="3.2.7" class="anchored" data-anchor-id="sec-distance-relations"
 </div>
 </div>
 <p>The distance matrix (technically speaking, a <code>DataFrame</code>) <code>d</code> between each of the first three elevation points, and the two regions, is then obtained as follows. In plain language, we take the geometry from each each row in <code>nz_height.iloc[:3,:]</code>, and apply the <code>.distance</code> method on <code>co</code> with its rows as the argument.</p>
-<div id="99b93fe0" class="cell" data-execution_count="56">
+<div id="43e04d89" class="cell" data-execution_count="56">
 <div class="sourceCode cell-code" id="cb65"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a>d <span class="op">=</span> nz_height.iloc[:<span class="dv">3</span>, :].<span class="bu">apply</span>(<span class="kw">lambda</span> x: co.distance(x.geometry), axis<span class="op">=</span><span class="dv">1</span>)</span>
 <span id="cb65-2"><a href="#cb65-2" aria-hidden="true" tabindex="-1"></a>d</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="56">
@@ -2552,10 +2552,10 @@ <h2 data-number="3.3" class="anchored" data-anchor-id="sec-spatial-ras"><span cl
 <section id="sec-spatial-subsetting-raster" class="level3" data-number="3.3.1">
 <h3 data-number="3.3.1" class="anchored" data-anchor-id="sec-spatial-subsetting-raster"><span class="header-section-number">3.3.1</span> Spatial subsetting</h3>
 <p>The previous chapter (and especially <a href="02-attribute-operations.html#sec-manipulating-raster-objects" class="quarto-xref"><span>Section 2.3</span></a>) demonstrated how to retrieve values associated with specific row and column combinations from a raster. Raster values can also be extracted by location (coordinates) and other spatial objects. To use coordinates for subsetting, we can use the <code>.sample</code> method of a <code>rasterio</code> file connection object, combined with a list of coordinate tuples. The method is demonstrated below to find the value of the cell that covers a point located at coordinates of <code>(0.1,0.1)</code> in <code>elev</code>. The returned object is a <em>generator</em>. The rationale for returning a generator, rather than a <code>list</code>, is memory efficiency. The number of sampled points may be huge, in which case we would want to generate the values one at a time rather than all at once.</p>
-<div id="8a2e0953" class="cell" data-execution_count="58">
+<div id="e472412d" class="cell" data-execution_count="58">
 <div class="sourceCode cell-code" id="cb67"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>src_elev.sample([(<span class="fl">0.1</span>, <span class="fl">0.1</span>)])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="58">
-<pre><code>&lt;generator object sample_gen at 0x7f78f0147290&gt;</code></pre>
+<pre><code>&lt;generator object sample_gen at 0x7fdd06d83290&gt;</code></pre>
 </div>
 </div>
 <div class="callout callout-style-default callout-note callout-titled">
@@ -2577,14 +2577,14 @@ <h3 data-number="3.3.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </div>
 </div>
 <p>In case we nevertheless want all values at once, such as when the number of points is small, we can force the generatrion of all values from a generator at once, using <code>list</code>. Since there was just one point, the result is one extracted value, in this case <code>16</code>.</p>
-<div id="dc65d471" class="cell" data-execution_count="59">
+<div id="bacd1c71" class="cell" data-execution_count="59">
 <div class="sourceCode cell-code" id="cb69"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>(src_elev.sample([(<span class="fl">0.1</span>, <span class="fl">0.1</span>)]))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="59">
 <pre><code>[array([16], dtype=uint8)]</code></pre>
 </div>
 </div>
 <p>We can use the same technique to extract the values of multiple points at once. For example, here we extract the raster values at two points, <code>(0.1,0.1)</code> and <code>(1.1,1.1)</code>. The resulting values are <code>16</code> and <code>6</code>.</p>
-<div id="d93e8fd3" class="cell" data-execution_count="60">
+<div id="ca943878" class="cell" data-execution_count="60">
 <div class="sourceCode cell-code" id="cb71"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>(src_elev.sample([(<span class="fl">0.1</span>, <span class="fl">0.1</span>), (<span class="fl">1.1</span>, <span class="fl">1.1</span>)]))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="60">
 <pre><code>[array([16], dtype=uint8), array([6], dtype=uint8)]</code></pre>
@@ -2625,7 +2625,7 @@ <h3 data-number="3.3.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </div>
 </div>
 <p>Another common use case of spatial subsetting is using a boolean mask, based on another raster with the same extent and resolution, or the original one, as illustrated in <a href="#fig-raster-subset" class="quarto-xref">Figure&nbsp;<span>3.17</span></a>. To do that, we erase the values in the array of one raster, according to another corresponding mask raster. For example, let’s read (<a href="01-spatial-data.html#sec-using-rasterio" class="quarto-xref"><span>Section 1.3.1</span></a>) the <code>elev.tif</code> raster values into an array named <code>elev</code> (<a href="#fig-raster-subset" class="quarto-xref">Figure&nbsp;<span>3.17</span></a> (a)).</p>
-<div id="5842f1c0" class="cell" data-execution_count="62">
+<div id="6d8aa748" class="cell" data-execution_count="62">
 <div class="sourceCode cell-code" id="cb74"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb74-1"><a href="#cb74-1" aria-hidden="true" tabindex="-1"></a>elev <span class="op">=</span> src_elev.read(<span class="dv">1</span>)</span>
 <span id="cb74-2"><a href="#cb74-2" aria-hidden="true" tabindex="-1"></a>elev</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="62">
@@ -2638,7 +2638,7 @@ <h3 data-number="3.3.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </div>
 </div>
 <p>and create a corresponding random boolean mask named <code>mask</code> (<a href="#fig-raster-subset" class="quarto-xref">Figure&nbsp;<span>3.17</span></a> (b)), of the same shape as <code>elev.tif</code> with values randomly assigned to <code>True</code> and <code>False</code>.</p>
-<div id="25a7df80" class="cell" data-execution_count="63">
+<div id="a784a90b" class="cell" data-execution_count="63">
 <div class="sourceCode cell-code" id="cb76"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb76-1"><a href="#cb76-1" aria-hidden="true" tabindex="-1"></a>np.random.seed(<span class="dv">1</span>)</span>
 <span id="cb76-2"><a href="#cb76-2" aria-hidden="true" tabindex="-1"></a>mask <span class="op">=</span> np.random.choice([<span class="va">True</span>, <span class="va">False</span>], src_elev.shape)</span>
 <span id="cb76-3"><a href="#cb76-3" aria-hidden="true" tabindex="-1"></a>mask</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2652,7 +2652,7 @@ <h3 data-number="3.3.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </div>
 </div>
 <p>Next, suppose that we want to keep only those values of <code>elev</code> which are <code>False</code> in <code>mask</code> (i.e., they are <em>not</em> masked). In other words, we want to mask <code>elev</code> with <code>mask</code>. The result will be stored in a copy named <code>masked_elev</code> (<a href="#fig-raster-subset" class="quarto-xref">Figure&nbsp;<span>3.17</span></a> (c)). In the case of <code>elev.tif</code>, to be able to store <code>np.nan</code> in the array of values, we also need to convert it to <code>float</code> (see <a href="02-attribute-operations.html#sec-summarizing-raster-objects" class="quarto-xref"><span>Section 2.3.2</span></a>). Afterwards, masking is a matter of assigning <code>np.nan</code> into a subset defined by the mask, using the ‘boolean array indexing’ syntax of <strong>numpy</strong>.</p>
-<div id="4282ac8a" class="cell" data-execution_count="64">
+<div id="97164e64" class="cell" data-execution_count="64">
 <div class="sourceCode cell-code" id="cb78"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb78-1"><a href="#cb78-1" aria-hidden="true" tabindex="-1"></a>masked_elev <span class="op">=</span> elev.copy()</span>
 <span id="cb78-2"><a href="#cb78-2" aria-hidden="true" tabindex="-1"></a>masked_elev <span class="op">=</span> masked_elev.astype(<span class="st">'float64'</span>)</span>
 <span id="cb78-3"><a href="#cb78-3" aria-hidden="true" tabindex="-1"></a>masked_elev[mask] <span class="op">=</span> np.nan</span>
@@ -2718,7 +2718,7 @@ <h3 data-number="3.3.1" class="anchored" data-anchor-id="sec-spatial-subsetting-
 </figure>
 </div>
 <p>The mask can be created from the array itself, using condition(s). That way, we can replace some values (e.g., values assumed to be wrong) with <code>np.nan</code>, such as in the following example.</p>
-<div id="ea87b29d" class="cell" data-execution_count="66">
+<div id="4fc8cbde" class="cell" data-execution_count="66">
 <div class="sourceCode cell-code" id="cb81"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb81-1"><a href="#cb81-1" aria-hidden="true" tabindex="-1"></a>elev2 <span class="op">=</span> elev.copy()</span>
 <span id="cb81-2"><a href="#cb81-2" aria-hidden="true" tabindex="-1"></a>elev2 <span class="op">=</span> elev2.astype(<span class="st">'float64'</span>)</span>
 <span id="cb81-3"><a href="#cb81-3" aria-hidden="true" tabindex="-1"></a>elev2[elev2 <span class="op">&lt;</span> <span class="dv">20</span>] <span class="op">=</span> np.nan</span>
@@ -2751,7 +2751,7 @@ <h3 data-number="3.3.2" class="anchored" data-anchor-id="sec-map-algebra"><span
 <h3 data-number="3.3.3" class="anchored" data-anchor-id="sec-raster-local-operations"><span class="header-section-number">3.3.3</span> Local operations</h3>
 <p>Local operations comprise all cell-by-cell operations in one or several layers. Raster algebra is a classical use case of local operations—this includes adding or subtracting values from a raster, squaring,, and multiplying rasters. Raster algebra also allows logical operations such as finding all raster cells that are greater than a specific value (e.g., <code>5</code> in our example below). Local operations are applied using the <strong>numpy</strong> array operations syntax, as demonstrated below.</p>
 <p>First, let’s take the array of <code>elev.tif</code> raster values, which we already read earlier (<a href="#sec-spatial-subsetting-raster" class="quarto-xref"><span>Section 3.3.1</span></a>).</p>
-<div id="b56e4126" class="cell" data-execution_count="67">
+<div id="85ae45b0" class="cell" data-execution_count="67">
 <div class="sourceCode cell-code" id="cb83"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb83-1"><a href="#cb83-1" aria-hidden="true" tabindex="-1"></a>elev</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="67">
 <pre><code>array([[ 1,  2,  3,  4,  5,  6],
@@ -2763,7 +2763,7 @@ <h3 data-number="3.3.3" class="anchored" data-anchor-id="sec-raster-local-operat
 </div>
 </div>
 <p>Now, any element-wise array operation can be applied using <strong>numpy</strong> arithmetic or conditional operators and functions, comprising local raster operations in spatial analysis terminology. For example, <code>elev + elev</code> adds the values of <code>elev</code> to itself, resulting in a raster with double values.</p>
-<div id="5351037d" class="cell" data-execution_count="68">
+<div id="a04fc5ee" class="cell" data-execution_count="68">
 <div class="sourceCode cell-code" id="cb85"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb85-1"><a href="#cb85-1" aria-hidden="true" tabindex="-1"></a>elev <span class="op">+</span> elev</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="68">
 <pre><code>array([[ 2,  4,  6,  8, 10, 12],
@@ -2775,7 +2775,7 @@ <h3 data-number="3.3.3" class="anchored" data-anchor-id="sec-raster-local-operat
 </div>
 </div>
 <p>Note that some functions and operators automatically change the data type to accommodate the resulting values, while other operators do not, potentially resulting in overflow (i.e., incorrect values for results beyond the data type range, such as trying to accommodate values above <code>255</code> in an <code>int8</code> array). For example, <code>elev**2</code> (<code>elev</code> squared) results in overflow. Since the <code>**</code> operator does not automatically change the data type, leaving it as <code>int8</code>, the resulting array has incorrect values for <code>16**2</code>, <code>17**2</code>, etc., which are above <code>255</code> and therefore cannot be accommodated.</p>
-<div id="fd09ddf4" class="cell" data-execution_count="69">
+<div id="a31853a9" class="cell" data-execution_count="69">
 <div class="sourceCode cell-code" id="cb87"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb87-1"><a href="#cb87-1" aria-hidden="true" tabindex="-1"></a>elev<span class="op">**</span><span class="dv">2</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="69">
 <pre><code>array([[  1,   4,   9,  16,  25,  36],
@@ -2787,7 +2787,7 @@ <h3 data-number="3.3.3" class="anchored" data-anchor-id="sec-raster-local-operat
 </div>
 </div>
 <p>To avoid this situation, we can, for instance, transform <code>elev</code> to the standard <code>int64</code> data type, using <code>.astype</code> before applying the <code>**</code> operator. That way, all results, up to <code>36**2</code> (<code>1296</code>), can be easily accommodated, since the <code>int64</code> data type supports values up to <code>9223372036854775807</code> (<a href="07-read-write.html#tbl-numpy-data-types" class="quarto-xref">Table&nbsp;<span>7.2</span></a>).</p>
-<div id="e10b9c70" class="cell" data-execution_count="70">
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 <div class="sourceCode cell-code" id="cb89"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb89-1"><a href="#cb89-1" aria-hidden="true" tabindex="-1"></a>elev.astype(<span class="bu">int</span>)<span class="op">**</span><span class="dv">2</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="70">
 <pre><code>array([[   1,    4,    9,   16,   25,   36],
@@ -2864,7 +2864,7 @@ <h3 data-number="3.3.3" class="anchored" data-anchor-id="sec-raster-local-operat
 </figure>
 </div>
 <p>Another good example of local operations is the classification of intervals of numeric values into groups such as grouping a digital elevation model into low (class <code>1</code>), middle (class <code>2</code>) and high (class <code>3</code>) elevations. Here, the raster values in the ranges <code>0</code>–<code>12</code>, <code>12</code>–<code>24</code>, and <code>24</code>–<code>36</code> are reclassified to take values <code>1</code>, <code>2</code>, and <code>3</code>, respectively.</p>
-<div id="66d3b0df" class="cell" data-execution_count="72">
+<div id="24cd10e2" class="cell" data-execution_count="72">
 <div class="sourceCode cell-code" id="cb92"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a>recl <span class="op">=</span> elev.copy()</span>
 <span id="cb92-2"><a href="#cb92-2" aria-hidden="true" tabindex="-1"></a>recl[(elev <span class="op">&gt;</span> <span class="dv">0</span>)  <span class="op">&amp;</span> (elev <span class="op">&lt;=</span> <span class="dv">12</span>)] <span class="op">=</span> <span class="dv">1</span></span>
 <span id="cb92-3"><a href="#cb92-3" aria-hidden="true" tabindex="-1"></a>recl[(elev <span class="op">&gt;</span> <span class="dv">12</span>) <span class="op">&amp;</span> (elev <span class="op">&lt;=</span> <span class="dv">24</span>)] <span class="op">=</span> <span class="dv">2</span></span>
@@ -2914,14 +2914,14 @@ <h3 data-number="3.3.3" class="anchored" data-anchor-id="sec-raster-local-operat
 \tag{3.1}\]</span></span></p>
 <p>, where <span class="math inline">\(NIR\)</span> is the near-infrared band and <span class="math inline">\(Red\)</span> is the red band.</p>
 <p>Let’s calculate NDVI for the multispectral Landsat satellite file (<code>landsat.tif</code>) of the Zion National Park. The file <code>landsat.tif</code> contains surface reflectance values (range <code>0</code>-<code>1</code>) in the blue, green, red, and near-infrared (NIR) bands. We start by reading the file and extracting the NIR and red bands, which are the fourth and third bands, respectively. Next, we apply the formula to calculate the NDVI values.</p>
-<div id="6d7bd501" class="cell" data-execution_count="74">
+<div id="2cdd65dc" class="cell" data-execution_count="74">
 <div class="sourceCode cell-code" id="cb94"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb94-1"><a href="#cb94-1" aria-hidden="true" tabindex="-1"></a>landsat <span class="op">=</span> src_landsat.read()</span>
 <span id="cb94-2"><a href="#cb94-2" aria-hidden="true" tabindex="-1"></a>nir <span class="op">=</span> landsat[<span class="dv">3</span>]</span>
 <span id="cb94-3"><a href="#cb94-3" aria-hidden="true" tabindex="-1"></a>red <span class="op">=</span> landsat[<span class="dv">2</span>]</span>
 <span id="cb94-4"><a href="#cb94-4" aria-hidden="true" tabindex="-1"></a>ndvi <span class="op">=</span> (nir<span class="op">-</span>red)<span class="op">/</span>(nir<span class="op">+</span>red)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>When plotting an RGB image using the <code>rasterio.plot.show</code> function, the function assumes that values are in the range <code>[0,1]</code> for floats, or <code>[0,255]</code> for integers (otherwise clipped) and the order of bands is RGB. To prepare the multi-band raster for <code>rasterio.plot.show</code>, we, therefore, reverse the order of the first three bands (to go from B-G-R-NIR to R-G-B), using the <code>[:3]</code> slice to select the first three bands and then the <code>[::-1]</code> slice to reverse the bands order, and divide by the raster maximum to set the maximum value to <code>1</code>.</p>
-<div id="d74c6a7b" class="cell" data-execution_count="75">
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 <div class="sourceCode cell-code" id="cb95"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb95-1"><a href="#cb95-1" aria-hidden="true" tabindex="-1"></a>landsat_rgb <span class="op">=</span> landsat[:<span class="dv">3</span>][::<span class="op">-</span><span class="dv">1</span>] <span class="op">/</span> landsat.<span class="bu">max</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="callout callout-style-default callout-note callout-titled">
@@ -3004,7 +3004,7 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sec-focal-operations"><
 <p>In this group of functions, we define the shape of the moving window with either one of <code>size</code>—a single number (e.g., <code>3</code>), or tuple (e.g., <code>(3,3)</code>), implying a filter of those dimensions, or <code>footprint</code>—a boolean array, representing both the window shape and the identity of elements being included.</p>
 <p>In addition to specific built-in filters, <code>convolve</code>—applies the sum function after multiplying by a custom <code>weights</code> array, and <code>generic_filter</code>—makes it possible to pass any custom function, where the user can specify any type of custom window-based calculation.</p>
 <p>For example, here we apply the minimum filter with window size of <code>3</code> on <code>elev</code>. As a result, we now have a new array <code>elev_min</code>, where each value is the minimum in the corresponding <span class="math inline">\(3 \times 3\)</span> neighborhood in <code>elev</code>.</p>
-<div id="0ffd9cbe" class="cell" data-execution_count="77">
+<div id="77cc658b" class="cell" data-execution_count="77">
 <div class="sourceCode cell-code" id="cb97"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb97-1"><a href="#cb97-1" aria-hidden="true" tabindex="-1"></a>elev_min <span class="op">=</span> scipy.ndimage.minimum_filter(elev, size<span class="op">=</span><span class="dv">3</span>)</span>
 <span id="cb97-2"><a href="#cb97-2" aria-hidden="true" tabindex="-1"></a>elev_min</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="77">
@@ -3017,7 +3017,7 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sec-focal-operations"><
 </div>
 </div>
 <p>Special care should be given to the edge pixels – how should they be calculated? The <strong>scipy.ndimage</strong> filtering functions give several options through the <code>mode</code> parameter (see the documentation of any filtering function, such as <code>scipy.ndimage.median_filter</code>, for the definition of each mode): <code>reflect</code> (the default), <code>constant</code>, <code>nearest</code>, <code>mirror</code>, <code>wrap</code>. Sometimes artificially extending raster edges is considered unsuitable. In other words, we may wish the resulting raster to contain pixel values with ‘complete’ windows only, for example, to have a uniform sample size or because values in all directions matter (such as in topographic calculations). There is no specific option <em>not</em> to extend edges in <strong>scipy.ndimage</strong>. However, to get the same effect, the edges of the filtered array can be assigned with <code>np.nan</code>, in a number of rows and columns according to filter size. For example, when using a filter of <code>size=3</code>, the outermost ‘layer’ of pixels may be assigned with <code>np.nan</code>, reflecting the fact that these pixels have incomplete <span class="math inline">\(3 \times 3\)</span> neighborhoods (<a href="#fig-focal-filter" class="quarto-xref">Figure&nbsp;<span>3.21</span></a>):</p>
-<div id="8ef1b8e7" class="cell" data-execution_count="78">
+<div id="29a246a4" class="cell" data-execution_count="78">
 <div class="sourceCode cell-code" id="cb99"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb99-1"><a href="#cb99-1" aria-hidden="true" tabindex="-1"></a>elev_min <span class="op">=</span> elev_min.astype(<span class="bu">float</span>)</span>
 <span id="cb99-2"><a href="#cb99-2" aria-hidden="true" tabindex="-1"></a>elev_min[:, [<span class="dv">0</span>, <span class="op">-</span><span class="dv">1</span>]] <span class="op">=</span> np.nan</span>
 <span id="cb99-3"><a href="#cb99-3" aria-hidden="true" tabindex="-1"></a>elev_min[[<span class="dv">0</span>, <span class="op">-</span><span class="dv">1</span>], :] <span class="op">=</span> np.nan</span>
@@ -3034,7 +3034,7 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sec-focal-operations"><
 <p>We can quickly check if the output meets our expectations. In our example, the minimum value has to be always the upper left corner of the moving window (remember we have created the input raster by row-wise incrementing the cell values by one starting at the upper left corner).</p>
 <p>Focal functions or filters play a dominant role in image processing. For example, low-pass or smoothing filters use the mean function to remove extremes. By contrast, high-pass filters, often created with custom neighborhood weights, accentuate features.</p>
 <p>In the case of categorical data, we can replace the mean with the mode, i.e., the most common value. To demonstrate applying a mode filter, let’s read the small sample categorical raster <code>grain.tif</code>.</p>
-<div id="8b5b9b69" class="cell" data-execution_count="79">
+<div id="3552b2e9" class="cell" data-execution_count="79">
 <div class="sourceCode cell-code" id="cb101"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb101-1"><a href="#cb101-1" aria-hidden="true" tabindex="-1"></a>grain <span class="op">=</span> src_grain.read(<span class="dv">1</span>)</span>
 <span id="cb101-2"><a href="#cb101-2" aria-hidden="true" tabindex="-1"></a>grain</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="79">
@@ -3047,7 +3047,7 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sec-focal-operations"><
 </div>
 </div>
 <p>There is no built-in filter function for a mode filter in <strong>scipy.ndimage</strong>, but we can use the <code>scipy.ndimage.generic_filter</code> function along with a custom filtering function, internally utilizing <code>scipy.stats.mode</code>.</p>
-<div id="81c5a620" class="cell" data-execution_count="80">
+<div id="bbc3c20c" class="cell" data-execution_count="80">
 <div class="sourceCode cell-code" id="cb103"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb103-1"><a href="#cb103-1" aria-hidden="true" tabindex="-1"></a>grain_mode <span class="op">=</span> scipy.ndimage.generic_filter(</span>
 <span id="cb103-2"><a href="#cb103-2" aria-hidden="true" tabindex="-1"></a>    grain, </span>
 <span id="cb103-3"><a href="#cb103-3" aria-hidden="true" tabindex="-1"></a>    <span class="kw">lambda</span> x: scipy.stats.mode(x.flatten())[<span class="dv">0</span>], </span>
@@ -3121,13 +3121,13 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sec-focal-operations"><
 </div>
 </div>
 <p>GDAL, along with all of its programs should be available in your Python environment, since GDAL is a dependency of <strong>rasterio</strong>. The following example, which should be run from the command line, takes the <code>srtm_32612.tif</code> raster (which we are going to create in <a href="06-reproj.html#sec-reprojecting-raster-geometries" class="quarto-xref"><span>Section 6.8</span></a>, therefore it is in the <code>'output'</code> directory), calculates slope (in decimal degrees, between <code>0</code> and <code>90</code>), and exports the result to a new file <code>srtm_32612_slope.tif</code>. Note that the arguments of <code>gdaldem</code> are the metric name (<code>slope</code>), then the input file path, and finally the output file path.</p>
-<div id="70174241" class="cell" data-execution_count="81">
+<div id="9fa78471" class="cell" data-execution_count="81">
 <div class="sourceCode cell-code" id="cb105"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb105-1"><a href="#cb105-1" aria-hidden="true" tabindex="-1"></a>os.system(<span class="st">'gdaldem slope output/srtm_32612.tif output/srtm_32612_slope.tif'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Here we ran the <code>gdaldem</code> command through <code>os.system</code>, in order to remain in the Python environment, even though we are calling an external program. Alternatively, you can run the standalone command in the command line interface you are using, such as the Anaconda Prompt:</p>
 <pre class="{sh}"><code>gdaldem slope output/srtm_32612.tif output/srtm_32612_slope.tif</code></pre>
 <p>Replacing the metric name, we can calculate other terrain properties. For example, here is how we can calculate an aspect raster <code>srtm_32612_aspect.tif</code>, also in degrees (between <code>0</code> and <code>360</code>).</p>
-<div id="ccdb5bc1" class="cell" data-execution_count="82">
+<div id="794aae03" class="cell" data-execution_count="82">
 <div class="sourceCode cell-code" id="cb107"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb107-1"><a href="#cb107-1" aria-hidden="true" tabindex="-1"></a>os.system(<span class="st">'gdaldem aspect output/srtm_32612.tif output/srtm_32612_aspect.tif'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p><a href="#fig-raster-slope" class="quarto-xref">Figure&nbsp;<span>3.22</span></a> shows the results, using our more familiar plotting methods from <strong>rasterio</strong>. The code section is relatively long due to the workaround to create a color key (see <a href="08-mapping.html#sec-plot-symbology" class="quarto-xref"><span>Section 8.2.3</span></a>) and removing ‘No Data’ flag values from the arrays so that the color key does not include them. Also note that we are using one of <strong>matplotlib</strong>’s the cyclic color scales (<code>'twilight'</code>) when plotting aspect (<a href="#fig-raster-slope" class="quarto-xref">Figure&nbsp;<span>3.22</span></a> (c)).</p>
@@ -3204,14 +3204,14 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sec-focal-operations"><
 <h3 data-number="3.3.5" class="anchored" data-anchor-id="sec-zonal-operations"><span class="header-section-number">3.3.5</span> Zonal operations</h3>
 <p>Just like focal operations, zonal operations apply an aggregation function to multiple raster cells. However, a second raster, usually with categorical values, defines the zonal filters (or ‘zones’) in the case of zonal operations, as opposed to a predefined neighborhood window in the case of focal operation presented in the previous section. Consequently, raster cells defining the zonal filter do not necessarily have to be neighbors. Our <code>grain.tif</code> raster is a good example, as illustrated in <a href="01-spatial-data.html#fig-rasterio-plot-grain" class="quarto-xref">Figure&nbsp;<span>1.24</span></a>: different grain sizes are spread irregularly throughout the raster. Finally, the result of a zonal operation is a summary table grouped by zone, which is why this operation is also known as zonal statistics in the GIS world. This is in contrast to focal operations (<a href="#sec-focal-operations" class="quarto-xref"><span>Section 3.3.4</span></a>) which return a raster object.</p>
 <p>To demonstrate, let’s get back to the <code>grain.tif</code> and <code>elev.tif</code> rasters. To calculate zonal statistics, we use the arrays with raster values, which we already imported earlier. Our intention is to calculate the average (or any other summary function, for that matter) of <em>elevation</em> in each zone defined by <em>grain</em> values. To do that, first we first obtain the unique values defining the zones using <code>np.unique</code>.</p>
-<div id="18e87699" class="cell" data-execution_count="84">
+<div id="c3bf216c" class="cell" data-execution_count="84">
 <div class="sourceCode cell-code" id="cb109"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb109-1"><a href="#cb109-1" aria-hidden="true" tabindex="-1"></a>np.unique(grain)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="82">
 <pre><code>array([0, 1, 2], dtype=uint8)</code></pre>
 </div>
 </div>
 <p>Now, we can use dictionary comprehension (see note below) to split the <code>elev</code> array into separate one-dimensional arrays with values per <code>grain</code> group, with keys being the unique <code>grain</code> values.</p>
-<div id="dd9a992d" class="cell" data-execution_count="85">
+<div id="114d25fc" class="cell" data-execution_count="85">
 <div class="sourceCode cell-code" id="cb111"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb111-1"><a href="#cb111-1" aria-hidden="true" tabindex="-1"></a>z <span class="op">=</span> {i: elev[grain <span class="op">==</span> i] <span class="cf">for</span> i <span class="kw">in</span> np.unique(grain)}</span>
 <span id="cb111-2"><a href="#cb111-2" aria-hidden="true" tabindex="-1"></a>z</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="83">
@@ -3239,7 +3239,7 @@ <h3 data-number="3.3.5" class="anchored" data-anchor-id="sec-zonal-operations"><
 </div>
 </div>
 <p>At this stage, we can expand the dictionary comprehension expression to calculate the mean elevation associated with each grain size class. Namely, instead of placing the elevation values (<code>elev[grain==i]</code>) into the dictionary values, we place their (rounded) mean (<code>elev[grain==i].mean().round(1)</code>).</p>
-<div id="7d61cd4a" class="cell" data-execution_count="86">
+<div id="a4a3227c" class="cell" data-execution_count="86">
 <div class="sourceCode cell-code" id="cb113"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb113-1"><a href="#cb113-1" aria-hidden="true" tabindex="-1"></a>z <span class="op">=</span> {i: elev[grain <span class="op">==</span> i].mean().<span class="bu">round</span>(<span class="dv">1</span>) <span class="cf">for</span> i <span class="kw">in</span> np.unique(grain)}</span>
 <span id="cb113-2"><a href="#cb113-2" aria-hidden="true" tabindex="-1"></a>z</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="84">
@@ -3263,7 +3263,7 @@ <h3 data-number="3.3.7" class="anchored" data-anchor-id="map-algebra-counterpart
 <h3 data-number="3.3.8" class="anchored" data-anchor-id="sec-merging-rasters"><span class="header-section-number">3.3.8</span> Merging rasters</h3>
 <p>Suppose we would like to compute the NDVI (see <a href="#sec-raster-local-operations" class="quarto-xref"><span>Section 3.3.3</span></a>), and additionally want to compute terrain attributes from elevation data for observations within a study area. Such computations rely on remotely sensed information. The corresponding source imagery is often divided into scenes covering a specific spatial extent (i.e., tiles), and frequently, a study area covers more than one scene. Then, we would need to merge (also known as mosaic) the scenes covering our study area. In case when all scenes are aligned (i.e., share the same origin and resolution), this can be thought of as simply gluing them into one big raster; otherwise, all scenes need to be resampled (see <a href="04-geometry-operations.html#sec-raster-resampling" class="quarto-xref"><span>Section 4.3.3</span></a>) to the same grid (e.g., the one defined by the first scene).</p>
 <p>For example, let’s merge digital elevation data from two SRTM elevation tiles, for Austria (<code>'aut.tif'</code>) and Switzerland (<code>'ch.tif'</code>). Merging can be done using function <code>rasterio.merge.merge</code>, which accepts a <code>list</code> of raster file connections, and returns the new <code>ndarray</code> and the corresponding transform object, representing the resulting mosaic.</p>
-<div id="7ae33dc5" class="cell" data-execution_count="87">
+<div id="247f8280" class="cell" data-execution_count="87">
 <div class="sourceCode cell-code" id="cb115"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb115-1"><a href="#cb115-1" aria-hidden="true" tabindex="-1"></a>src_1 <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/aut.tif'</span>)</span>
 <span id="cb115-2"><a href="#cb115-2" aria-hidden="true" tabindex="-1"></a>src_2 <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/ch.tif'</span>)</span>
 <span id="cb115-3"><a href="#cb115-3" aria-hidden="true" tabindex="-1"></a>out_image, out_transform <span class="op">=</span> rasterio.merge.merge([src_1, src_2])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
diff --git a/04-geometry-operations.html b/04-geometry-operations.html
index 60c5f682..c6d5abc1 100644
--- a/04-geometry-operations.html
+++ b/04-geometry-operations.html
@@ -361,7 +361,7 @@ <h1 class="title"><span id="sec-geometric-operations" class="quarto-section-iden
 <section id="prerequisites" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2>
 <p>This chapter requires importing the following packages:</p>
-<div id="f9da7e61" class="cell" data-execution_count="3">
+<div id="7ff530c2" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sys</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
@@ -375,7 +375,7 @@ <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2
 <span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio.mask</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>It also relies on the following data files:</p>
-<div id="4e5dcbae" class="cell" data-execution_count="4">
+<div id="b33e30bb" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>seine <span class="op">=</span> gpd.read_file(<span class="st">'data/seine.gpkg'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>us_states <span class="op">=</span> gpd.read_file(<span class="st">'data/us_states.gpkg'</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>nz <span class="op">=</span> gpd.read_file(<span class="st">'data/nz.gpkg'</span>)</span>
@@ -436,7 +436,7 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="sec-simplification"><sp
 </figure>
 </div>
 <p>The resulting <code>seine_simp</code> object is a copy of the original <code>seine</code> but with fewer vertices. This is apparent, with the result being visually simpler (<a href="#fig-simplify-lines" class="quarto-xref">Figure&nbsp;<span>4.1</span></a>, right) and consuming about twice less memory than the original object, as shown in the comparison below.</p>
-<div id="95446110" class="cell" data-execution_count="6">
+<div id="1a2da311" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f'Original: </span><span class="sc">{</span>sys<span class="sc">.</span>getsizeof(seine)<span class="sc">}</span><span class="ss"> bytes'</span>)</span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f'Simplified: </span><span class="sc">{</span>sys<span class="sc">.</span>getsizeof(seine_simp)<span class="sc">}</span><span class="ss"> bytes'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -445,15 +445,15 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="sec-simplification"><sp
 </div>
 </div>
 <p>Simplification is also applicable for polygons. This is illustrated using <code>us_states</code>, representing the contiguous United States. As we show in <a href="06-reproj.html" class="quarto-xref"><span>Chapter 6</span></a>, for many calculations <strong>geopandas</strong> (through <strong>shapely</strong>, and, ultimately, GEOS) assumes that the data is in a projected CRS and this could lead to unexpected results when applying distance-related operators. Therefore, the first step is to project the data into some adequate projected CRS, such as US National Atlas Equal Area (EPSG:<code>9311</code>) (on the left in Figure <a href="#fig-simplify-polygons" class="quarto-xref">Figure&nbsp;<span>4.2</span></a>), using <code>.to_crs</code> (<a href="06-reproj.html#sec-reprojecting-vector-geometries" class="quarto-xref"><span>Section 6.7</span></a>).</p>
-<div id="bdad711a" class="cell" data-execution_count="7">
+<div id="fbb87dd9" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>us_states9311 <span class="op">=</span> us_states.to_crs(<span class="dv">9311</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The <code>.simplify</code> method from <strong>geopandas</strong> works the same way with a <code>'Polygon'</code>/<code>'MultiPolygon'</code> layer such as <code>us_states9311</code>:</p>
-<div id="906145b5" class="cell" data-execution_count="8">
+<div id="e374103e" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>us_states_simp1 <span class="op">=</span> us_states9311.simplify(<span class="dv">100000</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>A limitation with <code>.simplify</code>, however, is that it simplifies objects on a per-geometry basis. This means the topology is lost, resulting in overlapping and ‘holey’ areal units as illustrated in <a href="#fig-simplify-polygons" class="quarto-xref">Figure&nbsp;<span>4.2</span></a> (b). The <code>.toposimplify</code> method from package <strong>topojson</strong> provides an alternative that overcomes this issue. The main advanatage of <code>.toposimplify</code> is that it is topologically ‘aware’: it simplifies the combined borders of the polygons (rather than each polygon on its own), thus ensuring that the overlap is maintained. The following code chunk uses <code>.toposimplify</code> to simplify <code>us_states9311</code>. Note that, when using the <strong>topojson</strong> package, we first need to calculate a topology object, using function <code>tp.Topology</code>, and then apply the simplification function, such as <code>.toposimplify</code>, to obtain a simplified layer. We are also using the <code>.to_gdf</code> method to return a <code>GeoDataFrame</code>.</p>
-<div id="0a546cb7" class="cell" data-execution_count="9">
+<div id="2f104bb0" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>topo <span class="op">=</span> tp.Topology(us_states9311, prequantize<span class="op">=</span><span class="va">False</span>)</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>us_states_simp2 <span class="op">=</span> topo.toposimplify(<span class="dv">100000</span>).to_gdf()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -513,12 +513,12 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="sec-simplification"><sp
 <h3 data-number="4.2.2" class="anchored" data-anchor-id="sec-centroids"><span class="header-section-number">4.2.2</span> Centroids</h3>
 <p>Centroid operations identify the center of geographic objects. Like statistical measures of central tendency (including mean and median definitions of ‘average’), there are many ways to define the geographic center of an object. All of them create single-point representations of more complex vector objects.</p>
 <p>The most commonly used centroid operation is the geographic centroid. This type of centroid operation (often referred to as ‘the centroid’) represents the center of mass in a spatial object (think of balancing a plate on your finger). Geographic centroids have many uses, for example to create a simple point representation of complex geometries, to estimate distances between polygons, or to specify the location where polygon text labels are placed. Centroids of the geometries in a <code>GeoSeries</code> or a <code>GeoDataFrame</code> are accessible through the <code>.centroid</code> property, as demonstrated in the code below, which generates the geographic centroids of regions in New Zealand and tributaries to the River Seine (black points in <a href="#fig-centroid-pnt-on-surface" class="quarto-xref">Figure&nbsp;<span>4.3</span></a>).</p>
-<div id="37051df7" class="cell" data-execution_count="11">
+<div id="536559e8" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>nz_centroid <span class="op">=</span> nz.centroid</span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>seine_centroid <span class="op">=</span> seine.centroid</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Sometimes the geographic centroid falls outside the boundaries of their parent objects (think of vector data in shape of a doughnut). In such cases ‘point on surface’ operations, created with the <code>.representative_point</code> method, can be used to guarantee the point will be in the parent object (e.g., for labeling irregular multipolygon objects such as island states), as illustrated by the red points in <a href="#fig-centroid-pnt-on-surface" class="quarto-xref">Figure&nbsp;<span>4.3</span></a>. Notice that these red points always lie on their parent objects.</p>
-<div id="e8524bf1" class="cell" data-execution_count="12">
+<div id="477b0a5d" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>nz_pos <span class="op">=</span> nz.representative_point()</span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>seine_pos <span class="op">=</span> seine.representative_point()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -571,7 +571,7 @@ <h3 data-number="4.2.2" class="anchored" data-anchor-id="sec-centroids"><span cl
 <h3 data-number="4.2.3" class="anchored" data-anchor-id="sec-buffers"><span class="header-section-number">4.2.3</span> Buffers</h3>
 <p>Buffers are polygons representing the area within a given distance of a geometric feature: regardless of whether the input is a point, line or polygon, the output is a polygon (when using positive buffer distance). Unlike simplification, which is often used for visualization and reducing file size, buffering tends to be used for geographic data analysis. How many points are within a given distance of this line? Which demographic groups are within travel distance of this new shop? These kinds of questions can be answered and visualized by creating buffers around the geographic entities of interest.</p>
 <p><a href="#fig-buffers" class="quarto-xref">Figure&nbsp;<span>4.4</span></a> illustrates buffers of two different sizes (5 and 50 <span class="math inline">\(km\)</span>) surrounding the river Seine and tributaries. These buffers were created with commands below, using the <code>.buffer</code> method, applied to a <code>GeoSeries</code> or <code>GeoDataFrame</code>. The <code>.buffer</code> method requires one important argument: the buffer distance, provided in the units of the CRS, in this case, meters.</p>
-<div id="60e1a53d" class="cell" data-execution_count="14">
+<div id="cf068b0c" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>seine_buff_5km <span class="op">=</span> seine.<span class="bu">buffer</span>(<span class="dv">5000</span>)</span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>seine_buff_50km <span class="op">=</span> seine.<span class="bu">buffer</span>(<span class="dv">50000</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -614,7 +614,7 @@ <h3 data-number="4.2.3" class="anchored" data-anchor-id="sec-buffers"><span clas
 </figure>
 </div>
 <p>Note that both <code>.centroid</code> and <code>.buffer</code> return a <code>GeoSeries</code> object, even when the input is a <code>GeoDataFrame</code>.</p>
-<div id="7718b744" class="cell" data-execution_count="16">
+<div id="763308ac" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>seine_buff_5km</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="16">
 <pre><code>0    POLYGON ((657550.332 6852587.97...
@@ -624,7 +624,7 @@ <h3 data-number="4.2.3" class="anchored" data-anchor-id="sec-buffers"><span clas
 </div>
 </div>
 <p>In the common scenario when the original attributes of the input features need to be retained, you can replace the existing geometry with the new <code>GeoSeries</code> by creating a copy of the original <code>GeoDataFrame</code> and assigning the new buffer <code>GeoSeries</code> to the <code>geometry</code> column.</p>
-<div id="4b848907" class="cell" data-execution_count="17">
+<div id="b87dbd37" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>seine_buff_5km <span class="op">=</span> seine.copy()</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>seine_buff_5km.geometry <span class="op">=</span> seine.<span class="bu">buffer</span>(<span class="dv">5000</span>)</span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>seine_buff_5km</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -663,7 +663,7 @@ <h3 data-number="4.2.3" class="anchored" data-anchor-id="sec-buffers"><span clas
 </div>
 </div>
 <p>An alternative option is to add a secondary geometry column directly to the original <code>GeoDataFrame</code>.</p>
-<div id="adc881e6" class="cell" data-execution_count="18">
+<div id="57cb54fd" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>seine[<span class="st">'geometry_5km'</span>] <span class="op">=</span> seine.<span class="bu">buffer</span>(<span class="dv">5000</span>)</span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>seine</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="18">
@@ -705,11 +705,11 @@ <h3 data-number="4.2.3" class="anchored" data-anchor-id="sec-buffers"><span clas
 </div>
 </div>
 <p>You can then switch to either geometry column (i.e., make it ‘active’) using <code>.set_geometry</code>, as in:</p>
-<div id="95c730a1" class="cell" data-execution_count="19">
+<div id="f8363747" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>seine <span class="op">=</span> seine.set_geometry(<span class="st">'geometry_5km'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Let’s revert to the original state of <code>seine</code> before moving on to the next section.</p>
-<div id="8011071d" class="cell" data-execution_count="20">
+<div id="b3d3f37e" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>seine <span class="op">=</span> seine.set_geometry(<span class="st">'geometry'</span>)</span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>seine <span class="op">=</span> seine.drop(<span class="st">'geometry_5km'</span>, axis<span class="op">=</span><span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -732,7 +732,7 @@ <h3 data-number="4.2.4" class="anchored" data-anchor-id="sec-affine-transformati
 <li><code>.rotate(angle, origin='center', use_radians=False)</code></li>
 </ul>
 <p>For example, <em>shifting</em> only requires the <span class="math inline">\(x_{off}\)</span> and <span class="math inline">\(y_{off}\)</span>, using <code>.translate</code>. The code below shifts the y-coordinates of <code>nz</code> by 100 <span class="math inline">\(km\)</span> to the north, but leaves the x-coordinates untouched.</p>
-<div id="1a741161" class="cell" data-execution_count="21">
+<div id="4621be1c" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>nz_shift <span class="op">=</span> nz.translate(<span class="dv">0</span>, <span class="dv">100000</span>)</span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>nz_shift</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="21">
@@ -760,7 +760,7 @@ <h3 data-number="4.2.4" class="anchored" data-anchor-id="sec-affine-transformati
 </div>
 </div>
 <p>Scaling enlarges or shrinks objects by a factor, and can be applied either globally or locally. Global scaling increases or decreases all coordinates values in relation to the origin coordinates, while keeping all geometries topological relations intact. <strong>geopandas</strong> implements scaling using the <code>.scale</code> method. Local scaling treats geometries independently and requires points around which geometries are going to be scaled, e.g., centroids. In the example below, each geometry is shrunk by a factor of two around the centroids (<a href="#fig-affine-transformations" class="quarto-xref">Figure&nbsp;<span>4.5</span></a> (b)). To achieve that, we pass the <code>0.5</code> and <code>0.5</code> scaling factors (for x and y, respectively), and the <code>'centroid'</code> option for the point of origin.</p>
-<div id="59e52eb7" class="cell" data-execution_count="22">
+<div id="bab24528" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>nz_scale <span class="op">=</span> nz.scale(<span class="fl">0.5</span>, <span class="fl">0.5</span>, origin<span class="op">=</span><span class="st">'centroid'</span>)</span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>nz_scale</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
@@ -776,7 +776,7 @@ <h3 data-number="4.2.4" class="anchored" data-anchor-id="sec-affine-transformati
 </div>
 <p>When setting the <code>origin</code> in <code>.scale</code>, other than <code>'centroid'</code> it is possible to use <code>'center'</code>, for the bounding box center, or specific point coordinates, such as <code>(0,0)</code>.</p>
 <p>Rotating the geometries can be done using the <code>.rotate</code> method. When rotating, we need to specify the rotation angle (positive values imply clockwise rotation) and the <code>origin</code> points (using the same options as in <code>scale</code>). For example, the following expression rotates <code>nz</code> by <span class="math inline">\(30\degree\)</span> counter-clockwise, around the geometry centroids.</p>
-<div id="7d6da7e1" class="cell" data-execution_count="23">
+<div id="e6dc15cc" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>nz_rotate <span class="op">=</span> nz.rotate(<span class="op">-</span><span class="dv">30</span>, origin<span class="op">=</span><span class="st">'centroid'</span>)</span>
 <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>nz_rotate</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
@@ -933,7 +933,7 @@ <h3 data-number="4.2.5" class="anchored" data-anchor-id="sec-clipping"><span cla
 <p>Keep in mind that <code>x</code> and <code>y</code> are interchangeable in all predicates except for <code>.difference</code>, where <code>x.difference(y)</code> means <code>x</code> minus <code>y</code>, whereas <code>y.difference(x)</code> means <code>y</code> minus <code>x</code>.</p>
 <p>The latter examples demonstrate pairwise operations between individual <code>shapely</code> geometries. The <strong>geopandas</strong> package, as is often the case, contains wrappers of these <strong>shapely</strong> functions to be applied to multiple, or pairwise, use cases. For example, applying either of the pairwise methods on a <code>GeoSeries</code> or <code>GeoDataFrame</code>, combined with a <code>shapely</code> geometry, returns the pairwise (many-to-one) results (which is analogous to other operators, like <code>.intersects</code> or <code>.distance</code>, see <a href="03-spatial-operations.html#sec-spatial-subsetting-vector" class="quarto-xref"><span>Section 3.2.1</span></a> and <a href="03-spatial-operations.html#sec-distance-relations" class="quarto-xref"><span>Section 3.2.7</span></a>, respectively).</p>
 <p>Let’s demonstrate the ‘many-to-one’ scenario by calculating the difference between each geometry in a <code>GeoSeries</code> and a fixed <code>shapely</code> geometry. To create the latter, let’s take <code>x</code> and combine it with itself translated (<a href="#sec-affine-transformations" class="quarto-xref"><span>Section 4.2.4</span></a>) to a distance of <code>1</code> and <code>2</code> units ‘upwards’ on the y-axis.</p>
-<div id="119a14cf" class="cell" data-execution_count="30">
+<div id="34028740" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>geom1 <span class="op">=</span> gpd.GeoSeries(x)</span>
 <span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>geom2 <span class="op">=</span> geom1.translate(<span class="dv">0</span>, <span class="dv">1</span>)</span>
 <span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>geom3 <span class="op">=</span> geom1.translate(<span class="dv">0</span>, <span class="dv">2</span>)</span>
@@ -965,7 +965,7 @@ <h3 data-number="4.2.5" class="anchored" data-anchor-id="sec-clipping"><span cla
 </div>
 </div>
 <p>Now, using <code>.intersection</code> automatically applies the <strong>shapely</strong> method of the same name on each geometry in <code>geom</code>, returning a new <code>GeoSeries</code>, which we name <code>geom_inter_y</code>, with the pairwise intersections. Note the empty third geometry (can you explain the meaning of this result?).</p>
-<div id="bc5502da" class="cell" data-execution_count="32">
+<div id="af0a2c42" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>geom_inter_y <span class="op">=</span> geom.intersection(y)</span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>geom_inter_y</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="32">
@@ -997,7 +997,7 @@ <h3 data-number="4.2.5" class="anchored" data-anchor-id="sec-clipping"><span cla
 <h3 data-number="4.2.6" class="anchored" data-anchor-id="sec-subsetting-vs-clipping"><span class="header-section-number">4.2.6</span> Subsetting vs.&nbsp;clipping</h3>
 <p>In the last two chapters we have introduced two types of spatial operators: boolean, such as <code>.intersects</code> (<a href="03-spatial-operations.html#sec-spatial-subsetting-vector" class="quarto-xref"><span>Section 3.2.1</span></a>), and geometry-generating, such as <code>.intersection</code> (<a href="#sec-clipping" class="quarto-xref"><span>Section 4.2.5</span></a>). Here, we illustrate the difference between them. We do this using the specific scenario of subsetting points by polygons, where (unlike in other cases) both methods can be used for the same purpose and giving the same result.</p>
 <p>To illustrate the point, we will subset points that cover the bounding box of the circles <code>x</code> and <code>y</code> from <a href="#fig-overlapping-circles" class="quarto-xref">Figure&nbsp;<span>4.6</span></a>. Some points will be inside just one circle, some will be inside both, and some will be inside neither. The following code sections generate the sample data for this section, a simple random distribution of points within the extent of circles <code>x</code> and <code>y</code>, resulting in output illustrated in <a href="#fig-random-points" class="quarto-xref">Figure&nbsp;<span>4.13</span></a>. We create the sample points in two steps. First, we figure out the bounds where random points are to be generated.</p>
-<div id="8e168a01" class="cell" data-execution_count="34">
+<div id="d2a4d588" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb39"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>bounds <span class="op">=</span> x.union(y).bounds</span>
 <span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>bounds</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="34">
@@ -1005,7 +1005,7 @@ <h3 data-number="4.2.6" class="anchored" data-anchor-id="sec-subsetting-vs-clipp
 </div>
 </div>
 <p>Second, we use <code>np.random.uniform</code> to calculate <code>n</code> random x- and y-coordinates within the given bounds.</p>
-<div id="321a97e8" class="cell" data-execution_count="35">
+<div id="6f0889c9" class="cell" data-execution_count="35">
 <div class="sourceCode cell-code" id="cb41"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>np.random.seed(<span class="dv">1</span>)</span>
 <span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>n <span class="op">=</span> <span class="dv">10</span></span>
 <span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>coords_x <span class="op">=</span> np.random.uniform(bounds[<span class="dv">0</span>], bounds[<span class="dv">2</span>], n)</span>
@@ -1026,7 +1026,7 @@ <h3 data-number="4.2.6" class="anchored" data-anchor-id="sec-subsetting-vs-clipp
 </div>
 </div>
 <p>Third, we transform the list of coordinates into a <code>list</code> of <code>shapely</code> points, and then to a <code>GeoSeries</code>.</p>
-<div id="6666a198" class="cell" data-execution_count="36">
+<div id="36bf05b4" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>pnt <span class="op">=</span> [shapely.Point(i) <span class="cf">for</span> i <span class="kw">in</span> coords]</span>
 <span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a>pnt <span class="op">=</span> gpd.GeoSeries(pnt)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -1049,7 +1049,7 @@ <h3 data-number="4.2.6" class="anchored" data-anchor-id="sec-subsetting-vs-clipp
 </div>
 </div>
 <p>Now, we can get back to our question: how to subset the points to only return the point that intersects with both <code>x</code> and <code>y</code>? The code chunks below demonstrate two ways to achieve the same result. In the first approach, we can calculate a boolean <code>Series</code>, evaluating whether each point of <code>pnt</code> intersects with the intersection of <code>x</code> and <code>y</code> (see <a href="03-spatial-operations.html#sec-spatial-subsetting-vector" class="quarto-xref"><span>Section 3.2.1</span></a>), and then use it to subset <code>pnt</code> to get the result <code>pnt1</code>.</p>
-<div id="f87ac9ba" class="cell" data-execution_count="38">
+<div id="6d4a7b17" class="cell" data-execution_count="38">
 <div class="sourceCode cell-code" id="cb45"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>sel <span class="op">=</span> pnt.intersects(x.intersection(y))</span>
 <span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a>pnt1 <span class="op">=</span> pnt[sel]</span>
 <span id="cb45-3"><a href="#cb45-3" aria-hidden="true" tabindex="-1"></a>pnt1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1061,7 +1061,7 @@ <h3 data-number="4.2.6" class="anchored" data-anchor-id="sec-subsetting-vs-clipp
 </div>
 </div>
 <p>In the second approach, we can also find the intersection between the input points represented by <code>pnt</code>, using the intersection of <code>x</code> and <code>y</code> as the subsetting/clipping object. Since the second argument is an individual <code>shapely</code> geometry (<code>x.intersection(y)</code>), we get ‘pairwise’ intersections of each <code>pnt</code> with it (see <a href="#sec-clipping" class="quarto-xref"><span>Section 4.2.5</span></a>):</p>
-<div id="f20b3c81" class="cell" data-execution_count="39">
+<div id="64fe7517" class="cell" data-execution_count="39">
 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a>pnt2 <span class="op">=</span> pnt.intersection(x.intersection(y))</span>
 <span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a>pnt2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="39">
@@ -1095,7 +1095,7 @@ <h3 data-number="4.2.6" class="anchored" data-anchor-id="sec-subsetting-vs-clipp
 </div>
 </div>
 <p>The only difference between the two approaches is that <code>.intersection</code> returns all intersections, even if they are empty. When these are filtered out, <code>pnt2</code> becomes identical to <code>pnt1</code>:</p>
-<div id="f29e6459" class="cell" data-execution_count="41">
+<div id="64871af5" class="cell" data-execution_count="41">
 <div class="sourceCode cell-code" id="cb50"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a>pnt2 <span class="op">=</span> pnt2[<span class="op">~</span>pnt2.is_empty]</span>
 <span id="cb50-2"><a href="#cb50-2" aria-hidden="true" tabindex="-1"></a>pnt2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="41">
@@ -1110,7 +1110,7 @@ <h3 data-number="4.2.6" class="anchored" data-anchor-id="sec-subsetting-vs-clipp
 <section id="sec-geometry-unions" class="level3" data-number="4.2.7">
 <h3 data-number="4.2.7" class="anchored" data-anchor-id="sec-geometry-unions"><span class="header-section-number">4.2.7</span> Geometry unions</h3>
 <p>Spatial aggregation can silently dissolve the geometries of touching polygons in the same group, as we saw in <a href="02-attribute-operations.html#sec-vector-attribute-aggregation" class="quarto-xref"><span>Section 2.2.2</span></a>. This is demonstrated in the code chunk below, in which 49 <code>us_states</code> are aggregated into 4 regions using the <code>.dissolve</code> method.</p>
-<div id="5c15d679" class="cell" data-execution_count="42">
+<div id="5c89e1f9" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb52"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a>regions <span class="op">=</span> us_states[[<span class="st">'REGION'</span>, <span class="st">'geometry'</span>, <span class="st">'total_pop_15'</span>]] <span class="op">\</span></span>
 <span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a>    .dissolve(by<span class="op">=</span><span class="st">'REGION'</span>, aggfunc<span class="op">=</span><span class="st">'sum'</span>).reset_index()</span>
 <span id="cb52-3"><a href="#cb52-3" aria-hidden="true" tabindex="-1"></a>regions</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1219,7 +1219,7 @@ <h3 data-number="4.2.7" class="anchored" data-anchor-id="sec-geometry-unions"><s
 </div>
 </div>
 <p>To dissolve two (or more) groups of a <code>GeoDataFrame</code> into one geometry, we can either (a) use a combined condition or (b) concatenate the two separate subsets and then dissolve using <code>.union_all</code>.</p>
-<div id="73dab6a3" class="cell" data-execution_count="45">
+<div id="e397dbbe" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb55"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Approach 1</span></span>
 <span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a>sel <span class="op">=</span> (us_states[<span class="st">'REGION'</span>] <span class="op">==</span> <span class="st">'West'</span>) <span class="op">|</span> (us_states[<span class="st">'NAME'</span>] <span class="op">==</span> <span class="st">'Texas'</span>)</span>
 <span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a>texas_union <span class="op">=</span> us_states[sel]</span>
@@ -1481,7 +1481,7 @@ <h3 data-number="4.2.8" class="anchored" data-anchor-id="sec-type-transformation
 </div>
 </div>
 <p>Let’s place it into a <code>GeoSeries</code>.</p>
-<div id="d292947d" class="cell" data-execution_count="57">
+<div id="97a75350" class="cell" data-execution_count="57">
 <div class="sourceCode cell-code" id="cb67"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>geom <span class="op">=</span> gpd.GeoSeries(ml)</span>
 <span id="cb67-2"><a href="#cb67-2" aria-hidden="true" tabindex="-1"></a>geom</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="57">
@@ -1490,7 +1490,7 @@ <h3 data-number="4.2.8" class="anchored" data-anchor-id="sec-type-transformation
 </div>
 </div>
 <p>Then, put it into a <code>GeoDataFrame</code> with an attribute called <code>'id'</code>:</p>
-<div id="b16b6a5a" class="cell" data-execution_count="58">
+<div id="b5bb779c" class="cell" data-execution_count="58">
 <div class="sourceCode cell-code" id="cb69"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a>dat <span class="op">=</span> gpd.GeoDataFrame(geometry<span class="op">=</span>geom, data<span class="op">=</span>pd.DataFrame({<span class="st">'id'</span>: [<span class="dv">1</span>]}))</span>
 <span id="cb69-2"><a href="#cb69-2" aria-hidden="true" tabindex="-1"></a>dat</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="58">
@@ -1518,14 +1518,14 @@ <h3 data-number="4.2.8" class="anchored" data-anchor-id="sec-type-transformation
 </div>
 </div>
 <p>You can imagine it as a road or river network. The above layer <code>dat</code> has only one row that defines all the lines. This restricts the number of operations that can be done, for example, it prevents adding names to each line segment or calculating lengths of single lines. Using <strong>shapely</strong> methods with which we are already familiar with (see above), the individual single-part geometries (i.e., the ‘parts’) can be accessed through the <code>.geoms</code> property.</p>
-<div id="47af1fef" class="cell" data-execution_count="59">
+<div id="6f26c989" class="cell" data-execution_count="59">
 <div class="sourceCode cell-code" id="cb70"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb70-1"><a href="#cb70-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>(ml.geoms)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="59">
 <pre><code>[&lt;LINESTRING (1 5, 4 3)&gt;, &lt;LINESTRING (4 4, 4 1)&gt;, &lt;LINESTRING (2 2, 4 2)&gt;]</code></pre>
 </div>
 </div>
 <p>However, specifically for the ‘multi-part to single part’ type transformation scenarios, there is also a method called <code>.explode</code>, which can convert an entire multi-part <code>GeoDataFrame</code> to a single-part one. The advantage is that the original attributes (such as <code>id</code>) are retained, so that we can keep track of the original multi-part geometry properties that each part came from. The <code>index_parts=True</code> argument also lets us keep track of the original multipart geometry indices, and part indices, named <code>level_0</code> and <code>level_1</code>, respectively.</p>
-<div id="cf3623e8" class="cell" data-execution_count="60">
+<div id="6f07e332" class="cell" data-execution_count="60">
 <div class="sourceCode cell-code" id="cb72"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb72-1"><a href="#cb72-1" aria-hidden="true" tabindex="-1"></a>dat1 <span class="op">=</span> dat.explode(index_parts<span class="op">=</span><span class="va">True</span>).reset_index()</span>
 <span id="cb72-2"><a href="#cb72-2" aria-hidden="true" tabindex="-1"></a>dat1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="60">
@@ -1609,7 +1609,7 @@ <h3 data-number="4.2.8" class="anchored" data-anchor-id="sec-type-transformation
 </figure>
 </div>
 <p>As a side-note, let’s demonstrate how the above <strong>shapely</strong> casting methods can be translated to <strong>geopandas</strong>. Suppose that we want to transform <code>dat1</code>, which is a layer of type <code>'LineString'</code> with three features, to a layer of type <code>'MultiPoint'</code> (also with three features). Recall that for a single geometry, we use the expression <code>shapely.MultiPoint(x.coords)</code>, where <code>x</code> is a <code>'LineString'</code> (<a href="#fig-type-transform-multipoint2" class="quarto-xref">Figure&nbsp;<span>4.22</span></a>). When dealing with a <code>GeoDataFrame</code>, we wrap the conversion into <code>.apply</code>, to apply it to all geometries:</p>
-<div id="5a27237b" class="cell" data-execution_count="62">
+<div id="34d661d5" class="cell" data-execution_count="62">
 <div class="sourceCode cell-code" id="cb74"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb74-1"><a href="#cb74-1" aria-hidden="true" tabindex="-1"></a>dat2 <span class="op">=</span> dat1.copy()</span>
 <span id="cb74-2"><a href="#cb74-2" aria-hidden="true" tabindex="-1"></a>dat2.geometry <span class="op">=</span> dat2.geometry.<span class="bu">apply</span>(<span class="kw">lambda</span> x: shapely.MultiPoint(x.coords))</span>
 <span id="cb74-3"><a href="#cb74-3" aria-hidden="true" tabindex="-1"></a>dat2</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1694,7 +1694,7 @@ <h3 data-number="4.2.8" class="anchored" data-anchor-id="sec-type-transformation
 </figure>
 </div>
 <p>The opposite transformation, i.e., ‘single-part to multi-part’, is achieved using the <code>.dissolve</code> method (which we are already familiar with, see <a href="#sec-geometry-unions" class="quarto-xref"><span>Section 4.2.7</span></a>). For example, here is how we can get from the <code>'LineString'</code> layer with three features back to the <code>'MultiLineString'</code> layer with one feature (since, in this case, there is just one group):</p>
-<div id="8a88a1b7" class="cell" data-execution_count="64">
+<div id="4cdc0f8c" class="cell" data-execution_count="64">
 <div class="sourceCode cell-code" id="cb76"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb76-1"><a href="#cb76-1" aria-hidden="true" tabindex="-1"></a>dat1.dissolve(by<span class="op">=</span><span class="st">'id'</span>).reset_index()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="64">
 <div>
@@ -1725,7 +1725,7 @@ <h3 data-number="4.2.8" class="anchored" data-anchor-id="sec-type-transformation
 </div>
 </div>
 <p>The next code chunk is another example, dissolving the 16 polygons in <code>nz</code> into two geometries of the north and south parts (i.e., the two <code>'Island'</code> groups).</p>
-<div id="dc08aa61" class="cell" data-execution_count="65">
+<div id="fe8d2eec" class="cell" data-execution_count="65">
 <div class="sourceCode cell-code" id="cb77"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb77-1"><a href="#cb77-1" aria-hidden="true" tabindex="-1"></a>nz_dis1 <span class="op">=</span> nz[[<span class="st">'Island'</span>, <span class="st">'Population'</span>, <span class="st">'geometry'</span>]] <span class="op">\</span></span>
 <span id="cb77-2"><a href="#cb77-2" aria-hidden="true" tabindex="-1"></a>    .dissolve(by<span class="op">=</span><span class="st">'Island'</span>, aggfunc<span class="op">=</span><span class="st">'sum'</span>) <span class="op">\</span></span>
 <span id="cb77-3"><a href="#cb77-3" aria-hidden="true" tabindex="-1"></a>    .reset_index()</span>
@@ -1763,7 +1763,7 @@ <h3 data-number="4.2.8" class="anchored" data-anchor-id="sec-type-transformation
 </div>
 </div>
 <p>Note that <code>.dissolve</code> not only combines single-part into multi-part geometries, but also dissolves any internal borders. So, in fact, the resulting geometries may be single-part (in case when all parts touch each other, unlike in <code>nz</code>). If, for some reason, we want to combine geometries into multi-part <em>without</em> dissolving, we can fall back to the <strong>pandas</strong> <code>.agg</code> method (custom table aggregation), supplemented with a <strong>shapely</strong> function specifying how exactly we want to transform each group of geometries into a new single geometry. In the following example, for instance, we collect all <code>'Polygon'</code> and <code>'MultiPolygon'</code> parts of <code>nz</code> into a single <code>'MultiPolygon'</code> geometry with many separate parts (i.e., without dissolving), per group.</p>
-<div id="c41a2754" class="cell" data-execution_count="66">
+<div id="a6a268bb" class="cell" data-execution_count="66">
 <div class="sourceCode cell-code" id="cb78"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb78-1"><a href="#cb78-1" aria-hidden="true" tabindex="-1"></a>nz_dis2 <span class="op">=</span> nz <span class="op">\</span></span>
 <span id="cb78-2"><a href="#cb78-2" aria-hidden="true" tabindex="-1"></a>    .groupby(<span class="st">'Island'</span>) <span class="op">\</span></span>
 <span id="cb78-3"><a href="#cb78-3" aria-hidden="true" tabindex="-1"></a>    .agg({</span>
@@ -1904,7 +1904,7 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="sec-geo-ras"><span class=
 <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin"><span class="header-section-number">4.3.1</span> Extent and origin</h3>
 <p>When merging or performing map algebra on rasters, their resolution, projection, origin, and/or extent have to match. Otherwise, how should we add the values of one raster with a resolution of <code>0.2</code> decimal degrees to a second raster with a resolution of <code>1</code> decimal degree? The same problem arises when we would like to merge satellite imagery from different sensors with different projections and resolutions. We can deal with such mismatches by aligning the rasters. Typically, raster alignment is done through resampling—that way, it is guaranteed that the rasters match exactly (<a href="#sec-raster-resampling" class="quarto-xref"><span>Section 4.3.3</span></a>). However, sometimes it can be useful to modify raster placement and extent manually, by adding or removing rows and columns, or by modifying the origin, that is, slightly shifting the raster. Sometimes, there are reasons other than alignment with a second raster for manually modifying raster extent and placement. For example, it may be useful to add extra rows and columns to a raster prior to focal operations, so that it is easier to operate on the edges.</p>
 <p>Let’s demostrate the first operation, raster padding. First, we will read the array with the <code>elev.tif</code> values:</p>
-<div id="5b8d8de0" class="cell" data-execution_count="68">
+<div id="7d56c208" class="cell" data-execution_count="68">
 <div class="sourceCode cell-code" id="cb80"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb80-1"><a href="#cb80-1" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> src_elev.read(<span class="dv">1</span>)</span>
 <span id="cb80-2"><a href="#cb80-2" aria-hidden="true" tabindex="-1"></a>r</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="68">
@@ -1917,7 +1917,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 </div>
 <p>To pad an <code>ndarray</code>, we can use the <code>np.pad</code> function. The function accepts an array, and a tuple of the form <code>((rows_top,rows_bottom),(columns_left, columns_right))</code>. Also, we can specify the value that’s being used for padding with <code>constant_values</code> (e.g., <code>18</code>). For example, here we pad <code>r</code> with one extra row and two extra columns, on both sides, resulting in the array <code>r_pad</code>:</p>
-<div id="5c05948a" class="cell" data-execution_count="69">
+<div id="fa560db4" class="cell" data-execution_count="69">
 <div class="sourceCode cell-code" id="cb82"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb82-1"><a href="#cb82-1" aria-hidden="true" tabindex="-1"></a>rows <span class="op">=</span> <span class="dv">1</span></span>
 <span id="cb82-2"><a href="#cb82-2" aria-hidden="true" tabindex="-1"></a>cols <span class="op">=</span> <span class="dv">2</span></span>
 <span id="cb82-3"><a href="#cb82-3" aria-hidden="true" tabindex="-1"></a>r_pad <span class="op">=</span> np.pad(r, ((rows,rows),(cols,cols)), constant_values<span class="op">=</span><span class="dv">18</span>)</span>
@@ -1934,7 +1934,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 </div>
 <p>However, for <code>r_pad</code> to be used in any spatial operation, we also have to update its transformation matrix. Whenever we add extra columns on the left, or extra rows on top, the raster <em>origin</em> changes. To reflect this fact, we have to take to ‘original’ origin and add the required multiple of pixel widths or heights (i.e., raster resolution steps). The transformation matrix of a raster is accessible from the raster file metadata (<a href="01-spatial-data.html#sec-raster-from-scratch" class="quarto-xref"><span>Section 1.3.2</span></a>) or, as a shortcut, through the <code>.transform</code> property of the raster file connection. For example, the next code chunk shows the transformation matrix of <code>elev.tif</code>.</p>
-<div id="8304fe7b" class="cell" data-execution_count="70">
+<div id="28615d1b" class="cell" data-execution_count="70">
 <div class="sourceCode cell-code" id="cb84"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb84-1"><a href="#cb84-1" aria-hidden="true" tabindex="-1"></a>src_elev.transform </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="70">
 <pre><code>Affine(0.5, 0.0, -1.5,
@@ -1942,7 +1942,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 </div>
 <p>From the transformation matrix, we are able to extract the origin.</p>
-<div id="36ef2917" class="cell" data-execution_count="71">
+<div id="e20eee2c" class="cell" data-execution_count="71">
 <div class="sourceCode cell-code" id="cb86"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb86-1"><a href="#cb86-1" aria-hidden="true" tabindex="-1"></a>xmin, ymax <span class="op">=</span> src_elev.transform[<span class="dv">2</span>], src_elev.transform[<span class="dv">5</span>]</span>
 <span id="cb86-2"><a href="#cb86-2" aria-hidden="true" tabindex="-1"></a>xmin, ymax</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="71">
@@ -1950,7 +1950,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 </div>
 <p>We can also get the resolution of the data, which is the distance between two adjacent pixels.</p>
-<div id="72ca5036" class="cell" data-execution_count="72">
+<div id="e62608bb" class="cell" data-execution_count="72">
 <div class="sourceCode cell-code" id="cb88"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb88-1"><a href="#cb88-1" aria-hidden="true" tabindex="-1"></a>dx, dy <span class="op">=</span> src_elev.transform[<span class="dv">0</span>], src_elev.transform[<span class="dv">4</span>]</span>
 <span id="cb88-2"><a href="#cb88-2" aria-hidden="true" tabindex="-1"></a>dx, dy</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="72">
@@ -1958,7 +1958,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 </div>
 <p>These two parts of information are enough to calculate the new origin (<code>xmin_new,ymax_new</code>) of the padded raster.</p>
-<div id="5bad332b" class="cell" data-execution_count="73">
+<div id="5b6f953e" class="cell" data-execution_count="73">
 <div class="sourceCode cell-code" id="cb90"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb90-1"><a href="#cb90-1" aria-hidden="true" tabindex="-1"></a>xmin_new <span class="op">=</span> xmin <span class="op">-</span> dx <span class="op">*</span> cols</span>
 <span id="cb90-2"><a href="#cb90-2" aria-hidden="true" tabindex="-1"></a>ymax_new <span class="op">=</span> ymax <span class="op">-</span> dy <span class="op">*</span> rows</span>
 <span id="cb90-3"><a href="#cb90-3" aria-hidden="true" tabindex="-1"></a>xmin_new, ymax_new</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1967,7 +1967,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 </div>
 <p>Using the updated origin, we can update the transformation matrix (<a href="01-spatial-data.html#sec-raster-from-scratch" class="quarto-xref"><span>Section 1.3.2</span></a>). Keep in mind that the meaning of the last two arguments is <code>xsize</code>, <code>ysize</code>, so we need to pass the absolute value of <code>dy</code> (since it is negative).</p>
-<div id="0afefe41" class="cell" data-execution_count="74">
+<div id="352ca7f0" class="cell" data-execution_count="74">
 <div class="sourceCode cell-code" id="cb92"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a>new_transform <span class="op">=</span> rasterio.transform.from_origin(</span>
 <span id="cb92-2"><a href="#cb92-2" aria-hidden="true" tabindex="-1"></a>    west<span class="op">=</span>xmin_new, </span>
 <span id="cb92-3"><a href="#cb92-3" aria-hidden="true" tabindex="-1"></a>    north<span class="op">=</span>ymax_new, </span>
@@ -2000,7 +2000,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 </div>
 <p>We can shift a raster origin not just when padding, but in any other use case, just by changing its transformation matrix. The effect is that the raster is going to be shifted (which is analogous to <code>.translate</code> for shifting a vector layer, see <a href="#sec-affine-transformations" class="quarto-xref"><span>Section 4.2.4</span></a>). Manually shifting a raster to arbitrary distance is rarely needed in real-life scenarios, but it is useful to know how to do it at least for a better understanding of the concept of <em>raster origin</em>. As an example, let’s shift the origin of <code>elev.tif</code> by <code>(-0.25,0.25)</code>. First, we need to calculate the new origin.</p>
-<div id="93deff21" class="cell" data-execution_count="76">
+<div id="63fe3528" class="cell" data-execution_count="76">
 <div class="sourceCode cell-code" id="cb95"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb95-1"><a href="#cb95-1" aria-hidden="true" tabindex="-1"></a>xmin_new <span class="op">=</span> xmin <span class="op">-</span> <span class="fl">0.25</span>  <span class="co"># shift xmin to the left</span></span>
 <span id="cb95-2"><a href="#cb95-2" aria-hidden="true" tabindex="-1"></a>ymax_new <span class="op">=</span> ymax <span class="op">+</span> <span class="fl">0.25</span>  <span class="co"># shift ymax upwards</span></span>
 <span id="cb95-3"><a href="#cb95-3" aria-hidden="true" tabindex="-1"></a>xmin_new, ymax_new</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2010,7 +2010,7 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="sec-extent-and-origin">
 </div>
 <p>To shift the origin in other directions we should change the two operators (<code>-</code>, <code>+</code>) accordingly.</p>
 <p>Then, same as when padding (see above), we create an updated transformation matrix.</p>
-<div id="e1ef2e9d" class="cell" data-execution_count="77">
+<div id="79916bd7" class="cell" data-execution_count="77">
 <div class="sourceCode cell-code" id="cb97"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb97-1"><a href="#cb97-1" aria-hidden="true" tabindex="-1"></a>new_transform <span class="op">=</span> rasterio.transform.from_origin(</span>
 <span id="cb97-2"><a href="#cb97-2" aria-hidden="true" tabindex="-1"></a>    west<span class="op">=</span>xmin_new, </span>
 <span id="cb97-3"><a href="#cb97-3" aria-hidden="true" tabindex="-1"></a>    north<span class="op">=</span>ymax_new, </span>
@@ -2064,14 +2064,14 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="sec-raster-agg-disagg">
 <li>Updating the <code>transform</code> according to <code>out_shape</code></li>
 </ul>
 <p>Let’s demonstrate it, using the <code>dem.tif</code> file. Note the original shape of the raster; it has <code>117</code> rows and <code>117</code> columns.</p>
-<div id="28791405" class="cell" data-execution_count="79">
+<div id="27f33e73" class="cell" data-execution_count="79">
 <div class="sourceCode cell-code" id="cb100"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb100-1"><a href="#cb100-1" aria-hidden="true" tabindex="-1"></a>src.read(<span class="dv">1</span>).shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="79">
 <pre><code>(117, 117)</code></pre>
 </div>
 </div>
 <p>Also note the transform, which tells us that the raster resolution is about 30.85 <span class="math inline">\(m\)</span>.</p>
-<div id="c92f0fdc" class="cell" data-execution_count="80">
+<div id="f6ae37c6" class="cell" data-execution_count="80">
 <div class="sourceCode cell-code" id="cb102"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb102-1"><a href="#cb102-1" aria-hidden="true" tabindex="-1"></a>src.transform</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="80">
 <pre><code>Affine(30.849999999999604, 0.0, 794599.1076146346,
@@ -2079,7 +2079,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="sec-raster-agg-disagg">
 </div>
 </div>
 <p>To aggregate, instead of reading the raster values the usual way, as in <code>src.read(1)</code>, we can specify <code>out_shape</code> to read the values into a different shape. Here, we calculate a new shape which is downscaled by a factor of <code>5</code>, i.e., the number of rows and columns is multiplied by <code>0.2</code>. We must truncate any partial rows and columns, e.g., using <code>int</code>. Each new pixel is now obtained, or resampled, from <span class="math inline">\(\sim 5 \times 5 = \sim 25\)</span> ‘old’ raster values. It is crucial to choose an appropriate <em>resampling method</em> through the <code>resampling</code> parameter. Here we use <code>rasterio.enums.Resampling.average</code>, i.e., the new ‘large’ pixel value is the average of all coinciding small pixels, which makes sense for our elevation data in <code>dem.tif</code>. See <a href="#sec-raster-resampling" class="quarto-xref"><span>Section 4.3.3</span></a> for a list of other available methods.</p>
-<div id="91d3e046" class="cell" data-execution_count="81">
+<div id="7257b937" class="cell" data-execution_count="81">
 <div class="sourceCode cell-code" id="cb104"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb104-1"><a href="#cb104-1" aria-hidden="true" tabindex="-1"></a>factor <span class="op">=</span> <span class="fl">0.2</span></span>
 <span id="cb104-2"><a href="#cb104-2" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> src.read(<span class="dv">1</span>,</span>
 <span id="cb104-3"><a href="#cb104-3" aria-hidden="true" tabindex="-1"></a>    out_shape<span class="op">=</span>(</span>
@@ -2090,14 +2090,14 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="sec-raster-agg-disagg">
 <span id="cb104-8"><a href="#cb104-8" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>As expected, the resulting array <code>r</code> has ~5 times smaller dimensions, as shown below.</p>
-<div id="3ba8c5db" class="cell" data-execution_count="82">
+<div id="ca4c4b42" class="cell" data-execution_count="82">
 <div class="sourceCode cell-code" id="cb105"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb105-1"><a href="#cb105-1" aria-hidden="true" tabindex="-1"></a>r.shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="82">
 <pre><code>(23, 23)</code></pre>
 </div>
 </div>
 <p>What’s left to be done is the second step, to update the transform, taking into account the change in raster shape. This can be done as follows, using <code>.transform.scale</code>.</p>
-<div id="3476cb55" class="cell" data-execution_count="83">
+<div id="8f67767e" class="cell" data-execution_count="83">
 <div class="sourceCode cell-code" id="cb107"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb107-1"><a href="#cb107-1" aria-hidden="true" tabindex="-1"></a>new_transform <span class="op">=</span> src.transform <span class="op">*</span> src.transform.scale(</span>
 <span id="cb107-2"><a href="#cb107-2" aria-hidden="true" tabindex="-1"></a>    (src.width <span class="op">/</span> r.shape[<span class="dv">1</span>]),</span>
 <span id="cb107-3"><a href="#cb107-3" aria-hidden="true" tabindex="-1"></a>    (src.height <span class="op">/</span> r.shape[<span class="dv">0</span>])</span>
@@ -2147,7 +2147,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="sec-raster-agg-disagg">
 </figure>
 </div>
 <p>This is a good opportunity to demonstrate exporting a raster with modified dimensions and transformation matrix. We can update the raster metadata required for writing with the <code>update</code> method.</p>
-<div id="30e59e1e" class="cell" data-execution_count="85">
+<div id="b71ee2cd" class="cell" data-execution_count="85">
 <div class="sourceCode cell-code" id="cb110"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb110-1"><a href="#cb110-1" aria-hidden="true" tabindex="-1"></a>dst_kwargs <span class="op">=</span> src.meta.copy()</span>
 <span id="cb110-2"><a href="#cb110-2" aria-hidden="true" tabindex="-1"></a>dst_kwargs.update({</span>
 <span id="cb110-3"><a href="#cb110-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">'transform'</span>: new_transform,</span>
@@ -2168,7 +2168,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="sec-raster-agg-disagg">
 </div>
 </div>
 <p>Then we can create a new file (<code>dem_agg5.tif</code>) in writing mode, and write the values from the aggregated array <code>r</code> into the 1<sup>st</sup> band of the file (see <a href="07-read-write.html#sec-data-output-raster" class="quarto-xref"><span>Section 7.6.2</span></a> for a detailed explanation of writing raster files with <strong>rasterio</strong>).</p>
-<div id="0e3b9689" class="cell" data-execution_count="86">
+<div id="33678d51" class="cell" data-execution_count="86">
 <div class="sourceCode cell-code" id="cb112"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb112-1"><a href="#cb112-1" aria-hidden="true" tabindex="-1"></a>dst <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/dem_agg5.tif'</span>, <span class="st">'w'</span>, <span class="op">**</span>dst_kwargs)</span>
 <span id="cb112-2"><a href="#cb112-2" aria-hidden="true" tabindex="-1"></a>dst.write(r, <span class="dv">1</span>)</span>
 <span id="cb112-3"><a href="#cb112-3" aria-hidden="true" tabindex="-1"></a>dst.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2209,7 +2209,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="sec-raster-agg-disagg">
 <li>Smoothing techniques, such as bilinear resampling (<code>rasterio.enums.Resampling.bilinear</code>), when we would like the smaller pixels to reflect gradual change between the original values, e.g., when the disaggregated raster is used for visualization purposes</li>
 </ul>
 <p>To disaggregate a raster, we go through exactly the same workflow as for aggregation, only using a different scaling factor, such as <code>factor=5</code> instead of <code>factor=0.2</code>, i.e., <em>increasing</em> the number of raster pixels instead of decreasing. In the example below, we disaggregate using bilinear interpolation, to get a smoothed high-resolution raster.</p>
-<div id="fe0c6203" class="cell" data-execution_count="87">
+<div id="c44f1813" class="cell" data-execution_count="87">
 <div class="sourceCode cell-code" id="cb116"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb116-1"><a href="#cb116-1" aria-hidden="true" tabindex="-1"></a>factor <span class="op">=</span> <span class="dv">5</span></span>
 <span id="cb116-2"><a href="#cb116-2" aria-hidden="true" tabindex="-1"></a>r2 <span class="op">=</span> src.read(<span class="dv">1</span>,</span>
 <span id="cb116-3"><a href="#cb116-3" aria-hidden="true" tabindex="-1"></a>    out_shape<span class="op">=</span>(</span>
@@ -2220,14 +2220,14 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="sec-raster-agg-disagg">
 <span id="cb116-8"><a href="#cb116-8" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>As expected, the dimensions of the disaggregated raster are this time ~5 times <em>bigger</em> than the original ones.</p>
-<div id="a15e80cb" class="cell" data-execution_count="88">
+<div id="b8bed215" class="cell" data-execution_count="88">
 <div class="sourceCode cell-code" id="cb117"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb117-1"><a href="#cb117-1" aria-hidden="true" tabindex="-1"></a>r2.shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="88">
 <pre><code>(585, 585)</code></pre>
 </div>
 </div>
 <p>To calculate the new transform, we use the same expression as for aggregation, only with the new <code>r2</code> shape.</p>
-<div id="675ee673" class="cell" data-execution_count="89">
+<div id="1d844681" class="cell" data-execution_count="89">
 <div class="sourceCode cell-code" id="cb119"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb119-1"><a href="#cb119-1" aria-hidden="true" tabindex="-1"></a>new_transform2 <span class="op">=</span> src.transform <span class="op">*</span> src.transform.scale(</span>
 <span id="cb119-2"><a href="#cb119-2" aria-hidden="true" tabindex="-1"></a>    (src.width <span class="op">/</span> r2.shape[<span class="dv">1</span>]),</span>
 <span id="cb119-3"><a href="#cb119-3" aria-hidden="true" tabindex="-1"></a>    (src.height <span class="op">/</span> r2.shape[<span class="dv">0</span>])</span>
@@ -2313,7 +2313,7 @@ <h3 data-number="4.3.3" class="anchored" data-anchor-id="sec-raster-resampling">
 <li><code>rasterio.enums.Resampling.sum</code>—Median</li>
 </ul>
 <p>Let’s demonstrate resampling into a destination grid which is specified through numeric constraints, such as the extent and resolution. Again, these could have been specified manually (such as here), or obtained from a template raster metadata that we would like to match. Note that the resolution of the destination grid is ~10 times more coarse (300 <span class="math inline">\(m\)</span>) than the original resolution of <code>dem.tif</code> (~30 <span class="math inline">\(m\)</span>) (<a href="#fig-raster-resample" class="quarto-xref">Figure&nbsp;<span>4.35</span></a>).</p>
-<div id="f171d5da" class="cell" data-execution_count="91">
+<div id="daf8b7e6" class="cell" data-execution_count="91">
 <div class="sourceCode cell-code" id="cb122"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb122-1"><a href="#cb122-1" aria-hidden="true" tabindex="-1"></a>xmin <span class="op">=</span> <span class="dv">794650</span></span>
 <span id="cb122-2"><a href="#cb122-2" aria-hidden="true" tabindex="-1"></a>xmax <span class="op">=</span> <span class="dv">798250</span></span>
 <span id="cb122-3"><a href="#cb122-3" aria-hidden="true" tabindex="-1"></a>ymin <span class="op">=</span> <span class="dv">8931750</span> </span>
@@ -2321,7 +2321,7 @@ <h3 data-number="4.3.3" class="anchored" data-anchor-id="sec-raster-resampling">
 <span id="cb122-5"><a href="#cb122-5" aria-hidden="true" tabindex="-1"></a>res <span class="op">=</span> <span class="dv">300</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The corresponding transform based on these constraints can be created using the <code>rasterio.transform.from_origin</code> function, as follows:</p>
-<div id="697b6a94" class="cell" data-execution_count="92">
+<div id="1b01e45f" class="cell" data-execution_count="92">
 <div class="sourceCode cell-code" id="cb123"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb123-1"><a href="#cb123-1" aria-hidden="true" tabindex="-1"></a>dst_transform <span class="op">=</span> rasterio.transform.from_origin(</span>
 <span id="cb123-2"><a href="#cb123-2" aria-hidden="true" tabindex="-1"></a>    west<span class="op">=</span>xmin, </span>
 <span id="cb123-3"><a href="#cb123-3" aria-hidden="true" tabindex="-1"></a>    north<span class="op">=</span>ymax, </span>
@@ -2336,7 +2336,7 @@ <h3 data-number="4.3.3" class="anchored" data-anchor-id="sec-raster-resampling">
 </div>
 <p>In case we needed to resample into a grid specified by an existing template raster, we could have skipped this step and simply read the transform from the template file, as in <code>rasterio.open('template.tif').transform</code>.</p>
 <p>We can move on to creating the destination file connection. For that, we also have to know the raster dimensions, which can be derived from the extent and the resolution.</p>
-<div id="6ae23162" class="cell" data-execution_count="93">
+<div id="ac1772c9" class="cell" data-execution_count="93">
 <div class="sourceCode cell-code" id="cb125"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb125-1"><a href="#cb125-1" aria-hidden="true" tabindex="-1"></a>width <span class="op">=</span> <span class="bu">int</span>((xmax <span class="op">-</span> xmin) <span class="op">/</span> res)</span>
 <span id="cb125-2"><a href="#cb125-2" aria-hidden="true" tabindex="-1"></a>height <span class="op">=</span> <span class="bu">int</span>((ymax <span class="op">-</span> ymin) <span class="op">/</span> res)</span>
 <span id="cb125-3"><a href="#cb125-3" aria-hidden="true" tabindex="-1"></a>width, height</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2345,7 +2345,7 @@ <h3 data-number="4.3.3" class="anchored" data-anchor-id="sec-raster-resampling">
 </div>
 </div>
 <p>Now we can create the destination file connection. We are using the same metadata as the source file, except for the dimensions and the transform, which are going to be different and reflect the resampling process.</p>
-<div id="d9da2054" class="cell" data-execution_count="94">
+<div id="fb001e16" class="cell" data-execution_count="94">
 <div class="sourceCode cell-code" id="cb127"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb127-1"><a href="#cb127-1" aria-hidden="true" tabindex="-1"></a>dst_kwargs <span class="op">=</span> src.meta.copy()</span>
 <span id="cb127-2"><a href="#cb127-2" aria-hidden="true" tabindex="-1"></a>dst_kwargs.update({</span>
 <span id="cb127-3"><a href="#cb127-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">'transform'</span>: dst_transform,</span>
@@ -2355,7 +2355,7 @@ <h3 data-number="4.3.3" class="anchored" data-anchor-id="sec-raster-resampling">
 <span id="cb127-7"><a href="#cb127-7" aria-hidden="true" tabindex="-1"></a>dst <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/dem_resample_nearest.tif'</span>, <span class="st">'w'</span>, <span class="op">**</span>dst_kwargs)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Finally, we reproject using function <code>rasterio.warp.reproject</code>. Note that the source and destination are specified using <code>rasterio.band</code> applied on both file connections, reflecting the fact that we operate on a specific layer of the rasters. The resampling method being used here is nearest neighbor resampling (<code>rasterio.enums.Resampling.nearest</code>).</p>
-<div id="0bedac9f" class="cell" data-execution_count="95">
+<div id="0044a18d" class="cell" data-execution_count="95">
 <div class="sourceCode cell-code" id="cb128"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb128-1"><a href="#cb128-1" aria-hidden="true" tabindex="-1"></a>rasterio.warp.reproject(</span>
 <span id="cb128-2"><a href="#cb128-2" aria-hidden="true" tabindex="-1"></a>    source<span class="op">=</span>rasterio.band(src, <span class="dv">1</span>),</span>
 <span id="cb128-3"><a href="#cb128-3" aria-hidden="true" tabindex="-1"></a>    destination<span class="op">=</span>rasterio.band(dst, <span class="dv">1</span>),</span>
@@ -2372,11 +2372,11 @@ <h3 data-number="4.3.3" class="anchored" data-anchor-id="sec-raster-resampling">
 </div>
 </div>
 <p>In the end, we close the file connection, thus finalizing the new file <code>output/dem_resample_nearest.tif</code> with the resampling result (<a href="#fig-raster-resample" class="quarto-xref">Figure&nbsp;<span>4.35</span></a>).</p>
-<div id="7c29756d" class="cell" data-execution_count="96">
+<div id="5adae25f" class="cell" data-execution_count="96">
 <div class="sourceCode cell-code" id="cb130"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb130-1"><a href="#cb130-1" aria-hidden="true" tabindex="-1"></a>dst.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Here is another code section just to demonstrate a different resampling method, the maximum resampling, i.e., every new pixel gets the maximum value of all the original pixels it coincides with (<a href="#fig-raster-resample" class="quarto-xref">Figure&nbsp;<span>4.35</span></a>). Note that all arguments in the <code>rasterio.warp.reproject</code> function call are identical to the previous example, except for the <code>resampling</code> method.</p>
-<div id="dde9397b" class="cell" data-execution_count="97">
+<div id="0b5d53ab" class="cell" data-execution_count="97">
 <div class="sourceCode cell-code" id="cb131"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb131-1"><a href="#cb131-1" aria-hidden="true" tabindex="-1"></a>dst <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/dem_resample_maximum.tif'</span>, <span class="st">'w'</span>, <span class="op">**</span>dst_kwargs)</span>
 <span id="cb131-2"><a href="#cb131-2" aria-hidden="true" tabindex="-1"></a>rasterio.warp.reproject(</span>
 <span id="cb131-3"><a href="#cb131-3" aria-hidden="true" tabindex="-1"></a>    source<span class="op">=</span>rasterio.band(src, <span class="dv">1</span>),</span>
diff --git a/05-raster-vector.html b/05-raster-vector.html
index 3314e7bf..57d6609a 100644
--- a/05-raster-vector.html
+++ b/05-raster-vector.html
@@ -343,7 +343,7 @@ <h1 class="title"><span id="sec-raster-vector" class="quarto-section-identifier"
 <section id="prerequisites" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2>
 <p>This chapter requires importing the following packages:</p>
-<div id="42a07ed2" class="cell" data-execution_count="3">
+<div id="8895ddda" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> math</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
@@ -358,7 +358,7 @@ <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2
 <span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterstats</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>It also relies on the following data files:</p>
-<div id="4e62578f" class="cell" data-execution_count="4">
+<div id="57fad86b" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>src_srtm <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/srtm.tif'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>src_nlcd <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/nlcd.tif'</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>src_grain <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/grain.tif'</span>)</span>
@@ -391,11 +391,11 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 <li>The <code>zion.gpkg</code> vector layer representing the Zion National Park boundaries (a <code>GeoDataFrame</code> named <code>zion</code>)</li>
 </ul>
 <p>Both target and cropping objects must have the same projection. Since it is easier and more precise to reproject vector layers, compared to rasters, we use the following expression to reproject (<a href="06-reproj.html#sec-reprojecting-vector-geometries" class="quarto-xref"><span>Section 6.7</span></a>) the vector layer <code>zion</code> into the CRS of the raster <code>src_srtm</code>.</p>
-<div id="9d439810" class="cell" data-execution_count="5">
+<div id="ba171067" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>zion <span class="op">=</span> zion.to_crs(src_srtm.crs)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>To mask the image, i.e., convert all pixels which do not intersect with the <code>zion</code> polygon to ‘No Data’, we use the <code>rasterio.mask.mask</code> function.</p>
-<div id="263cb736" class="cell" data-execution_count="6">
+<div id="a0318fb1" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>out_image_mask, out_transform_mask <span class="op">=</span> rasterio.mask.mask(</span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>    src_srtm, </span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    zion.geometry, </span>
@@ -404,7 +404,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 <span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Note that we need to choose and specify a ‘No Data’ value, within the valid range according to the data type. Since <code>srtm.tif</code> is of type <code>uint16</code> (how can we check?), we choose <code>9999</code> (a positive integer that is guaranteed not to occur in the raster). Also note that <strong>rasterio</strong> does not directly support <strong>geopandas</strong> data structures, so we need to pass a ‘collection’ of <strong>shapely</strong> geometries: a <code>GeoSeries</code> (see above) or a <code>list</code> of <strong>shapely</strong> geometries (see next example) both work. The output consists of two objects. The first one is the <code>out_image</code> array with the masked values.</p>
-<div id="4c042a46" class="cell" data-execution_count="7">
+<div id="b0eff164" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>out_image_mask</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
 <pre><code>array([[[9999, 9999, 9999, ..., 9999, 9999, 9999],
@@ -417,7 +417,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 </div>
 </div>
 <p>The second one is a new transformation matrix <code>out_transform</code>.</p>
-<div id="bbcd6e4c" class="cell" data-execution_count="8">
+<div id="a490333d" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>out_transform_mask</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
 <pre><code>Affine(0.0008333333332777796, 0.0, -113.23958321278403,
@@ -426,7 +426,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 </div>
 <p>Note that masking (without cropping!) does not modify the raster extent. Therefore, the new transform is identical to the original (<code>src_srtm.transform</code>).</p>
 <p>Unfortunately, the <code>out_image</code> and <code>out_transform</code> objects do not contain any information indicating that <code>9999</code> represents ‘No Data’. To associate the information with the raster, we must write it to file along with the corresponding metadata. For example, to write the masked raster to file, we first need to modify the ‘No Data’ setting in the metadata.</p>
-<div id="21554968" class="cell" data-execution_count="9">
+<div id="e7a3aba0" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>dst_kwargs <span class="op">=</span> src_srtm.meta</span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>dst_kwargs.update(nodata<span class="op">=</span><span class="dv">9999</span>)</span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>dst_kwargs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -443,17 +443,17 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 </div>
 </div>
 <p>Then we can write the masked raster to file with the updated metadata object.</p>
-<div id="00869387" class="cell" data-execution_count="10">
+<div id="8a643a78" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>new_dataset <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/srtm_masked.tif'</span>, <span class="st">'w'</span>, <span class="op">**</span>dst_kwargs)</span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>new_dataset.write(out_image_mask)</span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>new_dataset.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now we can re-import the raster and check that the ‘No Data’ value is correctly set.</p>
-<div id="5da8acab" class="cell" data-execution_count="11">
+<div id="6101f7e2" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>src_srtm_mask <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/srtm_masked.tif'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The <code>.meta</code> property contains the <code>nodata</code> entry. Now, any relevant operation (such as plotting, see <a href="#fig-raster-crop" class="quarto-xref">Figure&nbsp;<span>5.2</span></a> (b)) will take ‘No Data’ into account.</p>
-<div id="570152ab" class="cell" data-execution_count="12">
+<div id="4fd9d904" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>src_srtm_mask.meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="12">
 <pre><code>{'driver': 'GTiff',
@@ -490,7 +490,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 </div>
 </div>
 <p>The extent can now be used for masking. Here, we are also using the <code>all_touched=True</code> option, so that pixels which are partially overlapping with the extent are also included in the output.</p>
-<div id="5214e953" class="cell" data-execution_count="14">
+<div id="2425a62e" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>out_image_crop, out_transform_crop <span class="op">=</span> rasterio.mask.mask(</span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>    src_srtm, </span>
 <span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>    [bb], </span>
@@ -514,7 +514,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 </div>
 </div>
 <p>Finally, the third example is where we perform both crop and mask operations, using <code>rasterio.mask.mask</code> with <code>crop=True</code> passing <code>zion.geometry</code>.</p>
-<div id="a8c7c827" class="cell" data-execution_count="15">
+<div id="6dde496f" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>out_image_mask_crop, out_transform_mask_crop <span class="op">=</span> rasterio.mask.mask(</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>    src_srtm, </span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>    zion.geometry, </span>
@@ -523,7 +523,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 <span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>When writing the result to a file, it is here crucial to update the transform and dimensions, since they were modified as a result of cropping. Also note that <code>out_image_mask_crop</code> is a three-dimensional array (even though it has one band in this case), so the number of rows and columns are in <code>.shape[1]</code> and <code>.shape[2]</code> (rather than <code>.shape[0]</code> and <code>.shape[1]</code>), respectively.</p>
-<div id="e393eaf5" class="cell" data-execution_count="16">
+<div id="348f7a43" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>dst_kwargs <span class="op">=</span> src_srtm.meta</span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>dst_kwargs.update({</span>
 <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">'nodata'</span>: <span class="dv">9999</span>,</span>
@@ -540,7 +540,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="sec-raster-cropping"><spa
 <span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a>new_dataset.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Let’s also create a file connection to the newly created file <code>srtm_masked_cropped.tif</code> in order to plot it (<a href="#fig-raster-crop" class="quarto-xref">Figure&nbsp;<span>5.2</span></a> (d)).</p>
-<div id="ed23907f" class="cell" data-execution_count="17">
+<div id="3fd0d19c" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>src_srtm_mask_crop <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/srtm_masked_cropped.tif'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p><a href="#fig-raster-crop" class="quarto-xref">Figure&nbsp;<span>5.2</span></a> shows the original raster, and the three masking and/or cropping results.</p>
@@ -651,7 +651,7 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="sec-extraction-to-point
 </div>
 </div>
 <p>The following expression extracts elevation values from <code>srtm.tif</code> according to <code>zion_points</code>, using <code>rasterstats.point_query</code>.</p>
-<div id="b6522967" class="cell" data-execution_count="20">
+<div id="db2c9517" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>result1 <span class="op">=</span> rasterstats.point_query(</span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>    zion_points, </span>
 <span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>    src_srtm.read(<span class="dv">1</span>), </span>
@@ -662,7 +662,7 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="sec-extraction-to-point
 </div>
 <p>The first two arguments are the vector layer and the array with raster values. The <code>nodata</code> and <code>affine</code> arguments are used to align the array values into the CRS, and to correctly treat ‘No Data’ flags. Finally, the <code>interpolate</code> argument controls the way that the cell values are assigned to the point; <code>interpolate='nearest'</code> typically makes more sense, as opposed to the other option <code>interpolate='bilinear'</code> which is the default.</p>
 <p>Alternatively, we can pass a raster file path to <code>rasterstats.point_query</code>, in which case <code>nodata</code> and <code>affine</code> are not necessary, as the function can understand those properties directly from the raster file.</p>
-<div id="d6d90172" class="cell" data-execution_count="21">
+<div id="b5715a06" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>result2 <span class="op">=</span> rasterstats.point_query(</span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>    zion_points, </span>
 <span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">'data/srtm.tif'</span>,</span>
@@ -670,14 +670,14 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="sec-extraction-to-point
 <span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Either way, the resulting object is a <code>list</code> of raster values, corresponding to <code>zion_points</code>. For example, here are the elevations of the first five points.</p>
-<div id="f4baca5f" class="cell" data-execution_count="22">
+<div id="239b0000" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>result1[:<span class="dv">5</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
 <pre><code>[1802, 2433, 1886, 1370, 1452]</code></pre>
 </div>
 </div>
 <p>To get a <code>GeoDataFrame</code> with the original points geometries (and other attributes, if any), as well as the extracted raster values, we can assign the extraction result into a new column. As you can see, both approaches give the same result.</p>
-<div id="856d9666" class="cell" data-execution_count="23">
+<div id="3f0d1537" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>zion_points[<span class="st">'elev1'</span>] <span class="op">=</span> result1</span>
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>zion_points[<span class="st">'elev2'</span>] <span class="op">=</span> result2</span>
 <span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>zion_points</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -750,7 +750,7 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="sec-extraction-to-point
 <h3 data-number="5.3.2" class="anchored" data-anchor-id="sec-extraction-to-lines"><span class="header-section-number">5.3.2</span> Extraction to lines</h3>
 <p>Raster extraction is also applicable with line selectors. The typical line extraction algorithm is to extract one value for each raster cell touched by a line. However, this particular approach is not recommended to obtain values along the transects, as it is hard to get the correct distance between each pair of extracted raster values.</p>
 <p>For line extraction, a better approach is to split the line into many points (at equal distances along the line) and then extract the values for these points using the ‘extraction to points’ technique (<a href="#sec-extraction-to-points" class="quarto-xref"><span>Section 5.3.1</span></a>). To demonstrate this, the code below creates (see <a href="01-spatial-data.html#sec-vector-data" class="quarto-xref"><span>Section 1.2</span></a> for recap) <code>zion_transect</code>, a straight line going from northwest to southeast of the Zion National Park.</p>
-<div id="650dadaa" class="cell" data-execution_count="24">
+<div id="94f15a04" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>coords <span class="op">=</span> [[<span class="op">-</span><span class="fl">113.2</span>, <span class="fl">37.45</span>], [<span class="op">-</span><span class="fl">112.9</span>, <span class="fl">37.2</span>]]</span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>zion_transect <span class="op">=</span> shapely.LineString(coords)</span>
 <span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(zion_transect)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -760,19 +760,19 @@ <h3 data-number="5.3.2" class="anchored" data-anchor-id="sec-extraction-to-lines
 </div>
 <p>The utility of extracting heights from a linear selector is illustrated by imagining that you are planning a hike. The method demonstrated below provides an ‘elevation profile’ of the route (the line does not need to be straight), useful for estimating how long it will take due to long climbs.</p>
 <p>First, we need to create a layer consisting of points along our line (<code>zion_transect</code>), at specified intervals (e.g., <code>250</code>). To do that, we need to transform the line into a projected CRS (so that we work with true distances, in <span class="math inline">\(m\)</span>), such as UTM. This requires going through a <code>GeoSeries</code>, as <strong>shapely</strong> geometries have no CRS definition nor concept of reprojection (see <a href="01-spatial-data.html#sec-vector-layer-from-scratch" class="quarto-xref"><span>Section 1.2.6</span></a>).</p>
-<div id="49ac9b40" class="cell" data-execution_count="25">
+<div id="74d3c489" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>zion_transect_utm <span class="op">=</span> gpd.GeoSeries(zion_transect, crs<span class="op">=</span><span class="dv">4326</span>).to_crs(<span class="dv">32612</span>)</span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>zion_transect_utm <span class="op">=</span> zion_transect_utm.iloc[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The printout of the new geometry shows this is still a straight line between two points, only with coordinates in a projected CRS.</p>
-<div id="628a18b6" class="cell" data-execution_count="26">
+<div id="f90e26dc" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(zion_transect_utm)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>LINESTRING (305399.67208180577 4147066.650206682, 331380.8917453843 4118750.0947884847)</code></pre>
 </div>
 </div>
 <p>Next, we need to calculate the distances, along the line, where points are going to be generated. We do this using <code>np.arange</code>. The result is a numeric sequence starting at <code>0</code>, going up to line <code>.length</code>, in steps of <code>250</code> (<span class="math inline">\(m\)</span>).</p>
-<div id="fd4b6c8d" class="cell" data-execution_count="27">
+<div id="711802d4" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>distances <span class="op">=</span> np.arange(<span class="dv">0</span>, zion_transect_utm.length, <span class="dv">250</span>)</span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>distances[:<span class="dv">7</span>]  <span class="co">## First 7 distance cutoff points</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
@@ -780,7 +780,7 @@ <h3 data-number="5.3.2" class="anchored" data-anchor-id="sec-extraction-to-lines
 </div>
 </div>
 <p>The distance cutoffs are used to sample (‘interpolate’) points along the line. The <strong>shapely</strong> <code>.interpolate</code> method is used to generate the points, which then are reprojected back to the geographic CRS of the raster (EPSG:<code>4326</code>).</p>
-<div id="773524d1" class="cell" data-execution_count="28">
+<div id="e9ed332d" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-overflow-wrap code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>zion_transect_pnt <span class="op">=</span> [zion_transect_utm.interpolate(d) <span class="cf">for</span> d <span class="kw">in</span> distances]</span>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>zion_transect_pnt <span class="op">=</span> gpd.GeoSeries(zion_transect_pnt, crs<span class="op">=</span><span class="dv">32612</span>) <span class="op">\</span></span>
 <span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a>    .to_crs(src_srtm.crs)</span>
@@ -797,7 +797,7 @@ <h3 data-number="5.3.2" class="anchored" data-anchor-id="sec-extraction-to-lines
 </div>
 </div>
 <p>Finally, we extract the elevation values for each point in our transect and combine the information with <code>zion_transect_pnt</code> (after ‘promoting’ it to a <code>GeoDataFrame</code>, to accommodate extra attributes), using the point extraction method shown earlier (<a href="#sec-extraction-to-points" class="quarto-xref"><span>Section 5.3.1</span></a>). We also attach the respective distance cutoff points <code>distances</code>.</p>
-<div id="30b99369" class="cell" data-execution_count="29">
+<div id="7464fc0c" class="cell" data-execution_count="29">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>result <span class="op">=</span> rasterstats.point_query(</span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>    zion_transect_pnt, </span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>    src_srtm.read(<span class="dv">1</span>), </span>
@@ -922,7 +922,7 @@ <h3 data-number="5.3.2" class="anchored" data-anchor-id="sec-extraction-to-lines
 <section id="sec-extraction-to-polygons" class="level3" data-number="5.3.3">
 <h3 data-number="5.3.3" class="anchored" data-anchor-id="sec-extraction-to-polygons"><span class="header-section-number">5.3.3</span> Extraction to polygons</h3>
 <p>The final type of geographic vector object for raster extraction is polygons. Like lines, polygons tend to return many raster values per vector geometry. For continuous rasters (<a href="#fig-raster-extract-to-polygon" class="quarto-xref">Figure&nbsp;<span>5.5</span></a> (a)), we typically want to generate summary statistics for raster values per polygon, for example to characterize a single region or to compare many regions. The generation of raster summary statistics, by polygons, is demonstrated in the code below using <code>rasterstats.zonal_stats</code>, which creates a list of summary statistics (in this case a list of length 1, since there is just one polygon).</p>
-<div id="65f6c196" class="cell" data-execution_count="31">
+<div id="e09b745c" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>result <span class="op">=</span> rasterstats.zonal_stats(</span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>    zion, </span>
 <span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a>    src_srtm.read(<span class="dv">1</span>), </span>
@@ -949,7 +949,7 @@ <h3 data-number="5.3.3" class="anchored" data-anchor-id="sec-extraction-to-polyg
 </div>
 </div>
 <p>Transformation of the <code>list</code> to a <code>DataFrame</code> (e.g., to attach the derived attributes to the original polygon layer), is straightforward with the <code>pd.DataFrame</code> constructor.</p>
-<div id="3ec2d303" class="cell" data-execution_count="32">
+<div id="08c88aca" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>pd.DataFrame(result)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="32">
 <div>
@@ -987,7 +987,7 @@ <h3 data-number="5.3.3" class="anchored" data-anchor-id="sec-extraction-to-polyg
 </ul>
 <p>See the documentation of <code>rasterstats.zonal_stats</code> for the complete list. Additionally, the <code>rasterstats.zonal_stats</code> function accepts user-defined functions for calculating any custom statistics.</p>
 <p>To count occurrences of categorical raster values within polygons (<a href="#fig-raster-extract-to-polygon" class="quarto-xref">Figure&nbsp;<span>5.5</span></a> (b)), we can use masking (<a href="#sec-raster-cropping" class="quarto-xref"><span>Section 5.2</span></a>) combined with <code>np.unique</code>, as follows.</p>
-<div id="82586b9a" class="cell" data-execution_count="33">
+<div id="fe138abb" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb41"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>out_image, out_transform <span class="op">=</span> rasterio.mask.mask(</span>
 <span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>    src_nlcd, </span>
 <span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>    zion.geometry.to_crs(src_nlcd.crs), </span>
@@ -1072,11 +1072,11 @@ <h2 data-number="5.4" class="anchored" data-anchor-id="sec-rasterization"><span
 <section id="sec-rasterizing-points" class="level3" data-number="5.4.1">
 <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"><span class="header-section-number">5.4.1</span> Rasterizing points</h3>
 <p>To demonstrate point rasterization, we will prepare a ‘template’ raster that has the same extent and CRS as the input vector data <code>cycle_hire_osm_projected</code> (a dataset on cycle hire points in London, illustrated in <a href="#fig-rasterize-points" class="quarto-xref">Figure&nbsp;<span>5.6</span></a> (a)) and a spatial resolution of 1000 <span class="math inline">\(m\)</span>. To do that, we first take our point layer and transform it to a projected CRS.</p>
-<div id="06906038" class="cell" data-execution_count="35">
+<div id="45f137a4" class="cell" data-execution_count="35">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>cycle_hire_osm_projected <span class="op">=</span> cycle_hire_osm.to_crs(<span class="dv">27700</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Next, we calculate the <code>out_shape</code> and <code>transform</code> of the template raster. To calculate the transform, we combine the top-left corner of the <code>cycle_hire_osm_projected</code> bounding box with the required resolution (e.g., 1000 <span class="math inline">\(m\)</span>).</p>
-<div id="149ef179" class="cell" data-execution_count="36">
+<div id="ca8c9164" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb45"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>bounds <span class="op">=</span> cycle_hire_osm_projected.total_bounds</span>
 <span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a>res <span class="op">=</span> <span class="dv">1000</span></span>
 <span id="cb45-3"><a href="#cb45-3" aria-hidden="true" tabindex="-1"></a>transform <span class="op">=</span> rasterio.transform.from_origin(</span>
@@ -1092,7 +1092,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"
 </div>
 </div>
 <p>To calculate the <code>out_shape</code>, we divide the x-axis and y-axis extent by the resolution, taking the ceiling of the results.</p>
-<div id="6363d7ef" class="cell" data-execution_count="37">
+<div id="cc4f5327" class="cell" data-execution_count="37">
 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a>rows <span class="op">=</span> math.ceil((bounds[<span class="dv">3</span>] <span class="op">-</span> bounds[<span class="dv">1</span>]) <span class="op">/</span> res)</span>
 <span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a>cols <span class="op">=</span> math.ceil((bounds[<span class="dv">2</span>] <span class="op">-</span> bounds[<span class="dv">0</span>]) <span class="op">/</span> res)</span>
 <span id="cb47-3"><a href="#cb47-3" aria-hidden="true" tabindex="-1"></a>shape <span class="op">=</span> (rows, cols)</span>
@@ -1103,7 +1103,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"
 </div>
 <p>Finally, we are ready to rasterize. As mentioned above point rasterization can be a very flexible operation: the results depend not only on the nature of the template raster, but also on the pixel ‘activation’ method, namely the way we deal with multiple points matching the same pixel.</p>
 <p>To illustrate this flexibility, we will try three different approaches to point rasterization (<a href="#fig-rasterize-points" class="quarto-xref">Figure&nbsp;<span>5.6</span></a> (b)-(d)). First, we create a raster representing the presence or absence of cycle hire points (known as presence/absence rasters). In this case, we transfer the value of <code>1</code> to all pixels where at least one point falls in. In the <strong>rasterio</strong> framework, we use the <code>rasterio.features.rasterize</code> function, which requires an iterable object of <code>geometry,value</code> pairs. In this first example, we transform the point <code>GeoDataFrame</code> into a <code>list</code> of <code>shapely</code> geometries and the (fixed) value of <code>1</code>, using list comprehension as follows. The first five elements of the <code>list</code> are hereby printed to illustrate its structure.</p>
-<div id="bdd9d4f6" class="cell" data-execution_count="38">
+<div id="7bcbeb90" class="cell" data-execution_count="38">
 <div class="sourceCode cell-code" id="cb49"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>g <span class="op">=</span> [(g, <span class="dv">1</span>) <span class="cf">for</span> g <span class="kw">in</span> cycle_hire_osm_projected.geometry]</span>
 <span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a>g[:<span class="dv">5</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="38">
@@ -1115,7 +1115,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"
 </div>
 </div>
 <p>The list of <code>geometry,value</code> pairs is passed to <code>rasterio.features.rasterize</code>, along with the <code>out_shape</code> and <code>transform</code> which define the raster template. The result <code>ch_raster1</code> is an <code>ndarray</code> with the burned values of <code>1</code> where the pixel coincides with at least one point, and <code>0</code> in ‘unaffected’ pixels. Note that <code>merge_alg=rasterio.enums.MergeAlg.replace</code> (the default) is used here, which means that a pixel get <code>1</code> when one or more points fall in it, or keeps the original <code>0</code> value otherwise.</p>
-<div id="95351dfa" class="cell" data-execution_count="39">
+<div id="25c5afe2" class="cell" data-execution_count="39">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>ch_raster1 <span class="op">=</span> rasterio.features.rasterize(</span>
 <span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a>    shapes<span class="op">=</span>g,</span>
 <span id="cb51-3"><a href="#cb51-3" aria-hidden="true" tabindex="-1"></a>    out_shape<span class="op">=</span>shape, </span>
@@ -1137,7 +1137,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"
 </div>
 </div>
 <p>In our second variant of point rasterization, we count the number of bike hire stations. To do that, we use the fixed value of <code>1</code> (same as in the last example), but this time combined with the <code>merge_alg=rasterio.enums.MergeAlg.add</code> argument. That way, multiple values burned into the same pixel are <em>summed</em>, rather than replaced keeping last (which is the default). The new output, <code>ch_raster2</code>, shows the number of cycle hire points in each grid cell.</p>
-<div id="e1028daf" class="cell" data-execution_count="40">
+<div id="038c93b0" class="cell" data-execution_count="40">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>g <span class="op">=</span> [(g, <span class="dv">1</span>) <span class="cf">for</span> g <span class="kw">in</span> cycle_hire_osm_projected.geometry]</span>
 <span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a>ch_raster2 <span class="op">=</span> rasterio.features.rasterize(</span>
 <span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a>    shapes<span class="op">=</span>g,</span>
@@ -1162,7 +1162,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"
 </div>
 </div>
 <p>The cycle hire locations have different numbers of bicycles described by the capacity variable, raising the question, what is the capacity in each grid cell? To calculate that, in our third point rasterization variant we sum the field (<code>'capacity'</code>) rather than the fixed values of <code>1</code>. This requires using a more complex list comprehension expression, where we also (1) extract both geometries and the attribute of interest, and (2) filter out ‘No Data’ values, which can be done as follows. You are invited to run the separate parts to see how this works; the important point is that, in the end, we get the list <code>g</code> with the <code>geometry,value</code> pairs to be burned, only that the <code>value</code> is now variable, rather than fixed, among points.</p>
-<div id="520f0dc3" class="cell" data-execution_count="41">
+<div id="73d9b1a1" class="cell" data-execution_count="41">
 <div class="sourceCode cell-code" id="cb55"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>g <span class="op">=</span> [(g, v) <span class="cf">for</span> g, v <span class="kw">in</span> cycle_hire_osm_projected[[<span class="st">'geometry'</span>, <span class="st">'capacity'</span>]] <span class="op">\</span></span>
 <span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a>        .dropna(subset<span class="op">=</span><span class="st">'capacity'</span>)</span>
 <span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a>        .to_numpy() <span class="op">\</span></span>
@@ -1177,7 +1177,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"
 </div>
 </div>
 <p>Now we rasterize the points, again using <code>merge_alg=rasterio.enums.MergeAlg.add</code> to sum the capacity values per pixel.</p>
-<div id="293ad0e7" class="cell" data-execution_count="42">
+<div id="36b85da2" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb57"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>ch_raster3 <span class="op">=</span> rasterio.features.rasterize(</span>
 <span id="cb57-2"><a href="#cb57-2" aria-hidden="true" tabindex="-1"></a>    shapes<span class="op">=</span>g,</span>
 <span id="cb57-3"><a href="#cb57-3" aria-hidden="true" tabindex="-1"></a>    out_shape<span class="op">=</span>shape,</span>
@@ -1289,7 +1289,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="sec-rasterizing-points"
 <section id="sec-rasterizing-lines-and-polygons" class="level3" data-number="5.4.2">
 <h3 data-number="5.4.2" class="anchored" data-anchor-id="sec-rasterizing-lines-and-polygons"><span class="header-section-number">5.4.2</span> Rasterizing lines and polygons</h3>
 <p>Another dataset based on California’s polygons and borders (created below) illustrates rasterization of lines. There are three preliminary steps. First, we subset the California polygon.</p>
-<div id="36bb2b47" class="cell" data-execution_count="44">
+<div id="4fc43c2c" class="cell" data-execution_count="44">
 <div class="sourceCode cell-code" id="cb60"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb60-1"><a href="#cb60-1" aria-hidden="true" tabindex="-1"></a>california <span class="op">=</span> us_states[us_states[<span class="st">'NAME'</span>] <span class="op">==</span> <span class="st">'California'</span>]</span>
 <span id="cb60-2"><a href="#cb60-2" aria-hidden="true" tabindex="-1"></a>california</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="44">
@@ -1324,7 +1324,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="sec-rasterizing-lines-a
 </div>
 </div>
 <p>Second, we ‘cast’ the polygon into a <code>'MultiLineString'</code> geometry, using the <code>.boundary</code> property that <code>GeoSeries</code> and <code>DataFrame</code>s have.</p>
-<div id="2823fe8c" class="cell" data-execution_count="45">
+<div id="e0f8f19c" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb61"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a>california_borders <span class="op">=</span> california.boundary</span>
 <span id="cb61-2"><a href="#cb61-2" aria-hidden="true" tabindex="-1"></a>california_borders</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="45">
@@ -1333,7 +1333,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="sec-rasterizing-lines-a
 </div>
 </div>
 <p>Third, we create the <code>transform</code> and <code>shape</code> describing our template raster, with a resolution of <code>0.5</code> degree, using the same approach as in <a href="#sec-rasterizing-points" class="quarto-xref"><span>Section 5.4.1</span></a>.</p>
-<div id="2aa73ef4" class="cell" data-execution_count="46">
+<div id="c57915c9" class="cell" data-execution_count="46">
 <div class="sourceCode cell-code" id="cb63"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a>bounds <span class="op">=</span> california_borders.total_bounds</span>
 <span id="cb63-2"><a href="#cb63-2" aria-hidden="true" tabindex="-1"></a>res <span class="op">=</span> <span class="fl">0.5</span></span>
 <span id="cb63-3"><a href="#cb63-3" aria-hidden="true" tabindex="-1"></a>transform <span class="op">=</span> rasterio.transform.from_origin(</span>
@@ -1351,7 +1351,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="sec-rasterizing-lines-a
 </div>
 </div>
 <p>Finally, we rasterize <code>california_borders</code> based on the calculated template’s <code>shape</code> and <code>transform</code>. When considering line or polygon rasterization, one useful additional argument is <code>all_touched</code>. By default it is <code>False</code>, but when changed to <code>True</code>—all cells that are touched by a line or polygon border get a value. Line rasterization with <code>all_touched=True</code> is demonstrated in the code below (<a href="#fig-rasterize-lines-polygons" class="quarto-xref">Figure&nbsp;<span>5.7</span></a>, left). We are also using <code>fill=np.nan</code> to set ‘background’ values to ‘No Data’.</p>
-<div id="54521932" class="cell" data-execution_count="47">
+<div id="f66badd0" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb65"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a>california_raster1 <span class="op">=</span> rasterio.features.rasterize(</span>
 <span id="cb65-2"><a href="#cb65-2" aria-hidden="true" tabindex="-1"></a>    [(g, <span class="dv">1</span>) <span class="cf">for</span> g <span class="kw">in</span> california_borders],</span>
 <span id="cb65-3"><a href="#cb65-3" aria-hidden="true" tabindex="-1"></a>    out_shape<span class="op">=</span>shape,</span>
@@ -1362,7 +1362,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="sec-rasterizing-lines-a
 <span id="cb65-8"><a href="#cb65-8" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Compare it to polygon rasterization, with <code>all_touched=False</code> (the default), which selects only raster cells whose centroids are inside the selector polygon, as illustrated in <a href="#fig-rasterize-lines-polygons" class="quarto-xref">Figure&nbsp;<span>5.7</span></a> (right).</p>
-<div id="e4eaf3f3" class="cell" data-execution_count="48">
+<div id="6d220508" class="cell" data-execution_count="48">
 <div class="sourceCode cell-code" id="cb66"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a>california_raster2 <span class="op">=</span> rasterio.features.rasterize(</span>
 <span id="cb66-2"><a href="#cb66-2" aria-hidden="true" tabindex="-1"></a>    [(g, <span class="dv">1</span>) <span class="cf">for</span> g <span class="kw">in</span> california.geometry],</span>
 <span id="cb66-3"><a href="#cb66-3" aria-hidden="true" tabindex="-1"></a>    out_shape<span class="op">=</span>shape,</span>
@@ -1372,7 +1372,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="sec-rasterizing-lines-a
 <span id="cb66-7"><a href="#cb66-7" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>To illustrate which raster pixels are actually selected as part of rasterization, we also show them as points. This also requires the following code section to calculate the points, which we explain in <a href="#sec-spatial-vectorization" class="quarto-xref"><span>Section 5.5</span></a>.</p>
-<div id="ed0bc63d" class="cell" data-execution_count="49">
+<div id="4d46f6d2" class="cell" data-execution_count="49">
 <div class="sourceCode cell-code" id="cb67"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>height <span class="op">=</span> california_raster1.shape[<span class="dv">0</span>]</span>
 <span id="cb67-2"><a href="#cb67-2" aria-hidden="true" tabindex="-1"></a>width <span class="op">=</span> california_raster1.shape[<span class="dv">1</span>]</span>
 <span id="cb67-3"><a href="#cb67-3" aria-hidden="true" tabindex="-1"></a>cols, rows <span class="op">=</span> np.meshgrid(np.arange(width), np.arange(height))</span>
@@ -1499,19 +1499,19 @@ <h2 data-number="5.5" class="anchored" data-anchor-id="sec-spatial-vectorization
 <h3 data-number="5.5.1" class="anchored" data-anchor-id="sec-raster-to-polygons"><span class="header-section-number">5.5.1</span> Raster to polygons</h3>
 <p>The <code>rasterio.features.shapes</code> gives access to raster pixels as polygon geometries, along with the associated raster values. The returned object is a generator (see note in <a href="03-spatial-operations.html#sec-spatial-subsetting-raster" class="quarto-xref"><span>Section 3.3.1</span></a>), yielding <code>geometry,value</code> pairs.</p>
 <p>For example, the following expression returns a generator named <code>shapes</code>, referring to the pixel polygons.</p>
-<div id="4a8273f1" class="cell" data-execution_count="51">
+<div id="969ec8ad" class="cell" data-execution_count="51">
 <div class="sourceCode cell-code" id="cb69"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a>shapes <span class="op">=</span> rasterio.features.shapes(rasterio.band(src_grain, <span class="dv">1</span>) )</span>
 <span id="cb69-2"><a href="#cb69-2" aria-hidden="true" tabindex="-1"></a>shapes</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="51">
-<pre><code>&lt;generator object shapes at 0x7fca98e13cd0&gt;</code></pre>
+<pre><code>&lt;generator object shapes at 0x7f240b533cd0&gt;</code></pre>
 </div>
 </div>
 <p>We can generate all shapes at once into a <code>list</code> named <code>pol</code> with <code>list(shapes)</code>.</p>
-<div id="8f7222b0" class="cell" data-execution_count="52">
+<div id="29c5862b" class="cell" data-execution_count="52">
 <div class="sourceCode cell-code" id="cb71"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" tabindex="-1"></a>pol <span class="op">=</span> <span class="bu">list</span>(shapes)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Each element in <code>pol</code> is a <code>tuple</code> of length 2, containing the GeoJSON-like <code>dict</code>—representing the polygon geometry and the value of the pixel(s) which comprise the polygon. For example, here is the first element of <code>pol</code>.</p>
-<div id="325da1a4" class="cell" data-execution_count="53">
+<div id="5f4a2f4c" class="cell" data-execution_count="53">
 <div class="sourceCode cell-code" id="cb72"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb72-1"><a href="#cb72-1" aria-hidden="true" tabindex="-1"></a>pol[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="53">
 <pre><code>({'type': 'Polygon',
@@ -1537,7 +1537,7 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="sec-raster-to-polygons"
 </div>
 </div>
 <p>To transform the <code>list</code> coming out of <code>rasterio.features.shapes</code> into the familiar <code>GeoDataFrame</code>, we need few more steps of data reshaping. First, we apply the <code>shapely.geometry.shape</code> function to go from a <code>list</code> of GeoJSON-like <code>dict</code>s to a <code>list</code> of <code>shapely</code> geometry objects. The <code>list</code> can then be converted to a <code>GeoSeries</code> (see <a href="01-spatial-data.html#sec-vector-layer-from-scratch" class="quarto-xref"><span>Section 1.2.6</span></a>).</p>
-<div id="75e23763" class="cell" data-execution_count="54">
+<div id="611af29e" class="cell" data-execution_count="54">
 <div class="sourceCode cell-code" id="cb74"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb74-1"><a href="#cb74-1" aria-hidden="true" tabindex="-1"></a>geom <span class="op">=</span> [shapely.geometry.shape(i[<span class="dv">0</span>]) <span class="cf">for</span> i <span class="kw">in</span> pol]</span>
 <span id="cb74-2"><a href="#cb74-2" aria-hidden="true" tabindex="-1"></a>geom <span class="op">=</span> gpd.GeoSeries(geom, crs<span class="op">=</span>src_grain.crs)</span>
 <span id="cb74-3"><a href="#cb74-3" aria-hidden="true" tabindex="-1"></a>geom</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1553,7 +1553,7 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="sec-raster-to-polygons"
 </div>
 </div>
 <p>The values can also be extracted from the <code>rasterio.features.shapes</code> result and turned into a corresponding <code>Series</code>.</p>
-<div id="921d3728" class="cell" data-execution_count="55">
+<div id="1bc051a1" class="cell" data-execution_count="55">
 <div class="sourceCode cell-code" id="cb76"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb76-1"><a href="#cb76-1" aria-hidden="true" tabindex="-1"></a>values <span class="op">=</span> [i[<span class="dv">1</span>] <span class="cf">for</span> i <span class="kw">in</span> pol]</span>
 <span id="cb76-2"><a href="#cb76-2" aria-hidden="true" tabindex="-1"></a>values <span class="op">=</span> pd.Series(values)</span>
 <span id="cb76-3"><a href="#cb76-3" aria-hidden="true" tabindex="-1"></a>values</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1569,7 +1569,7 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="sec-raster-to-polygons"
 </div>
 </div>
 <p>Finally, the two can be combined into a <code>GeoDataFrame</code>, hereby named <code>result</code>.</p>
-<div id="3407ca95" class="cell" data-execution_count="56">
+<div id="103fe0ce" class="cell" data-execution_count="56">
 <div class="sourceCode cell-code" id="cb78"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb78-1"><a href="#cb78-1" aria-hidden="true" tabindex="-1"></a>result <span class="op">=</span> gpd.GeoDataFrame({<span class="st">'value'</span>: values, <span class="st">'geometry'</span>: geom})</span>
 <span id="cb78-2"><a href="#cb78-2" aria-hidden="true" tabindex="-1"></a>result</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="56">
@@ -1648,7 +1648,7 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="sec-raster-to-polygons"
 <section id="sec-raster-to-points" class="level3" data-number="5.5.2">
 <h3 data-number="5.5.2" class="anchored" data-anchor-id="sec-raster-to-points"><span class="header-section-number">5.5.2</span> Raster to points</h3>
 <p>To transform a raster to points, we can use the <code>rasterio.transform.xy</code> function. As the name suggests, the function accepts row and column indices, and transforms them into x- and y-coordinates (using the raster’s transformation matrix). For example, the coordinates of the top-left pixel can be calculated passing the <code>(row,col)</code> indices of <code>(0,0)</code>.</p>
-<div id="692d79c4" class="cell" data-execution_count="58">
+<div id="a97e867f" class="cell" data-execution_count="58">
 <div class="sourceCode cell-code" id="cb80"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb80-1"><a href="#cb80-1" aria-hidden="true" tabindex="-1"></a>src <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/elev.tif'</span>)</span>
 <span id="cb80-2"><a href="#cb80-2" aria-hidden="true" tabindex="-1"></a>rasterio.transform.xy(src.transform, <span class="dv">0</span>, <span class="dv">0</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="58">
@@ -1666,7 +1666,7 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="sec-raster-to-points"><
 </div>
 <div class="callout-body-container callout-body">
 <p>Keep in mind that the coordinates of the top-left pixel (<code>(-1.25, 1.25)</code>), as calculated in the above expression, refer to the pixel <em>centroid</em>. Therefore, they are not identical to the raster origin coordinates (<code>(-1.5,1.5)</code>), as specified in the transformation matrix, which are the coordinates of the top-left edge/corner of the raster (see <a href="#fig-raster-to-points" class="quarto-xref">Figure&nbsp;<span>5.9</span></a>).</p>
-<div id="21e07ef7" class="cell" data-execution_count="59">
+<div id="4aeea795" class="cell" data-execution_count="59">
 <div class="sourceCode cell-code" id="cb82"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb82-1"><a href="#cb82-1" aria-hidden="true" tabindex="-1"></a>src.transform</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="59">
 <pre><code>Affine(0.5, 0.0, -1.5,
@@ -1676,13 +1676,13 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="sec-raster-to-points"><
 </div>
 </div>
 <p>To generalize the above expression to calculate the coordinates of <em>all</em> pixels, we first need to generate a grid of all possible row/column index combinations. This can be done using <code>np.meshgrid</code>, as follows.</p>
-<div id="6ebbd415" class="cell" data-execution_count="60">
+<div id="4519c399" class="cell" data-execution_count="60">
 <div class="sourceCode cell-code" id="cb84"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb84-1"><a href="#cb84-1" aria-hidden="true" tabindex="-1"></a>height <span class="op">=</span> src.shape[<span class="dv">0</span>]</span>
 <span id="cb84-2"><a href="#cb84-2" aria-hidden="true" tabindex="-1"></a>width <span class="op">=</span> src.shape[<span class="dv">1</span>]</span>
 <span id="cb84-3"><a href="#cb84-3" aria-hidden="true" tabindex="-1"></a>cols, rows <span class="op">=</span> np.meshgrid(np.arange(width), np.arange(height))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We now have two arrays, <code>rows</code> and <code>cols</code>, matching the shape of <code>elev.tif</code> and containing the corresponding row and column indices.</p>
-<div id="b1122bfa" class="cell" data-execution_count="61">
+<div id="490a1dac" class="cell" data-execution_count="61">
 <div class="sourceCode cell-code" id="cb85"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb85-1"><a href="#cb85-1" aria-hidden="true" tabindex="-1"></a>rows</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="61">
 <pre><code>array([[0, 0, 0, 0, 0, 0],
@@ -1693,7 +1693,7 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="sec-raster-to-points"><
        [5, 5, 5, 5, 5, 5]])</code></pre>
 </div>
 </div>
-<div id="d74d7e0d" class="cell" data-execution_count="62">
+<div id="a69f83d4" class="cell" data-execution_count="62">
 <div class="sourceCode cell-code" id="cb87"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb87-1"><a href="#cb87-1" aria-hidden="true" tabindex="-1"></a>cols</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="62">
 <pre><code>array([[0, 1, 2, 3, 4, 5],
@@ -1705,10 +1705,10 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="sec-raster-to-points"><
 </div>
 </div>
 <p>These can be passed to <code>rasterio.transform.xy</code> to transform the indices into point coordinates, accordingly stored in lists of arrays <code>x</code> and <code>y</code>.</p>
-<div id="78ec9c9e" class="cell" data-execution_count="63">
+<div id="4ddaa457" class="cell" data-execution_count="63">
 <div class="sourceCode cell-code" id="cb89"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb89-1"><a href="#cb89-1" aria-hidden="true" tabindex="-1"></a>x, y <span class="op">=</span> rasterio.transform.xy(src.transform, rows, cols)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="a5fde711" class="cell" data-execution_count="64">
+<div id="b7d30d0b" class="cell" data-execution_count="64">
 <div class="sourceCode cell-code" id="cb90"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb90-1"><a href="#cb90-1" aria-hidden="true" tabindex="-1"></a>x</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="64">
 <pre><code>[array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),
@@ -1719,7 +1719,7 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="sec-raster-to-points"><
  array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25])]</code></pre>
 </div>
 </div>
-<div id="c7d99036" class="cell" data-execution_count="65">
+<div id="698aa83e" class="cell" data-execution_count="65">
 <div class="sourceCode cell-code" id="cb92"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a>y</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="65">
 <pre><code>[array([1.25, 1.25, 1.25, 1.25, 1.25, 1.25]),
@@ -1731,7 +1731,7 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="sec-raster-to-points"><
 </div>
 </div>
 <p>Typically we want to work with the points in the form of a <code>GeoDataFrame</code> which also holds the attribute(s) value(s) as point attributes. To get there, we can transform the coordinates as well as any attributes to 1-dimensional arrays, and then use methods we are already familiar with (<a href="01-spatial-data.html#sec-vector-layer-from-scratch" class="quarto-xref"><span>Section 1.2.6</span></a>) to combine them into a <code>GeoDataFrame</code>.</p>
-<div id="2f91d5b4" class="cell" data-execution_count="66">
+<div id="7050e0f0" class="cell" data-execution_count="66">
 <div class="sourceCode cell-code" id="cb94"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb94-1"><a href="#cb94-1" aria-hidden="true" tabindex="-1"></a>x <span class="op">=</span> np.array(x).flatten()</span>
 <span id="cb94-2"><a href="#cb94-2" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> np.array(y).flatten()</span>
 <span id="cb94-3"><a href="#cb94-3" aria-hidden="true" tabindex="-1"></a>z <span class="op">=</span> src.read(<span class="dv">1</span>).flatten()</span>
@@ -1873,12 +1873,12 @@ <h3 data-number="5.5.3" class="anchored" data-anchor-id="sec-raster-to-contours"
 <li>Writing a custom function to export contour coordinates generated by, e.g., <strong>matplotlib</strong> or <strong>skimage</strong></li>
 </ol>
 <p>We demonstrate the first approach, using <code>gdal_contour</code>. Although we deviate from the Python-focused approach towards more direct interaction with GDAL, the benefit of <code>gdal_contour</code> is the proven algorithm, customized to spatial data, and with many relevant options. Both the <code>gdal_contour</code> program (along with other GDAL programs) and its <strong>osgeo</strong> Python wrapper, should already be installed on your system since GDAL is a dependency of <strong>rasterio</strong>. Using the command line pathway, generating 50 <span class="math inline">\(m\)</span> contours of the <code>dem.tif</code> file can be done as follows.</p>
-<div id="a2e45e20" class="cell" data-execution_count="69">
+<div id="140b2cd2" class="cell" data-execution_count="69">
 <div class="sourceCode cell-code" id="cb97"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb97-1"><a href="#cb97-1" aria-hidden="true" tabindex="-1"></a>os.system(<span class="st">'gdal_contour -a elev data/dem.tif output/dem_contour.gpkg -i 50.0'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Like all GDAL programs (also see <code>gdaldem</code> example in <a href="03-spatial-operations.html#sec-focal-operations" class="quarto-xref"><span>Section 3.3.4</span></a>), <code>gdal_contour</code> works with files. Here, the input is the <code>data/dem.tif</code> file and the result is exported to the <code>output/dem_contour.gpkg</code> file.</p>
 <p>To illustrate the result, let’s read the resulting <code>dem_contour.gpkg</code> layer back into the Python environment. Note that the layer contains an attribute named <code>'elev'</code> (as specified using <code>-a elev</code>) with the contour elevation values.</p>
-<div id="33a1d5f7" class="cell" data-execution_count="70">
+<div id="e4983b92" class="cell" data-execution_count="70">
 <div class="sourceCode cell-code" id="cb98"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb98-1"><a href="#cb98-1" aria-hidden="true" tabindex="-1"></a>contours1 <span class="op">=</span> gpd.read_file(<span class="st">'output/dem_contour.gpkg'</span>)</span>
 <span id="cb98-2"><a href="#cb98-2" aria-hidden="true" tabindex="-1"></a>contours1</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="69">
@@ -1985,7 +1985,7 @@ <h2 data-number="5.6" class="anchored" data-anchor-id="sec-distance-to-nearest-g
 </div>
 </div>
 <p>For a ‘template’ raster, we will aggregate the New Zealand DEM, in the <code>nz_elev.tif</code> file, to 5 times coarser resolution. The code section below follows the aggregation example in <a href="04-geometry-operations.html#sec-raster-agg-disagg" class="quarto-xref"><span>Section 4.3.2</span></a>.</p>
-<div id="848c4ce3" class="cell" data-execution_count="73">
+<div id="8cf2a26e" class="cell" data-execution_count="73">
 <div class="sourceCode cell-code" id="cb101"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb101-1"><a href="#cb101-1" aria-hidden="true" tabindex="-1"></a>factor <span class="op">=</span> <span class="fl">0.2</span></span>
 <span id="cb101-2"><a href="#cb101-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Reading aggregated array</span></span>
 <span id="cb101-3"><a href="#cb101-3" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> src_nz_elev.read(<span class="dv">1</span>,</span>
@@ -2020,7 +2020,7 @@ <h2 data-number="5.6" class="anchored" data-anchor-id="sec-distance-to-nearest-g
 </div>
 </div>
 <p>To calculate the actual distances, we must convert each pixel to a vector (point) geometry. For this purpose, we use the technique demonstrated in <a href="#sec-raster-to-points" class="quarto-xref"><span>Section 5.5.2</span></a>, but we’re keeping the points as a <code>list</code> of <code>shapely</code> geometries, rather than a <code>GeoDataFrame</code>, since such a list is sufficient for the subsequent calculation.</p>
-<div id="bd6bc5c8" class="cell" data-execution_count="75">
+<div id="d450a7bc" class="cell" data-execution_count="75">
 <div class="sourceCode cell-code" id="cb103"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb103-1"><a href="#cb103-1" aria-hidden="true" tabindex="-1"></a>height <span class="op">=</span> r.shape[<span class="dv">0</span>]</span>
 <span id="cb103-2"><a href="#cb103-2" aria-hidden="true" tabindex="-1"></a>width <span class="op">=</span> r.shape[<span class="dv">1</span>]</span>
 <span id="cb103-3"><a href="#cb103-3" aria-hidden="true" tabindex="-1"></a>cols, rows <span class="op">=</span> np.meshgrid(np.arange(width), np.arange(height))</span>
@@ -2043,7 +2043,7 @@ <h2 data-number="5.6" class="anchored" data-anchor-id="sec-distance-to-nearest-g
 </div>
 <p>The result <code>geom</code> is a <code>list</code> of <code>shapely</code> geometries, representing raster cell centroids (excluding <code>np.nan</code> pixels, which were filtered out).</p>
 <p>Now we can calculate the corresponding <code>list</code> of point geometries and associated distances, using the <code>.distance</code> method from <strong>shapely</strong>:</p>
-<div id="e462f61d" class="cell" data-execution_count="76">
+<div id="61c4879c" class="cell" data-execution_count="76">
 <div class="sourceCode cell-code" id="cb105"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb105-1"><a href="#cb105-1" aria-hidden="true" tabindex="-1"></a>distances <span class="op">=</span> [(i, i.distance(coastline)) <span class="cf">for</span> i <span class="kw">in</span> geom]</span>
 <span id="cb105-2"><a href="#cb105-2" aria-hidden="true" tabindex="-1"></a>distances[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="75">
@@ -2051,7 +2051,7 @@ <h2 data-number="5.6" class="anchored" data-anchor-id="sec-distance-to-nearest-g
 </div>
 </div>
 <p>Finally, we rasterize (see <a href="#sec-rasterizing-points" class="quarto-xref"><span>Section 5.4.1</span></a>) the distances into our raster template.</p>
-<div id="6e9ebeee" class="cell" data-execution_count="77">
+<div id="612c31d3" class="cell" data-execution_count="77">
 <div class="sourceCode cell-code" id="cb107"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb107-1"><a href="#cb107-1" aria-hidden="true" tabindex="-1"></a>image <span class="op">=</span> rasterio.features.rasterize(</span>
 <span id="cb107-2"><a href="#cb107-2" aria-hidden="true" tabindex="-1"></a>    distances,</span>
 <span id="cb107-3"><a href="#cb107-3" aria-hidden="true" tabindex="-1"></a>    out_shape<span class="op">=</span>r.shape,</span>
diff --git a/06-reproj.html b/06-reproj.html
index 8f4e64f4..bd964b8f 100644
--- a/06-reproj.html
+++ b/06-reproj.html
@@ -352,7 +352,7 @@ <h1 class="title"><span id="sec-reproj-geo-data" class="quarto-section-identifie
 <section id="prerequisites" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2>
 <p>This chapter requires importing the following packages:</p>
-<div id="050ce21e" class="cell" data-execution_count="3">
+<div id="857599a0" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> shutil</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> math</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
@@ -365,7 +365,7 @@ <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2
 <span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio.warp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>It also relies on the following data files:</p>
-<div id="afbb5ebc" class="cell" data-execution_count="4">
+<div id="2d5d7dd1" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>src_srtm <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/srtm.tif'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>src_nlcd <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/nlcd.tif'</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>zion <span class="op">=</span> gpd.read_file(<span class="st">'data/zion.gpkg'</span>)</span>
@@ -390,7 +390,7 @@ <h2 data-number="6.2" class="anchored" data-anchor-id="sec-coordinate-reference-
 <p>Each refers to the same thing: the ‘WGS84’ coordinate system that forms the basis of Global Positioning System (GPS) coordinates and many other datasets. But which one is correct?</p>
 <p>The short answer is that the third way to identify CRSs is correct: <code>EPSG:4326</code> is understood by <strong>geopandas</strong> and <strong>rasterio</strong> packages covered in this book, plus many other software projects for working with geographic data including QGIS and PROJ. <code>EPSG:4326</code> is future-proof. Furthermore, although it is machine readable, unlike the proj-string representation <code>EPSG:4326</code> is short, easy to remember and highly ‘findable’ online (searching for <code>EPSG:4326</code> yields a dedicated page on the website epsg.io<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>, for example). The more concise identifier <code>4326</code> is also understood by <strong>geopandas</strong> and <strong>rasterio</strong>.</p>
 <p>The longer answer is that none of the three descriptions is sufficient, and more detail is needed for unambiguous CRS handling and transformations: due to the complexity of CRSs, it is not possible to capture all relevant information about them in such short text strings. For this reason, the Open Geospatial Consortium (OGC, which also developed the Simple Features specification that the <strong>geopandas</strong> package implements) developed an open standard format for describing CRSs that is called WKT (Well Known Text). This is detailed in a 100+ page document that ‘defines the structure and content of a text string implementation of the abstract model for coordinate reference systems described in ISO 19111:2019’ <span class="citation" data-cites="opengeospatialconsortium_wellknown_2019">(<a href="references.html#ref-opengeospatialconsortium_wellknown_2019" role="doc-biblioref">Open Geospatial Consortium 2019</a>)</span>. The WKT representation of the WGS84 CRS, which has the identifier <code>EPSG:4326</code> is as follows.</p>
-<div id="43e900c1" class="cell" data-execution_count="5">
+<div id="e02a8323" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>crs <span class="op">=</span> pyproj.CRS.from_string(<span class="st">'EPSG:4326'</span>) <span class="co"># or '.from_epsg(4326)'</span></span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(crs.to_wkt(pretty<span class="op">=</span><span class="va">True</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -427,7 +427,7 @@ <h2 data-number="6.2" class="anchored" data-anchor-id="sec-coordinate-reference-
 <p>Should any attributes or values given in the cited identifier be in conflict with attributes or values given explicitly in the WKT description, the WKT values shall prevail.</p>
 </blockquote>
 <p>The convention of referring to CRSs identifiers in the form <code>AUTHORITY:CODE</code> allows a wide range of formally defined coordinate systems to be referred to. The most commonly used authority in CRS identifiers is EPSG, an acronym for the European Petroleum Survey Group which published a standardized list of CRSs. Other authorities can be used in CRS identifiers. <code>ESRI:54030</code>, for example, refers to ESRI’s implementation of the Robinson projection, which has the following WKT string.</p>
-<div id="9961ceeb" class="cell" data-execution_count="6">
+<div id="7f866cf1" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>crs <span class="op">=</span> pyproj.CRS.from_string(<span class="st">'ESRI:54030'</span>)</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(crs.to_wkt(pretty<span class="op">=</span><span class="va">True</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -482,7 +482,7 @@ <h2 data-number="6.2" class="anchored" data-anchor-id="sec-coordinate-reference-
 <section id="sec-querying-and-setting-coordinate-systems" class="level2" data-number="6.3">
 <h2 data-number="6.3" class="anchored" data-anchor-id="sec-querying-and-setting-coordinate-systems"><span class="header-section-number">6.3</span> Querying and setting coordinate systems</h2>
 <p>Let’s see how CRSs are stored in Python spatial objects and how they can be queried and set. First, we will look at getting and setting CRSs in vector geographic data objects. Consider the <code>GeoDataFrame</code> object named <code>world</code>, imported from a file <code>world.gpkg</code> that represents countries worldwide. Its CRS can be retrieved using the <code>.crs</code> property.</p>
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 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>world.crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <pre><code>&lt;Geographic 2D CRS: EPSG:4326&gt;
@@ -508,44 +508,44 @@ <h2 data-number="6.3" class="anchored" data-anchor-id="sec-querying-and-setting-
 </ol>
 <p>The WKT representation, which is internally used when saving the object to a file or doing any coordinate operations, can be extracted using <code>.crs.to_wkt()</code> as shown above (<a href="#sec-coordinate-reference-systems" class="quarto-xref"><span>Section 6.2</span></a>). We can also see that the <code>world</code> object has the WGS84 ellipsoid, the latitude and longitude axis order, and uses the Greenwich prime meridian. We also have the suitable suitable area specification for the use of this CRS, and CRS identifier: <code>EPSG:4326</code>.</p>
 <p>The CRS specification object, such as <code>world.crs</code>, has several useful properties and methods to explicitly retrieve information about the used CRS. For example, we can check whether the CRS is geographic with the <code>.is_geographic</code> property.</p>
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 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>world.crs.is_geographic</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <pre><code>True</code></pre>
 </div>
 </div>
 <p>CRS units of both axes (typically identical) can be retrieved with the <code>.axis_info</code> property.</p>
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 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>world.crs.axis_info[<span class="dv">0</span>].unit_name, world.crs.axis_info[<span class="dv">1</span>].unit_name</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <pre><code>('degree', 'degree')</code></pre>
 </div>
 </div>
 <p><code>AUTHORITY</code> and <code>CODE</code> strings may be obtained with the <code>.to_authority()</code> method.</p>
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 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>world.crs.to_authority()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
 <pre><code>('EPSG', '4326')</code></pre>
 </div>
 </div>
 <p>In cases when a coordinate reference system (CRS) is missing or the wrong CRS is set, the <code>.set_crs</code> method can be used on a <code>GeoSeries</code> or a <code>GeoDataFrame</code> to set it. The CRS can be specified using an EPSG code as the first argument. In case the object already has a different CRS definition, we must also specify <code>allow_override=True</code> to replace it (otherwise we get an error). In the first example we set the <code>EPSG:4326</code> CRS, which has no effect because <code>world</code> already has that exact CRS definition, while the second example replaces the existing CRS with a new definition of <code>EPSG:3857</code>.</p>
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 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>world2 <span class="op">=</span> world.set_crs(<span class="dv">4326</span>)</span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>world3 <span class="op">=</span> world.set_crs(<span class="dv">3857</span>, allow_override<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The provided number is interpreted as an <code>EPSG</code> code. We can also use strings, as in <code>'EPSG:4326'</code>, which is useful to make the code more clear and when using other authorities than <code>EPSG</code>.</p>
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 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>world4 <span class="op">=</span> world.set_crs(<span class="st">'ESRI:54009'</span>, allow_override<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>In <strong>rasterio</strong>, the CRS information is stored as part of a raster file connection metadata (<a href="01-spatial-data.html#sec-using-rasterio" class="quarto-xref"><span>Section 1.3.1</span></a>). Replacing the CRS definition for a <strong>rasterio</strong> file connection is typically not necessary, because it is not considered in any operation; only the transformation matrix and coordinates are. One exception is when writing the raster, in which case we need to construct the metadata of the raster file to be written, and therein specify the CRS anyway (<a href="01-spatial-data.html#sec-raster-from-scratch" class="quarto-xref"><span>Section 1.3.2</span></a>). However, if we, for some reason, need to change the CRS definition in the file connection metadata, we can do that when opening the file in <code>r+</code> (reading and writing) mode. To demonstrate, we will create a copy of the <code>nlcd.tif</code> file, named <code>nlcd2.tif</code>,</p>
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 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>shutil.copy(<span class="st">'data/nlcd.tif'</span>, <span class="st">'output/nlcd_modified_crs.tif'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <pre><code>'output/nlcd_modified_crs.tif'</code></pre>
 </div>
 </div>
 <p>and examine its existing CRS.</p>
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 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>src_nlcd2 <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_modified_crs.tif'</span>, <span class="st">'r+'</span>)</span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>src_nlcd2.crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -566,12 +566,12 @@ <h2 data-number="6.3" class="anchored" data-anchor-id="sec-querying-and-setting-
 </div>
 </div>
 <p>To replace the definition with a new one, such as <code>EPSG:3857</code>, we can use the <code>.crs</code> method, as shown below.</p>
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 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>src_nlcd2.crs <span class="op">=</span> <span class="dv">3857</span></span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>src_nlcd2.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Next, examining the file connection demonstrates that the CRS was indeed changed.</p>
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 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_modified_crs.tif'</span>).crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="16">
 <pre><code>CRS.from_epsg(3857)</code></pre>
@@ -579,7 +579,7 @@ <h2 data-number="6.3" class="anchored" data-anchor-id="sec-querying-and-setting-
 </div>
 <p>Importantly, the <code>.set_crs</code> (for vector layers) or the assignment to <code>.crs</code> (for rasters), as shown above, do not alter coordinates’ values or geometries. Their role is only to set a metadata information about the object CRS. Consequently, the objects we created, <code>world3</code>, <code>world4</code>, and <code>src_nlcd2</code> are ‘incorrect’, in the sense that the geometries are in fact given in a different CRS than specified in the associated CRS definition.</p>
 <p>In some cases, the CRS of a geographic object is unknown, as is the case in the London dataset created in the code chunk below, building on the example of London introduced in <a href="01-spatial-data.html#sec-vector-layer-from-scratch" class="quarto-xref"><span>Section 1.2.6</span></a>.</p>
-<div id="1b54ba8a" class="cell" data-execution_count="17">
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 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>lnd_point <span class="op">=</span> shapely.Point(<span class="op">-</span><span class="fl">0.1</span>, <span class="fl">51.5</span>)</span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>lnd_geom <span class="op">=</span> gpd.GeoSeries([lnd_point])</span>
 <span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>lnd_layer <span class="op">=</span> gpd.GeoDataFrame({<span class="st">'geometry'</span>: lnd_geom})</span>
@@ -607,12 +607,12 @@ <h2 data-number="6.3" class="anchored" data-anchor-id="sec-querying-and-setting-
 </div>
 </div>
 <p>Querying the <code>.crs</code> of such a layer returns <code>None</code>, therefore nothing is printed.</p>
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 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>lnd_layer.crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>This implies that <strong>geopandas</strong> does not know what the CRS is and is unwilling to guess. Unless a CRS is manually specified or is loaded from a source that has CRS metadata, <strong>geopandas</strong> does not make any explicit assumptions about which coordinate systems, other than to say ‘I don’t know’. This behavior makes sense given the diversity of available CRSs but differs from some approaches, such as the GeoJSON file format specification, which makes the simplifying assumption that all coordinates have a lon/lat CRS: <code>EPSG:4326</code>.</p>
 <p>A CRS can be added to <code>GeoSeries</code> or <code>GeoDataFrame</code> objects using the <code>.set_crs</code> method, as mentioned above.</p>
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 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>lnd_layer <span class="op">=</span> lnd_layer.set_crs(<span class="dv">4326</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>When working with <strong>geopandas</strong> and <strong>rasterio</strong>, datasets without a specified CRS are not an issue in most workflows, since only the coordinates are considered. It is up to the user to make sure that, when working with more than one layer, all of the coordinates are given in the same CRS (whether specified or not). When exporting the results, though, it is important to keep the CRS definition in place, because other software typically <em>do</em> use, and require, the CRS definition in calculation. It should also be mentioned that, in some cases the CRS specification is left unspecified on purpose, for example when working with layers in arbitrary or non-geographic space (simulations, internal building plans, analysis of plot-scale ecological patterns, etc.).</p>
@@ -627,7 +627,7 @@ <h2 data-number="6.4" class="anchored" data-anchor-id="sec-geometry-operations-o
 </ul>
 <p>For example, to calculate a buffer of 100 <span class="math inline">\(km\)</span> around London, we need to work with a layer representing London in a projected CRS (e.g., <code>EPSG:27700</code>) and pass the distance value in the CRS units (e.g., <code>100000</code> <span class="math inline">\(m\)</span>).</p>
 <p>In the following code chunk we create, from scratch, a point layer <code>lnd_layer_proj</code> with a point representing London (compare it to <code>lnd_layer</code>, in a geographical CRS which we created above, see <a href="#sec-querying-and-setting-coordinate-systems" class="quarto-xref"><span>Section 6.3</span></a>).</p>
-<div id="a70ae941" class="cell" data-execution_count="20">
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 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>lnd_point_proj <span class="op">=</span> shapely.Point(<span class="dv">530000</span>, <span class="dv">180000</span>)</span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>lnd_geom_proj <span class="op">=</span> gpd.GeoSeries([lnd_point_proj], crs<span class="op">=</span><span class="dv">27700</span>)</span>
 <span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>lnd_layer_proj <span class="op">=</span> gpd.GeoDataFrame({<span class="st">'geometry'</span>: lnd_geom_proj})</span>
@@ -655,7 +655,7 @@ <h2 data-number="6.4" class="anchored" data-anchor-id="sec-geometry-operations-o
 </div>
 </div>
 <p>Now, we can use the <code>.buffer</code> method (<a href="04-geometry-operations.html#sec-buffers" class="quarto-xref"><span>Section 4.2.3</span></a>) to calculate the buffer of 100 <span class="math inline">\(km\)</span> around London.</p>
-<div id="aa83fd83" class="cell" data-execution_count="21">
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 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>lnd_layer_proj_buff <span class="op">=</span> lnd_layer_proj.<span class="bu">buffer</span>(<span class="dv">100000</span>)</span>
 <span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>lnd_layer_proj_buff</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -665,7 +665,7 @@ <h2 data-number="6.4" class="anchored" data-anchor-id="sec-geometry-operations-o
 </div>
 <p>The resulting buffer is shown in the left panel of <a href="#fig-reprojection-geo-proj" class="quarto-xref">Figure&nbsp;<span>6.1</span></a>.</p>
 <p>Calculating a 100-<span class="math inline">\(km\)</span> buffer directly for <code>lnd_layer</code>, which is in a geographical CRS, is impossible. Since the <code>lnd_layer</code> is in decimal degrees, the closest thing to a 100-<span class="math inline">\(km\)</span> buffer would be to use a distance of 1 degree, which is roughly equivalent to 100 <span class="math inline">\(km\)</span> (1 degree is about 111 <span class="math inline">\(km\)</span> at the equator):</p>
-<div id="e6e314eb" class="cell" data-execution_count="22">
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 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>lnd_layer_buff <span class="op">=</span> lnd_layer.<span class="bu">buffer</span>(<span class="dv">1</span>)</span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>lnd_layer_buff</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -755,7 +755,7 @@ <h2 data-number="6.4" class="anchored" data-anchor-id="sec-geometry-operations-o
 <h2 data-number="6.5" class="anchored" data-anchor-id="sec-when-to-reproject"><span class="header-section-number">6.5</span> When to reproject?</h2>
 <p>The previous section showed how to set the CRS manually, with an expression such as <code>lnd_layer.set_crs(4326)</code>. In real-world applications, however, CRSs are usually set automatically when data is read-in. Thus, in many projects the main CRS-related task is to transform objects, from one CRS into another. But when should data be transformed? And into which CRS? There are no clear-cut answers to these questions and CRS selection always involves trade-offs <span class="citation" data-cites="maling_coordinate_1992">(<a href="references.html#ref-maling_coordinate_1992" role="doc-biblioref">Maling 1992</a>)</span>. However, there are some general principles provided in this section that can help you decide.</p>
 <p>First, it’s worth considering when to transform. In some cases, transformation to a geographic CRS is essential, such as when publishing data online (for example, a Leaflet-based map using Python package <strong>folium</strong>). Another case is when two objects with different CRSs must be compared or combined, as shown when we try to find the distance between two objects with different CRSs.</p>
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 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>lnd_layer.distance(lnd_layer_proj)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
 <pre><code>/tmp/ipykernel_363/2145313019.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'distance' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.
@@ -785,7 +785,7 @@ <h2 data-number="6.6" class="anchored" data-anchor-id="sec-which-crs-to-use"><sp
 <p>A commonly used default is Universal Transverse Mercator (UTM), a set of CRSs that divide the Earth into 60 longitudinal wedges and 20 latitudinal segments. The transverse Mercator projection used by UTM CRSs is conformal but distorts areas and distances with increasing severity with distance from the center of the UTM zone. Documentation from the GIS software Manifold therefore suggests restricting the longitudinal extent of projects using UTM zones to 6 degrees from the central meridian<a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a>. Therefore, we recommend using UTM only when your focus is on preserving angles for a relatively small area!</p>
 <p>Almost every place on Earth has a UTM code, such as <code>'60H'</code> which refers, among others, to northern New Zealand. UTM EPSG codes run sequentially from <code>32601</code> to <code>32660</code> for northern hemisphere locations and from <code>32701</code> to <code>32760</code> for southern hemisphere locations.</p>
 <p>To show how the system works, let’s create a function, <code>lonlat2UTM</code> to calculate the EPSG code associated with any point on the planet.</p>
-<div id="2cb1471d" class="cell" data-execution_count="25">
+<div id="53634007" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> lonlat2UTM(lon, lat):</span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>    utm <span class="op">=</span> (math.floor((lon <span class="op">+</span> <span class="dv">180</span>) <span class="op">/</span> <span class="dv">6</span>) <span class="op">%</span> <span class="dv">60</span>) <span class="op">+</span> <span class="dv">1</span></span>
 <span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">if</span> lat <span class="op">&gt;</span> <span class="dv">0</span>:</span>
@@ -795,14 +795,14 @@ <h2 data-number="6.6" class="anchored" data-anchor-id="sec-which-crs-to-use"><sp
 <span id="cb37-7"><a href="#cb37-7" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> utm</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The following command uses this function to identify the UTM zone and associated EPSG code for Auckland.</p>
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 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>lonlat2UTM(<span class="fl">174.7</span>, <span class="op">-</span><span class="fl">36.9</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <pre><code>32760</code></pre>
 </div>
 </div>
 <p>Here is another example for London (where we ‘unpack’ the coordinates of the 1<sup>st</sup> geometry in <code>lnd_layer</code> into the <code>lonlat2UTM</code> function arguments).</p>
-<div id="07aff391" class="cell" data-execution_count="27">
+<div id="334c3c23" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>lonlat2UTM(<span class="op">*</span>lnd_layer.geometry.iloc[<span class="dv">0</span>].coords[<span class="dv">0</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
 <pre><code>32630</code></pre>
@@ -824,11 +824,11 @@ <h2 data-number="6.6" class="anchored" data-anchor-id="sec-which-crs-to-use"><sp
 <h2 data-number="6.7" class="anchored" data-anchor-id="sec-reprojecting-vector-geometries"><span class="header-section-number">6.7</span> Reprojecting vector geometries</h2>
 <p><a href="01-spatial-data.html#sec-vector-data" class="quarto-xref"><span>Section 1.2</span></a> demonstrated how vector geometries are made-up of points, and how points form the basis of more complex objects such as lines and polygons. Reprojecting vectors thus consists of transforming the coordinates of these points, which form the vertices of lines and polygons.</p>
 <p><a href="#sec-geometry-operations-on-projected-and-unprojected-data" class="quarto-xref"><span>Section 6.4</span></a> contains an example in which at a <code>GeoDataFrame</code> had to be transformed into an equivalent object, with a different CRS, to calculate the distance between two objects. Reprojection of vector layers is done using the <code>.to_crs</code> method.</p>
-<div id="d66ea6d7" class="cell" data-execution_count="28">
+<div id="d5c2a56b" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>lnd_layer2 <span class="op">=</span> lnd_layer.to_crs(<span class="dv">27700</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now that a transformed version of <code>lnd_layer</code> has been created, the distance between the two representations of London can be found using the <code>.distance</code> method.</p>
-<div id="0270c819" class="cell" data-execution_count="29">
+<div id="37f9614d" class="cell" data-execution_count="29">
 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>lnd_layer2.distance(lnd_layer_proj)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="29">
 <pre><code>0    2017.949587
@@ -837,7 +837,7 @@ <h2 data-number="6.7" class="anchored" data-anchor-id="sec-reprojecting-vector-g
 </div>
 <p>It may come as a surprise that <code>lnd_layer</code> and <code>lnd_layer2</code> are just over 2 <span class="math inline">\(km\)</span> apart! The difference in location between the two points is not due to imperfections in the transforming operation (which is in fact very accurate) but the low precision of the manually specified coordinates when creating <code>lnd_layer</code> and <code>lnd_layer_proj</code>.</p>
 <p>Reprojecting to a different CRS is also demonstrated below using <code>cycle_hire_osm</code>, a point layer that represents ‘docking stations’ where you can hire bicycles in London. The contents of the CRS object associated with a given geometry column are changed when the object’s CRS is transformed. In the code chunk below, we create a new version of <code>cycle_hire_osm</code> with a projected CRS.</p>
-<div id="4d0a1927" class="cell" data-execution_count="30">
+<div id="2b2e6d6c" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb45"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>cycle_hire_osm_projected <span class="op">=</span> cycle_hire_osm.to_crs(<span class="dv">27700</span>)</span>
 <span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a>cycle_hire_osm_projected.crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="30">
@@ -858,7 +858,7 @@ <h2 data-number="6.7" class="anchored" data-anchor-id="sec-reprojecting-vector-g
 </div>
 </div>
 <p>The resulting object has a new CRS according to the EPSG code <code>27700</code>. How to find out more details about this EPSG code, or any code? One option is to search for it online. Another option is to create a standalone CRS object within the Python environment (using <code>pyproj.CRS.from_string</code> or <code>pyproj.CRS.from_epsg</code>, see <a href="#sec-coordinate-reference-systems" class="quarto-xref"><span>Section 6.2</span></a>), and then query its properties, such as <code>.name</code> and <code>.to_wkt()</code>.</p>
-<div id="14c4c8f2" class="cell" data-execution_count="31">
+<div id="44f44010" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a>crs_lnd_new <span class="op">=</span> pyproj.CRS.from_epsg(<span class="dv">27700</span>)</span>
 <span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a>crs_lnd_new.name, crs_lnd_new.to_wkt()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="31">
@@ -890,7 +890,7 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 <li><code>rasterio.warp.reproject</code>, introduced in <a href="04-geometry-operations.html#sec-raster-resampling" class="quarto-xref"><span>Section 4.3.3</span></a>, calculates cell values in the destination grid, using the user-selected resampling method (such as nearest neighbor, or bilinear)</li>
 </ol>
 <p>Let’s take a look at two examples of raster transformation: using categorical and continuous data. Land cover data are usually represented by categorical maps. The <code>nlcd.tif</code> file provides information for a small area in Utah, USA obtained from National Land Cover Database 2011 in the NAD83 / UTM zone 12N CRS. We already created a connection to the <code>nlcd.tif</code> file at the beginning of this chapter, named <code>src_nlcd</code>.</p>
-<div id="5282c3f2" class="cell" data-execution_count="32">
+<div id="d30d4013" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb49"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>src_nlcd</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="32">
 <pre><code>&lt;open DatasetReader name='data/nlcd.tif' mode='r'&gt;</code></pre>
@@ -899,11 +899,11 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 <p>Recall from previous chapters that the raster transformation matrix and dimensions are accessible from the file connection using <code>src_nlcd.transform</code>, <code>src_nlcd.width</code>, and <code>src_nlcd.height</code>, respectively. <!--src_nlcd.bounds is not mentioned here but it is used in the code chunk below... --></p>
 <p>This information will be required to calculate the destination transformation matrix.</p>
 <p>First, let’s define the destination CRS. In this case, we choose WGS84 (EPSG code <code>4326</code>).</p>
-<div id="e108b36d" class="cell" data-execution_count="33">
+<div id="adef7730" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>dst_crs <span class="op">=</span> <span class="st">'EPSG:4326'</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, we are ready to calculate the destination raster transformation matrix (<code>dst_transform</code>), and the destination dimensions (<code>dst_width</code>, <code>dst_height</code>), using <code>rasterio.warp.calculate_default_transform</code>, as follows:</p>
-<div id="d5152265" class="cell" data-execution_count="34">
+<div id="a9d55842" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb52"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a>dst_transform, dst_width, dst_height <span class="op">=</span> rasterio.warp.calculate_default_transform(</span>
 <span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a>    src_nlcd.crs,</span>
 <span id="cb52-3"><a href="#cb52-3" aria-hidden="true" tabindex="-1"></a>    dst_crs,</span>
@@ -913,20 +913,20 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 <span id="cb52-7"><a href="#cb52-7" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Here is the result.</p>
-<div id="bcbd87ad" class="cell" data-execution_count="35">
+<div id="688de7e0" class="cell" data-execution_count="35">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>dst_transform</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="35">
 <pre><code>Affine(0.00031506316853514724, 0.0, -113.24138811813536,
        0.0, -0.00031506316853514724, 37.51912722777022)</code></pre>
 </div>
 </div>
-<div id="9afbb4a1" class="cell" data-execution_count="36">
+<div id="38226fb9" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb55"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>dst_width</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="36">
 <pre><code>1244</code></pre>
 </div>
 </div>
-<div id="a7b36d1e" class="cell" data-execution_count="37">
+<div id="72e0ef83" class="cell" data-execution_count="37">
 <div class="sourceCode cell-code" id="cb57"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>dst_height</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="37">
 <pre><code>1246</code></pre>
@@ -966,7 +966,7 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 </div>
 </div>
 <p>Recall from <a href="04-geometry-operations.html#sec-raster-resampling" class="quarto-xref"><span>Section 4.3.3</span></a> that resampling using <code>rasterio.warp.reproject</code> can take place directly into a ‘destination’ raster file connection. Therefore, our next step is to create the metadata file used for writing the reprojected raster to file. For convenience, we are taking the metadata of the source raster (<code>src_nlcd.meta</code>), making a copy (<code>dst_kwargs</code>), and then updating those specific properties that need to be changed. Note that the reprojection process typically creates ‘No Data’ pixels, even when there were none in the input raster, since the raster orientation changes and the edges need to be ‘filled’ to get back a rectangular extent. For example, a reprojected raster may appear as a ‘tilted’ rectangle, inside a larger straight rectangular extent, whereas the margins around the tilted rectangle are inevitably filled with ‘No Data’ (e.g., the white stripes surrounding the edges in <a href="#fig-raster-reproject-nlcd" class="quarto-xref">Figure&nbsp;<span>6.2</span></a> (b) are ‘No Data’ pixels created as a result of reprojection). We need to specify a ‘No Data’ value of our choice, if there is no existing definition, or keep the existing source raster ‘No Data’ setting, such as <code>255</code> in this case.</p>
-<div id="8f483f0c" class="cell" data-execution_count="38">
+<div id="0d1ad5d6" class="cell" data-execution_count="38">
 <div class="sourceCode cell-code" id="cb61"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a>dst_kwargs <span class="op">=</span> src_nlcd.meta.copy()</span>
 <span id="cb61-2"><a href="#cb61-2" aria-hidden="true" tabindex="-1"></a>dst_kwargs.update({</span>
 <span id="cb61-3"><a href="#cb61-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">'crs'</span>: dst_crs,</span>
@@ -989,11 +989,11 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 </div>
 <p>Now, we are ready to create the reprojected raster. Here, reprojection takes place between two file connections, meaning that the raster value arrays are not being read into memory at once. (It is also possible to reproject into an in-memory <code>ndarray</code> object.)</p>
 <p>To write the reprojected raster, we first create a destination file connection <code>dst_nlcd</code>, pointing at the output file path of our choice (<code>'output/nlcd_4326.tif'</code>), using the updated metadata object created earlier (<code>dst_kwargs</code>):</p>
-<div id="94a26b86" class="cell" data-execution_count="39">
+<div id="487e8d0b" class="cell" data-execution_count="39">
 <div class="sourceCode cell-code" id="cb63"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a>dst_nlcd <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_4326.tif'</span>, <span class="st">'w'</span>, <span class="op">**</span>dst_kwargs)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Then, we use the <code>rasterio.warp.reproject</code> function to calculate and write the reprojection result into the <code>dst_nlcd</code> file connection.</p>
-<div id="b8e4796c" class="cell" data-execution_count="40">
+<div id="6ad0341b" class="cell" data-execution_count="40">
 <div class="sourceCode cell-code" id="cb64"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a>rasterio.warp.reproject(</span>
 <span id="cb64-2"><a href="#cb64-2" aria-hidden="true" tabindex="-1"></a>    source<span class="op">=</span>rasterio.band(src_nlcd, <span class="dv">1</span>),</span>
 <span id="cb64-3"><a href="#cb64-3" aria-hidden="true" tabindex="-1"></a>    destination<span class="op">=</span>rasterio.band(dst_nlcd, <span class="dv">1</span>),</span>
@@ -1005,11 +1005,11 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 <span id="cb64-9"><a href="#cb64-9" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Note–like in the example in <a href="04-geometry-operations.html#sec-raster-resampling" class="quarto-xref"><span>Section 4.3.3</span></a>—that the <code>source</code> and <code>destination</code> accept a ‘band’ object, created using <code>rasterio.band</code>. In this case, there is just one band. If there were more bands, we would have to repeat the procedure for each band, using <code>i</code> instead of <code>1</code> inside a loop. Finally, we close the file connection so that the data are actually written.</p>
-<div id="6350e172" class="cell" data-execution_count="41">
+<div id="46aaba57" class="cell" data-execution_count="41">
 <div class="sourceCode cell-code" id="cb65"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a>dst_nlcd.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Many properties of the new object differ from the previous one, including the number of columns and rows (and therefore number of cells), resolution (transformed from meters into degrees), and extent, as summarized below by comparing the <code>.meta</code> object of the source and destination rasters.</p>
-<div id="8f2614a0" class="cell" data-execution_count="42">
+<div id="58e07cbb" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb66"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a>src_nlcd.meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="42">
 <pre><code>{'driver': 'GTiff',
@@ -1023,7 +1023,7 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
         0.0, -31.52465870178793, 4154086.47216415)}</code></pre>
 </div>
 </div>
-<div id="dac3c933" class="cell" data-execution_count="43">
+<div id="ac95973b" class="cell" data-execution_count="43">
 <div class="sourceCode cell-code" id="cb68"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb68-1"><a href="#cb68-1" aria-hidden="true" tabindex="-1"></a>src_nlcd_4326 <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_4326.tif'</span>)</span>
 <span id="cb68-2"><a href="#cb68-2" aria-hidden="true" tabindex="-1"></a>src_nlcd_4326.meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="43">
@@ -1039,13 +1039,13 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 </div>
 </div>
 <p>Examining the unique raster values tells us that the new raster has the same categories, plus the value <code>255</code> representing ‘No Data’:</p>
-<div id="a1bd5341" class="cell" data-execution_count="44">
+<div id="4a05905e" class="cell" data-execution_count="44">
 <div class="sourceCode cell-code" id="cb70"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb70-1"><a href="#cb70-1" aria-hidden="true" tabindex="-1"></a>np.unique(src_nlcd.read(<span class="dv">1</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="44">
 <pre><code>array([1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)</code></pre>
 </div>
 </div>
-<div id="e9b6ae0f" class="cell" data-execution_count="45">
+<div id="56a34a51" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb72"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb72-1"><a href="#cb72-1" aria-hidden="true" tabindex="-1"></a>np.unique(src_nlcd_4326.read(<span class="dv">1</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="45">
 <pre><code>array([  1,   2,   3,   4,   5,   6,   7,   8, 255], dtype=uint8)</code></pre>
@@ -1091,7 +1091,7 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 </div>
 <p>In the above example, we automatically calculated an optimal (i.e., most information preserving) destination grid using <code>rasterio.warp.calculate_default_transform</code>. This is appropriate when there are no specific requirements for the destination raster spatial properties. Namely, we are not required to obtain a specific origin and resolution, but just wish to preserve the raster values as much as possible. To do that, <code>rasterio.warp.calculate_default_transform</code> ‘tries’ to keep the extent and resolution of the destination raster as similar as possible to the source. In other situations, however, we need to reproject a raster into a specific ‘template’, so that it corresponds, for instance, with other rasters we use in the analysis. In the following code examples, we reproject the <code>nlcd.tif</code> raster, again, but this time using the <code>nlcd_4326.tif</code> reprojection result as the ‘template’ to demonstrate this alternative workflow.</p>
 <p>First, we create a connection to our ‘template’ raster to read its metadata.</p>
-<div id="e0f7315d" class="cell" data-execution_count="47">
+<div id="c09237db" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb75"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb75-1"><a href="#cb75-1" aria-hidden="true" tabindex="-1"></a>template <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_4326.tif'</span>)</span>
 <span id="cb75-2"><a href="#cb75-2" aria-hidden="true" tabindex="-1"></a>template.meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="47">
@@ -1107,11 +1107,11 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 </div>
 </div>
 <p>Then, we create a write-mode connection to our destination raster, using this exact metadata, meaning that the resampling result is going to have identical properties as the ‘template’.</p>
-<div id="96a1f2d3" class="cell" data-execution_count="48">
+<div id="6b702c7c" class="cell" data-execution_count="48">
 <div class="sourceCode cell-code" id="cb77"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb77-1"><a href="#cb77-1" aria-hidden="true" tabindex="-1"></a>dst_nlcd_2 <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_4326_2.tif'</span>, <span class="st">'w'</span>, <span class="op">**</span>template.meta)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, we can resample and write the result with <code>rasterio.warp.reproject</code>.</p>
-<div id="fbd4c093" class="cell" data-execution_count="49">
+<div id="d48654cb" class="cell" data-execution_count="49">
 <div class="sourceCode cell-code" id="cb78"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb78-1"><a href="#cb78-1" aria-hidden="true" tabindex="-1"></a>rasterio.warp.reproject(</span>
 <span id="cb78-2"><a href="#cb78-2" aria-hidden="true" tabindex="-1"></a>    source<span class="op">=</span>rasterio.band(src_nlcd, <span class="dv">1</span>),</span>
 <span id="cb78-3"><a href="#cb78-3" aria-hidden="true" tabindex="-1"></a>    destination<span class="op">=</span>rasterio.band(dst_nlcd_2, <span class="dv">1</span>),</span>
@@ -1124,7 +1124,7 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 <span id="cb78-10"><a href="#cb78-10" aria-hidden="true" tabindex="-1"></a>dst_nlcd_2.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Naturally, the outputs of the last two examples—<code>nlcd_4326.tif</code> and <code>nlcd_4326_2.tif</code>—are identical, as we used the same destination grid and the same source data. We can check it with <code>np.all</code>.</p>
-<div id="fbe02e41" class="cell" data-execution_count="50">
+<div id="f875091b" class="cell" data-execution_count="50">
 <div class="sourceCode cell-code" id="cb79"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb79-1"><a href="#cb79-1" aria-hidden="true" tabindex="-1"></a>d <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_4326.tif'</span>).read(<span class="dv">1</span>) <span class="op">==</span> <span class="op">\</span></span>
 <span id="cb79-2"><a href="#cb79-2" aria-hidden="true" tabindex="-1"></a>    rasterio.<span class="bu">open</span>(<span class="st">'output/nlcd_4326_2.tif'</span>).read(<span class="dv">1</span>)</span>
 <span id="cb79-3"><a href="#cb79-3" aria-hidden="true" tabindex="-1"></a>np.<span class="bu">all</span>(d)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1136,7 +1136,7 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 <p>Importantly, when the template raster has much more ‘coarse’ resolution than the source raster, the <code>rasterio.enums.Resampling.average</code> (for continuous rasters) or <code>rasterio.enums.Resampling.mode</code> (for categorical rasters) resampling methods should be used, instead of <code>rasterio.enums.Resampling.nearest</code>. Otherwise, much of the data will be lost, as the ‘nearest’ method can capture one-pixel value only for each destination raster pixel.</p>
 <p>Reprojecting continuous rasters (with numeric or, in this case, integer values) follows an almost identical procedure. This is demonstrated below with <code>srtm.tif</code> from the Shuttle Radar Topography Mission (SRTM), which represents height in meters above sea level (elevation) with the WGS84 CRS.</p>
 <p>We will reproject this dataset into a projected CRS, but not with the nearest neighbor method. Instead, we will use the bilinear method which computes the output cell value based on the four nearest cells in the original raster. The values in the projected dataset are the distance-weighted average of the values from these four cells: the closer the input cell is to the center of the output cell, the greater its weight. The following code section creates a text string representing WGS 84 / UTM zone 12N, and reprojects the raster into this CRS, using the bilinear method. The code is practically the same as in the first example in this section, except for changing the source and destination file names, and replacing <code>rasterio.enums.Resampling.nearest</code> with <code>rasterio.enums.Resampling.bilinear</code>.</p>
-<div id="1397d15f" class="cell" data-execution_count="51">
+<div id="30387be1" class="cell" data-execution_count="51">
 <div class="sourceCode cell-code" id="cb81"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb81-1"><a href="#cb81-1" aria-hidden="true" tabindex="-1"></a>dst_crs <span class="op">=</span> <span class="st">'EPSG:32612'</span></span>
 <span id="cb81-2"><a href="#cb81-2" aria-hidden="true" tabindex="-1"></a>dst_transform, dst_width, dst_height <span class="op">=</span> rasterio.warp.calculate_default_transform(</span>
 <span id="cb81-3"><a href="#cb81-3" aria-hidden="true" tabindex="-1"></a>    src_srtm.crs,</span>
@@ -1207,7 +1207,7 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="sec-reprojecting-raster-g
 <h2 data-number="6.9" class="anchored" data-anchor-id="sec-custom-map-projections"><span class="header-section-number">6.9</span> Custom map projections</h2>
 <p>Established CRSs captured by <code>AUTHORITY:CODE</code> identifiers such as <code>EPSG:4326</code> are well suited for many applications. However, it is desirable to use alternative projections or to create custom CRSs in some cases. <a href="#sec-which-crs-to-use" class="quarto-xref"><span>Section 6.6</span></a> mentioned reasons for using custom CRSs, and provided several possible approaches. Here, we show how to apply these ideas in Python.</p>
 <p>One approach is to take an existing WKT definition of a CRS, modify some of its elements, and then use the new definition for reprojecting, using the reprojection methods shown above for vector layers (<a href="#sec-reprojecting-vector-geometries" class="quarto-xref"><span>Section 6.7</span></a>) and rasters (<a href="#sec-reprojecting-raster-geometries" class="quarto-xref"><span>Section 6.8</span></a>). For example, let’s transform the <code>zion.gpkg</code> vector layer to a custom azimuthal equidistant (AEQD) CRS. Using a custom AEQD CRS requires knowing the coordinates of the center point of a dataset in degrees (geographic CRS). In our case, this information can be extracted by calculating the centroid of the <code>zion</code> layer transformed into WGS84:</p>
-<div id="4ae2f904" class="cell" data-execution_count="53">
+<div id="06e24374" class="cell" data-execution_count="53">
 <div class="sourceCode cell-code" id="cb83"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb83-1"><a href="#cb83-1" aria-hidden="true" tabindex="-1"></a>lon, lat <span class="op">=</span> zion.to_crs(<span class="dv">4326</span>).union_all().centroid.coords[<span class="dv">0</span>]</span>
 <span id="cb83-2"><a href="#cb83-2" aria-hidden="true" tabindex="-1"></a>lon, lat</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="53">
@@ -1215,7 +1215,7 @@ <h2 data-number="6.9" class="anchored" data-anchor-id="sec-custom-map-projection
 </div>
 </div>
 <p>Next, we can use the obtained lon/lat coordinates in <code>coords</code> to update the WKT definition of the azimuthal equidistant (AEQD) CRS seen below. Notice that we modified just two values below—<code>"Central_Meridian"</code> to the longitude and <code>"Latitude_Of_Origin"</code> to the latitude of our centroid.</p>
-<div id="051b7fb1" class="cell" data-execution_count="54">
+<div id="e8297fb9" class="cell" data-execution_count="54">
 <div class="sourceCode cell-code" id="cb85"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb85-1"><a href="#cb85-1" aria-hidden="true" tabindex="-1"></a>my_wkt <span class="op">=</span> <span class="ss">f'''PROJCS["Custom_AEQD",</span></span>
 <span id="cb85-2"><a href="#cb85-2" aria-hidden="true" tabindex="-1"></a><span class="ss"> GEOGCS["GCS_WGS_1984",</span></span>
 <span id="cb85-3"><a href="#cb85-3" aria-hidden="true" tabindex="-1"></a><span class="ss">  DATUM["WGS_1984",</span></span>
@@ -1260,7 +1260,7 @@ <h2 data-number="6.9" class="anchored" data-anchor-id="sec-custom-map-projection
 </div>
 </div>
 <p>This approach’s last step is to transform our original object (<code>zion</code>) to our new custom CRS (<code>zion_aeqd</code>).</p>
-<div id="d97c2a60" class="cell" data-execution_count="55">
+<div id="8bd48e17" class="cell" data-execution_count="55">
 <div class="sourceCode cell-code" id="cb90"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb90-1"><a href="#cb90-1" aria-hidden="true" tabindex="-1"></a>zion_aeqd <span class="op">=</span> zion.to_crs(my_wkt)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Custom projections can also be made interactively, for example, using the Projection Wizard<a href="#fn5" class="footnote-ref" id="fnref5" role="doc-noteref"><sup>5</sup></a> web application <span class="citation" data-cites="savric_projection_2016">(<a href="references.html#ref-savric_projection_2016" role="doc-biblioref">Šavrič, Jenny, and Jenny 2016</a>)</span>. This website allows you to select a spatial extent of your data and a distortion property, and returns a list of possible projections. The list also contains WKT definitions of the projections that you can copy and use for reprojections. See Open Geospatial Consortium <span class="citation" data-cites="opengeospatialconsortium_wellknown_2019">(<a href="references.html#ref-opengeospatialconsortium_wellknown_2019" role="doc-biblioref">Open Geospatial Consortium 2019</a>)</span> for details on creating custom CRS definitions with WKT strings.</p>
diff --git a/07-read-write.html b/07-read-write.html
index c52f5a0a..7e4db92b 100644
--- a/07-read-write.html
+++ b/07-read-write.html
@@ -357,7 +357,7 @@ <h1 class="title"><span id="sec-read-write" class="quarto-section-identifier"><s
 <section id="prerequisites" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2>
 <p>This chapter requires importing the following packages:</p>
-<div id="421d4884" class="cell" data-execution_count="4">
+<div id="6ab84e7c" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> urllib.request</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> zipfile</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
@@ -372,7 +372,7 @@ <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2
 <span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> osmnx <span class="im">as</span> ox</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>It also relies on the following data files:</p>
-<div id="f9feba71" class="cell" data-execution_count="5">
+<div id="d7a85891" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>nz <span class="op">=</span> gpd.read_file(<span class="st">'data/nz.gpkg'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>nz_elev <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/nz_elev.tif'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -389,7 +389,7 @@ <h2 data-number="7.2" class="anchored" data-anchor-id="sec-retrieving-open-data"
 <p>Some global geoportals overcome this issue. The GEOSS portal<a href="#fn4" class="footnote-ref" id="fnref4" role="doc-noteref"><sup>4</sup></a> and the Copernicus Data Space Ecosystem<a href="#fn5" class="footnote-ref" id="fnref5" role="doc-noteref"><sup>5</sup></a>, for example, contain many raster datasets with global coverage. A wealth of vector datasets can be accessed from the SEDAC<a href="#fn6" class="footnote-ref" id="fnref6" role="doc-noteref"><sup>6</sup></a> portal run by the National Aeronautics and Space Administration (NASA) and the European Union’s INSPIRE geoportal<a href="#fn7" class="footnote-ref" id="fnref7" role="doc-noteref"><sup>7</sup></a>, with global and regional coverage.</p>
 <p>Most geoportals provide a graphical interface allowing datasets to be queried based on characteristics such as spatial and temporal extent, the United States Geological Survey’s EarthExplorer<a href="#fn8" class="footnote-ref" id="fnref8" role="doc-noteref"><sup>8</sup></a> and NASA’s EarthData Search<a href="#fn9" class="footnote-ref" id="fnref9" role="doc-noteref"><sup>9</sup></a> being prime examples. Exploring datasets interactively on a browser is an effective way of understanding available layers. From reproducibility and efficiency perspectives, downloading data is, however, best done with code. Downloads can be initiated from the command line using a variety of techniques, primarily via URLs and APIs (see the Sentinel API<a href="#fn10" class="footnote-ref" id="fnref10" role="doc-noteref"><sup>10</sup></a>, for example).</p>
 <p>Files hosted on static URLs can be downloaded with the following method, as illustrated in the code chunk below which accesses the Natural Earth Data<a href="#fn11" class="footnote-ref" id="fnref11" role="doc-noteref"><sup>11</sup></a> website to download the world airports layer zip file and to extract the contained ESRI Shapefile. Note that the download code is complicated by the fact that the server checks the <code>User-agent</code> header of the request, basically to make sure that the download takes place through a browser. To overcome this, we add a header corresponding to a request coming from a browser (such as Firefox) in our code.</p>
-<div id="674affb4" class="cell" data-execution_count="6">
+<div id="b53348cb" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Set URL+filename</span></span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">'https://www.naturalearthdata.com/http//www.naturalearthdata.com/'</span></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>url <span class="op">+=</span> <span class="st">'download/10m/cultural/ne_10m_airports.zip'</span></span>
@@ -430,7 +430,7 @@ <h2 data-number="7.2" class="anchored" data-anchor-id="sec-retrieving-open-data"
 <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packages"><span class="header-section-number">7.3</span> Geographic data packages</h2>
 <p>Several Python packages have been developed for accessing geographic data, two of which are demonstrated below. These provide interfaces to one or more spatial libraries or geoportals and aim to make data access even quicker from the command line.</p>
 <p>Administrative borders are often useful in spatial analysis. These can be accessed with the <code>cartopy.io.shapereader.natural_earth</code> function from the <strong>cartopy</strong> package <span class="citation" data-cites="cartopy">(<a href="references.html#ref-cartopy" role="doc-biblioref">Met Office 2010-2015</a>)</span>. For example, the following code loads the <code>'admin_2_counties'</code> dataset of US counties into a <code>GeoDataFrame</code>.</p>
-<div id="a6f642a0" class="cell" data-execution_count="9">
+<div id="be2b265f" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>filename <span class="op">=</span> cartopy.io.shapereader.natural_earth(</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>    resolution<span class="op">=</span><span class="st">'10m'</span>,</span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>    category<span class="op">=</span><span class="st">'cultural'</span>,</span>
@@ -540,7 +540,7 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
 <p>Note that <a href="#fig-ne-counties" class="quarto-xref">Figure&nbsp;<span>7.2</span></a> x-axis spans the entire range of longitudes, between <code>-180</code> and <code>180</code>, since the Aleutian Islands county (which is small and difficult to see on the map) crosses the International Date Line.</p>
 <p>Other layers can from NaturalEarth be accessed the same way. You need to specify the <code>resolution</code>, <code>category</code>, and <code>name</code> of the requested dataset in Natural Earth Data, then run the <code>cartopy.io.shapereader.natural_earth</code>, which downloads the file(s) and returns the path, and read the file into the Python environment, e.g., using <code>gpd.read_file</code>. This is an alternative approach to ‘directly’ downloading files as shown earlier (<a href="#sec-retrieving-open-data" class="quarto-xref"><span>Section 7.2</span></a>).</p>
 <p>The second example uses the <strong>osmnx</strong> package <span class="citation" data-cites="osmnx">(<a href="references.html#ref-osmnx" role="doc-biblioref">Boeing 2017</a>)</span> to find parks from the OpenStreetMap (OSM) database. As illustrated in the code chunk below, OpenStreetMap data can be obtained using the <code>ox.features.features_from_place</code> function. The first argument is a string which is geocoded to a polygon (the <code>ox.features.features_from_bbox</code> and <code>ox.features.features_from_polygon</code> can also be used to query a custom area of interest). The second argument specifies the OSM tag(s)<a href="#fn12" class="footnote-ref" id="fnref12" role="doc-noteref"><sup>12</sup></a>, selecting which OSM elements we’re interested in (parks, in this case), represented by key-value pairs.</p>
-<div id="67b9d5e5" class="cell" data-execution_count="11">
+<div id="cc40ba3a" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>parks <span class="op">=</span> ox.features.features_from_place(</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    query<span class="op">=</span><span class="st">'leeds uk'</span>, </span>
 <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    tags<span class="op">=</span>{<span class="st">'leisure'</span>: <span class="st">'park'</span>}</span>
@@ -579,7 +579,7 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_d632a7d90e07a7b8211fbfc15df549e0 {
+                #map_d2bd802906c154e704967575d8edcb09 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -609,14 +609,14 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_d632a7d90e07a7b8211fbfc15df549e0&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_d2bd802906c154e704967575d8edcb09&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_d632a7d90e07a7b8211fbfc15df549e0 = L.map(
-                &quot;map_d632a7d90e07a7b8211fbfc15df549e0&quot;,
+            var map_d2bd802906c154e704967575d8edcb09 = L.map(
+                &quot;map_d2bd802906c154e704967575d8edcb09&quot;,
                 {
                     center: [53.8276068, -1.5234091],
                     crs: L.CRS.EPSG3857,
@@ -625,79 +625,79 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_d632a7d90e07a7b8211fbfc15df549e0);
+            L.control.scale().addTo(map_d2bd802906c154e704967575d8edcb09);
 
             
 
         
     
-            var tile_layer_a2e91bd16d5b3c813d8d203e73f4a349 = L.tileLayer(
+            var tile_layer_371771054647a7c3cb39d01dd44c0d42 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_a2e91bd16d5b3c813d8d203e73f4a349.addTo(map_d632a7d90e07a7b8211fbfc15df549e0);
+            tile_layer_371771054647a7c3cb39d01dd44c0d42.addTo(map_d2bd802906c154e704967575d8edcb09);
         
     
-            map_d632a7d90e07a7b8211fbfc15df549e0.fitBounds(
+            map_d2bd802906c154e704967575d8edcb09.fitBounds(
                 [[53.7143165, -1.7360313], [53.9408971, -1.3107869]],
                 {}
             );
         
     
-        function geo_json_b07280c7ac24749b3a283fceea22a14f_styler(feature) {
+        function geo_json_4d10893fd7684af47e2a008293f3b3de_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_b07280c7ac24749b3a283fceea22a14f_highlighter(feature) {
+        function geo_json_4d10893fd7684af47e2a008293f3b3de_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_b07280c7ac24749b3a283fceea22a14f_pointToLayer(feature, latlng) {
+        function geo_json_4d10893fd7684af47e2a008293f3b3de_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_b07280c7ac24749b3a283fceea22a14f_styler(feature)
+            let style = geo_json_4d10893fd7684af47e2a008293f3b3de_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_b07280c7ac24749b3a283fceea22a14f_onEachFeature(feature, layer) {
+        function geo_json_4d10893fd7684af47e2a008293f3b3de_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_b07280c7ac24749b3a283fceea22a14f.resetStyle(e.target);
+                            geo_json_4d10893fd7684af47e2a008293f3b3de.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_b07280c7ac24749b3a283fceea22a14f_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_4d10893fd7684af47e2a008293f3b3de_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_b07280c7ac24749b3a283fceea22a14f = L.geoJson(null, {
-                onEachFeature: geo_json_b07280c7ac24749b3a283fceea22a14f_onEachFeature,
+        var geo_json_4d10893fd7684af47e2a008293f3b3de = L.geoJson(null, {
+                onEachFeature: geo_json_4d10893fd7684af47e2a008293f3b3de_onEachFeature,
             
-                style: geo_json_b07280c7ac24749b3a283fceea22a14f_styler,
-                pointToLayer: geo_json_b07280c7ac24749b3a283fceea22a14f_pointToLayer,
+                style: geo_json_4d10893fd7684af47e2a008293f3b3de_styler,
+                pointToLayer: geo_json_4d10893fd7684af47e2a008293f3b3de_pointToLayer,
         });
 
-        function geo_json_b07280c7ac24749b3a283fceea22a14f_add (data) {
-            geo_json_b07280c7ac24749b3a283fceea22a14f
+        function geo_json_4d10893fd7684af47e2a008293f3b3de_add (data) {
+            geo_json_4d10893fd7684af47e2a008293f3b3de
                 .addData(data);
         }
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-    geo_json_b07280c7ac24749b3a283fceea22a14f.bindTooltip(
+    geo_json_4d10893fd7684af47e2a008293f3b3de.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -721,7 +721,7 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_b07280c7ac24749b3a283fceea22a14f.addTo(map_d632a7d90e07a7b8211fbfc15df549e0);
+            geo_json_4d10893fd7684af47e2a008293f3b3de.addTo(map_d2bd802906c154e704967575d8edcb09);
         
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@@ -736,7 +736,7 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
 <p>OpenStreetMap is a vast global database of crowd-sourced data, is growing daily, and has a wider ecosystem of tools enabling easy access to the data, from the Overpass turbo<a href="#fn15" class="footnote-ref" id="fnref15" role="doc-noteref"><sup>15</sup></a> web service for rapid development and testing of OSM queries to <code>osm2pgsql</code> for importing the data into a PostGIS database. Although the quality of datasets derived from OSM varies, the data source and wider OSM ecosystems have many advantages: they provide datasets that are available globally, free of charge, and constantly improving thanks to an army of volunteers. Using OSM encourages ‘citizen science’ and contributions back to the digital commons (you can start editing data representing a part of the world you know well at <a href="https://www.openstreetmap.org/" class="uri">https://www.openstreetmap.org/</a>).</p>
 <p>One way to obtain spatial information is to perform geocoding—transform a description of a location, usually an address, into a set of coordinates. This is typically done by sending a query to an online service and getting the location as a result. Many such services exist that differ in the used method of geocoding, usage limitations, costs, or API key requirements. Nominatim<a href="#fn16" class="footnote-ref" id="fnref16" role="doc-noteref"><sup>16</sup></a> is a well-known free service, based on OpenStreetMap data, and there are many other free and commercial geocoding services.</p>
 <p><strong>geopandas</strong> provides the <code>gpd.tools.geocode</code>, which can geocode addresses to a <code>GeoDataFrame</code>. Internally it uses the <strong>geopy</strong> package, supporting several providers through the <code>provider</code> parameter (use <code>geopy.geocoders.SERVICE_TO_GEOCODER</code> to see possible options). The example below searches for John Snow blue plaque<a href="#fn17" class="footnote-ref" id="fnref17" role="doc-noteref"><sup>17</sup></a> coordinates located on a building in the Soho district of London. The result is a <code>GeoDataFrame</code> with the address we passed to <code>gpd.tools.geocode</code>, and the detected point location.</p>
-<div id="6443a834" class="cell" data-execution_count="15">
+<div id="184226cf" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>result <span class="op">=</span> gpd.tools.geocode(<span class="st">'54 Frith St, London W1D 4SJ, UK'</span>, timeout<span class="op">=</span><span class="dv">10</span>)</span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>result</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="13">
@@ -764,7 +764,7 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
 </div>
 </div>
 <p>Importantly, (1) we can pass a <code>list</code> of multiple addresses instead of just one, resulting in a <code>GeoDataFrame</code> with corresponding multiple rows, and (2) ‘No Results’ responses are represented by <code>POINT EMPTY</code> geometries, as shown in the following example.</p>
-<div id="c6e81fa4" class="cell" data-execution_count="16">
+<div id="4eb38877" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>result <span class="op">=</span> gpd.tools.geocode(</span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>    [<span class="st">'54 Frith St, London W1D 4SJ, UK'</span>, <span class="st">'abcdefghijklmnopqrstuvwxyz'</span>], </span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>    timeout<span class="op">=</span><span class="dv">10</span></span>
@@ -832,7 +832,7 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_1736ab2e1c401a597c246adfa89fa04a {
+                #map_47619f4488c9bf50b400d9389ea8565a {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -862,14 +862,14 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_1736ab2e1c401a597c246adfa89fa04a&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_47619f4488c9bf50b400d9389ea8565a&quot; ></div>
         
 </body>
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-            var map_1736ab2e1c401a597c246adfa89fa04a = L.map(
-                &quot;map_1736ab2e1c401a597c246adfa89fa04a&quot;,
+            var map_47619f4488c9bf50b400d9389ea8565a = L.map(
+                &quot;map_47619f4488c9bf50b400d9389ea8565a&quot;,
                 {
                     center: [51.513770550000004, -0.13178011626807157],
                     crs: L.CRS.EPSG3857,
@@ -878,79 +878,79 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_1736ab2e1c401a597c246adfa89fa04a);
+            L.control.scale().addTo(map_47619f4488c9bf50b400d9389ea8565a);
 
             
 
         
     
-            var tile_layer_cce7df35c20168d88bddabbca4269c85 = L.tileLayer(
+            var tile_layer_685ae15886dfeff6d9356ea842d1f1e1 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_cce7df35c20168d88bddabbca4269c85.addTo(map_1736ab2e1c401a597c246adfa89fa04a);
+            tile_layer_685ae15886dfeff6d9356ea842d1f1e1.addTo(map_47619f4488c9bf50b400d9389ea8565a);
         
     
-            map_1736ab2e1c401a597c246adfa89fa04a.fitBounds(
+            map_47619f4488c9bf50b400d9389ea8565a.fitBounds(
                 [[51.513770550000004, -0.13178011626807157], [51.513770550000004, -0.13178011626807157]],
                 {}
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-        function geo_json_354dcfcf201f9191228bfe8842d60a52_styler(feature) {
+        function geo_json_ac14297f38d3a35f8e9fca9f43635e0a_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;color&quot;: &quot;red&quot;, &quot;fillColor&quot;: &quot;red&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_354dcfcf201f9191228bfe8842d60a52_highlighter(feature) {
+        function geo_json_ac14297f38d3a35f8e9fca9f43635e0a_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_354dcfcf201f9191228bfe8842d60a52_pointToLayer(feature, latlng) {
+        function geo_json_ac14297f38d3a35f8e9fca9f43635e0a_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 20, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_354dcfcf201f9191228bfe8842d60a52_styler(feature)
+            let style = geo_json_ac14297f38d3a35f8e9fca9f43635e0a_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_354dcfcf201f9191228bfe8842d60a52_onEachFeature(feature, layer) {
+        function geo_json_ac14297f38d3a35f8e9fca9f43635e0a_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_354dcfcf201f9191228bfe8842d60a52.resetStyle(e.target);
+                            geo_json_ac14297f38d3a35f8e9fca9f43635e0a.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_354dcfcf201f9191228bfe8842d60a52_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_ac14297f38d3a35f8e9fca9f43635e0a_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_354dcfcf201f9191228bfe8842d60a52 = L.geoJson(null, {
-                onEachFeature: geo_json_354dcfcf201f9191228bfe8842d60a52_onEachFeature,
+        var geo_json_ac14297f38d3a35f8e9fca9f43635e0a = L.geoJson(null, {
+                onEachFeature: geo_json_ac14297f38d3a35f8e9fca9f43635e0a_onEachFeature,
             
-                style: geo_json_354dcfcf201f9191228bfe8842d60a52_styler,
-                pointToLayer: geo_json_354dcfcf201f9191228bfe8842d60a52_pointToLayer,
+                style: geo_json_ac14297f38d3a35f8e9fca9f43635e0a_styler,
+                pointToLayer: geo_json_ac14297f38d3a35f8e9fca9f43635e0a_pointToLayer,
         });
 
-        function geo_json_354dcfcf201f9191228bfe8842d60a52_add (data) {
-            geo_json_354dcfcf201f9191228bfe8842d60a52
+        function geo_json_ac14297f38d3a35f8e9fca9f43635e0a_add (data) {
+            geo_json_ac14297f38d3a35f8e9fca9f43635e0a
                 .addData(data);
         }
-            geo_json_354dcfcf201f9191228bfe8842d60a52_add({&quot;bbox&quot;: [-0.13178011626807157, 51.513770550000004, -0.13178011626807157, 51.513770550000004], &quot;features&quot;: [{&quot;bbox&quot;: [-0.13178011626807157, 51.513770550000004, -0.13178011626807157, 51.513770550000004], &quot;geometry&quot;: {&quot;coordinates&quot;: [-0.13178011626807157, 51.513770550000004], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;address&quot;: &quot;54, Frith Street, W1D 3JD, Frith Street, London, England, United Kingdom&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
+            geo_json_ac14297f38d3a35f8e9fca9f43635e0a_add({&quot;bbox&quot;: [-0.13178011626807157, 51.513770550000004, -0.13178011626807157, 51.513770550000004], &quot;features&quot;: [{&quot;bbox&quot;: [-0.13178011626807157, 51.513770550000004, -0.13178011626807157, 51.513770550000004], &quot;geometry&quot;: {&quot;coordinates&quot;: [-0.13178011626807157, 51.513770550000004], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;address&quot;: &quot;54, Frith Street, W1D 3JD, Frith Street, London, England, United Kingdom&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_354dcfcf201f9191228bfe8842d60a52.bindTooltip(
+    geo_json_ac14297f38d3a35f8e9fca9f43635e0a.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -974,7 +974,7 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="sec-geographic-data-packa
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_354dcfcf201f9191228bfe8842d60a52.addTo(map_1736ab2e1c401a597c246adfa89fa04a);
+            geo_json_ac14297f38d3a35f8e9fca9f43635e0a.addTo(map_47619f4488c9bf50b400d9389ea8565a);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -1103,7 +1103,7 @@ <h2 data-number="7.5" class="anchored" data-anchor-id="sec-data-input"><span cla
 <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span class="header-section-number">7.5.1</span> Vector data</h3>
 <p>Spatial vector data comes in a wide variety of file formats. Most popular representations such as <code>.shp</code>, <code>.geojson</code>, and <code>.gpkg</code> files can be imported and exported with <strong>geopandas</strong> functions <code>read_file</code> and <code>to_file</code> (covered in <a href="#sec-data-output" class="quarto-xref"><span>Section 7.6</span></a>), respectively.</p>
 <p><strong>geopandas</strong> uses GDAL to read and write data, via <strong>pyogrio</strong> since <code>geopandas</code> version <code>1.0.0</code> (previously via <strong>fiona</strong>). After <strong>pyogrio</strong> is imported, <code>pyogrio.list_drivers</code> can be used to list drivers available to GDAL, including whether they can read (<code>'r'</code>), append (<code>'a'</code>), or write (<code>'w'</code>) data, or all three.</p>
-<div id="4643c040" class="cell" data-execution_count="20">
+<div id="d4a099b8" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>pyogrio.list_drivers()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <pre><code>{'PCIDSK': 'rw',
@@ -1112,11 +1112,11 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
  'AVCE00': 'r',
  'HTTP': 'r'}</code></pre>
 <p>The first argument of the <strong>geopandas</strong> versatile data import function <code>gpd.read_file</code> is <code>filename</code>, which is typically a string, but can also be a file connection. The content of a string could vary between different drivers. In most cases, as with the ESRI Shapefile (<code>.shp</code>) or the GeoPackage format (<code>.gpkg</code>), the <code>filename</code> argument would be a path or a URL to an actual file, such as <code>geodata.gpkg</code>. The driver is automatically selected based on the file extension, as demonstrated for a <code>.gpkg</code> file below.</p>
-<div id="46aea353" class="cell" data-execution_count="21">
+<div id="d13e3ddd" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>world <span class="op">=</span> gpd.read_file(<span class="st">'data/world.gpkg'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>For some drivers, such as a File Geodatabase (<code>OpenFileGDB</code>), <code>filename</code> could be provided as a folder name. GeoJSON, a plain text format, on the other hand, can be read from a <code>.geojson</code> file, but also from a string.</p>
-<div id="343773c2" class="cell" data-execution_count="22">
+<div id="46a4c030" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>gpd.read_file(<span class="st">'{"type":"Point","coordinates":[34.838848,31.296301]}'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="18">
 <div>
@@ -1142,7 +1142,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 </div>
 <p>Some vector formats, such as GeoPackage, can store multiple data layers. By default, <code>gpd.read_file</code> reads the first layer of the file specified in <code>filename</code>. However, using the <code>layer</code> argument you can specify any other layer. To list the available layers, we can use function <code>gpd.list_layers</code> (or <code>pyogrio.list_layers</code>).</p>
 <p>The <code>gpd.read_file</code> function also allows for reading just parts of the file into RAM with two possible mechanisms. The first one is related to the <code>where</code> argument, which allows specifying what part of the data to read using an SQL <code>WHERE</code> expression. An example below extracts data for Tanzania only from the <code>world.gpkg</code> file (<a href="#fig-read-shp-query" class="quarto-xref">Figure&nbsp;<span>7.5</span></a> (a)). It is done by specifying that we want to get all rows for which <code>name_long</code> equals to <code>'Tanzania'</code>.</p>
-<div id="042ed60f" class="cell" data-execution_count="23">
+<div id="7d25584c" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>tanzania <span class="op">=</span> gpd.read_file(<span class="st">'data/world.gpkg'</span>, where<span class="op">=</span><span class="st">'name_long="Tanzania"'</span>)</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>tanzania</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="19">
@@ -1177,7 +1177,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 </div>
 </div>
 <p>If you do not know the names of the available columns, a good approach is to read the layer metadata using <code>pyogrio.read_info</code>. The resulting object contains, among other properties, the column names (<code>fields</code>) and data types (<code>dtypes</code>):</p>
-<div id="97e5da72" class="cell" data-execution_count="24">
+<div id="a911ef98" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>info <span class="op">=</span> pyogrio.read_info(<span class="st">'data/world.gpkg'</span>)</span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>info[<span class="st">'fields'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="20">
@@ -1185,7 +1185,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
        'type', 'area_km2', 'pop', 'lifeExp', 'gdpPercap'], dtype=object)</code></pre>
 </div>
 </div>
-<div id="011386e7" class="cell" data-execution_count="25">
+<div id="b03aac5a" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>info[<span class="st">'dtypes'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="21">
 <pre><code>array(['object', 'object', 'object', 'object', 'object', 'object',
@@ -1193,11 +1193,11 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 </div>
 </div>
 <p>The second mechanism uses the <code>mask</code> argument to filter data based on intersection with an existing geometry. This argument expects a geometry (<code>GeoDataFrame</code>, <code>GeoSeries</code>, or <code>shapely</code> geometry) representing the area where we want to extract the data. Let’s try it using a small example—we want to read polygons from our file that intersect with the buffer of 50,000 <span class="math inline">\(m\)</span> of Tanzania’s borders. To do it, we need to transform the geometry to a projected CRS (such as <code>EPSG:32736</code>), prepare our ‘filter’ by creating the buffer (<a href="04-geometry-operations.html#sec-buffers" class="quarto-xref"><span>Section 4.2.3</span></a>), and transform back to the original CRS to be used as a mask (<a href="#fig-read-shp-query" class="quarto-xref">Figure&nbsp;<span>7.5</span></a> (a)).</p>
-<div id="e7190232" class="cell" data-execution_count="26">
+<div id="074ee505" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>tanzania_buf <span class="op">=</span> tanzania.to_crs(<span class="dv">32736</span>).<span class="bu">buffer</span>(<span class="dv">50000</span>).to_crs(<span class="dv">4326</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, we can pass the ‘filter’ geometry <code>tanzania_buf</code> to the <code>mask</code> argument of <code>gpd.read_file</code>.</p>
-<div id="0e8bca21" class="cell" data-execution_count="27">
+<div id="5c7bb924" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>tanzania_neigh <span class="op">=</span> gpd.read_file(<span class="st">'data/world.gpkg'</span>, mask<span class="op">=</span>tanzania_buf)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Our result, shown in <a href="#fig-read-shp-query" class="quarto-xref">Figure&nbsp;<span>7.5</span></a> (b), contains Tanzania and every country intersecting with its 50,000 <span class="math inline">\(m\)</span> buffer. Note that the last two expressions are used to add text labels with the <code>name_long</code> of each country, placed at the country centroid.</p>
@@ -1273,7 +1273,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 </div>
 </div>
 <p>Instead of columns describing ‘XY’ coordinates, a single column can also contain the geometry information, not necessarily points but possibly any other geometry type. Well-known text (WKT), well-known binary (WKB), and GeoJSON are examples of formats used to encode geometry in such a column. For instance, the <code>world_wkt.csv</code> file has a column named <code>'WKT'</code>, representing polygons of the world’s countries (in WKT format). When importing the CSV file into a <code>DataFrame</code>, the <code>'WKT'</code> column is interpreted just like any other string column.</p>
-<div id="295cfa75" class="cell" data-execution_count="30">
+<div id="ddb8f88d" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>world_wkt <span class="op">=</span> pd.read_csv(<span class="st">'data/world_wkt.csv'</span>)</span>
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>world_wkt</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="26">
@@ -1356,7 +1356,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 </div>
 </div>
 <p>To convert it to a <code>GeoDataFrame</code>, we can apply the <code>gpd.GeoSeries.from_wkt</code> function (which is analogous to <code>shapely</code>’s <code>shapely.from_wkt</code>, see <a href="01-spatial-data.html#sec-geometries" class="quarto-xref"><span>Section 1.2.5</span></a>) on the WKT strings, to convert the series of WKT strings into a <code>GeoSeries</code> with the geometries.</p>
-<div id="93108a11" class="cell" data-execution_count="31">
+<div id="4ee317a7" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>world_wkt[<span class="st">'geometry'</span>] <span class="op">=</span> gpd.GeoSeries.from_wkt(world_wkt[<span class="st">'WKT'</span>])</span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>world_wkt <span class="op">=</span> gpd.GeoDataFrame(world_wkt)</span>
 <span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>world_wkt</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1457,7 +1457,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 </div>
 <p>As a final example, we will show how <strong>geopandas</strong> also reads KML files. A KML file stores geographic information in XML format—a data format for the creation of web pages and the transfer of data in an application-independent way <span class="citation" data-cites="nolan_xml_2014">(<a href="references.html#ref-nolan_xml_2014" role="doc-biblioref">Nolan and Lang 2014</a>)</span>. Here, we access a KML file from the web.</p>
 <p>The sample KML file <code>KML_Samples.kml</code> contains more than one layer.</p>
-<div id="827fa9f6" class="cell" data-execution_count="33">
+<div id="7abba819" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>u <span class="op">=</span> <span class="st">'https://developers.google.com/kml/documentation/KML_Samples.kml'</span></span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>gpd.list_layers(u)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="29">
@@ -1510,7 +1510,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 </div>
 </div>
 <p>We can choose, for instance, the first layer <code>'Placemarks'</code> and read it, using <code>gpd.read_file</code> with an additional <code>layer</code> argument.</p>
-<div id="741bcc38" class="cell" data-execution_count="34">
+<div id="45cd0426" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>placemarks <span class="op">=</span> gpd.read_file(u, layer<span class="op">=</span><span class="st">'Placemarks'</span>)</span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>placemarks</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="30">
@@ -1555,7 +1555,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="sec-input-vector"><span
 <section id="sec-input-raster" class="level3" data-number="7.5.2">
 <h3 data-number="7.5.2" class="anchored" data-anchor-id="sec-input-raster"><span class="header-section-number">7.5.2</span> Raster data</h3>
 <p>Similar to vector data, raster data comes in many file formats, some of which support multilayer files. <code>rasterio.open</code> is used to create a file connection to a raster file, which can be subsequently used to read the metadata and/or the values, as shown previously (<a href="01-spatial-data.html#sec-using-rasterio" class="quarto-xref"><span>Section 1.3.1</span></a>).</p>
-<div id="8dc26c55" class="cell" data-execution_count="35">
+<div id="cc46205f" class="cell" data-execution_count="35">
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>src <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/srtm.tif'</span>)</span>
 <span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>src</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="31">
@@ -1563,7 +1563,7 @@ <h3 data-number="7.5.2" class="anchored" data-anchor-id="sec-input-raster"><span
 </div>
 </div>
 <p>All of the previous examples, like the one above, read spatial information from files stored on your hard drive. However, GDAL also allows reading data directly from online resources, such as HTTP/HTTPS/FTP web resources. Let’s try it by connecting to the global monthly snow probability at 500 <span class="math inline">\(m\)</span> resolution for the period 2000-2012 <span class="citation" data-cites="hengl_t_2021_5774954">(<a href="references.html#ref-hengl_t_2021_5774954" role="doc-biblioref">Hengl 2021</a>)</span>. Snow probability for December is stored as a Cloud Optimized GeoTIFF (COG) file (see <a href="#sec-file-formats" class="quarto-xref"><span>Section 7.4</span></a>) and can be accessed by its HTTPS URI.</p>
-<div id="fc8d7106" class="cell" data-execution_count="36">
+<div id="67e09802" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">'https://zenodo.org/record/5774954/files/'</span></span>
 <span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>url <span class="op">+=</span> <span class="st">'clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0.tif'</span></span>
 <span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>src <span class="op">=</span> rasterio.<span class="bu">open</span>(url)</span>
@@ -1575,14 +1575,14 @@ <h3 data-number="7.5.2" class="anchored" data-anchor-id="sec-input-raster"><span
 <p>In the example above <code>rasterio.open</code> creates a connection to the file without obtaining any values, as we did for the local <code>srtm.tif</code> file. The values can be read into an <code>ndarray</code> using the <code>.read</code> method of the file connection (<a href="01-spatial-data.html#sec-using-rasterio" class="quarto-xref"><span>Section 1.3.1</span></a>). Using parameters of <code>.read</code> allows us to just read a small portion of the data, without downloading the entire file. This is very useful when working with large datasets hosted online from resource-constrained computing environments such as laptops.</p>
 <p>For example, we can read a specified rectangular extent of the raster. With <strong>rasterio</strong>, this is done using the so-called <em>windowed reading</em> capabilities. Note that, with windowed reading, we import just a subset of the raster extent into an <code>ndarray</code> covering any partial extent. Windowed reading is therefore memory- (and, in this case, bandwidth-) efficient, since it avoids reading the entire raster into memory. It can also be considered an alternative pathway to <em>cropping</em> (<a href="05-raster-vector.html#sec-raster-cropping" class="quarto-xref"><span>Section 5.2</span></a>).</p>
 <p>To read a raster <em>window</em>, let’s first define the bounding box coordinates. For example, here we use a <span class="math inline">\(10 \times 10\)</span> degrees extent coinciding with Reykjavik.</p>
-<div id="72cd81b5" class="cell" data-execution_count="37">
+<div id="7a3d3abc" class="cell" data-execution_count="37">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>xmin<span class="op">=-</span><span class="dv">30</span></span>
 <span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>xmax<span class="op">=-</span><span class="dv">20</span></span>
 <span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>ymin<span class="op">=</span><span class="dv">60</span></span>
 <span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a>ymax<span class="op">=</span><span class="dv">70</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Using the extent coordinates along with the raster transformation matrix, we create a window object, using the <code>rasterio.windows.from_bounds</code> function. This function basically ‘translates’ the extent from coordinates, to row/column ranges.</p>
-<div id="9c02d901" class="cell" data-execution_count="38">
+<div id="1cf38e17" class="cell" data-execution_count="38">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>w <span class="op">=</span> rasterio.windows.from_bounds(</span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>    left<span class="op">=</span>xmin, </span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>    bottom<span class="op">=</span>ymin,</span>
@@ -1596,7 +1596,7 @@ <h3 data-number="7.5.2" class="anchored" data-anchor-id="sec-input-raster"><span
 </div>
 </div>
 <p>Now we can read the partial array, according to the specified window <code>w</code>, by passing it to the <code>.read</code> method.</p>
-<div id="d4342073" class="cell" data-execution_count="39">
+<div id="694d6c8e" class="cell" data-execution_count="39">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> src.read(<span class="dv">1</span>, window<span class="op">=</span>w)</span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>r</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="35">
@@ -1610,7 +1610,7 @@ <h3 data-number="7.5.2" class="anchored" data-anchor-id="sec-input-raster"><span
 </div>
 </div>
 <p>Note that the transformation matrix of the window is not the same as that of the original raster (unless it incidentally starts from the top-left corner)! Therefore, we must re-create the transformation matrix, with the modified origin (<code>xmin</code>,<code>ymax</code>), yet the same resolution, as follows.</p>
-<div id="dda464e7" class="cell" data-execution_count="40">
+<div id="6dd5285f" class="cell" data-execution_count="40">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>w_transform <span class="op">=</span> rasterio.transform.from_origin(</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>    west<span class="op">=</span>xmin, </span>
 <span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>    north<span class="op">=</span>ymax, </span>
@@ -1642,7 +1642,7 @@ <h3 data-number="7.5.2" class="anchored" data-anchor-id="sec-input-raster"><span
 </div>
 </div>
 <p>Another option is to extract raster values at particular points, directly from the file connection, using the <code>.sample</code> method (see <a href="03-spatial-operations.html#sec-spatial-subsetting-raster" class="quarto-xref"><span>Section 3.3.1</span></a>). For example, we can get the snow probability for December in Reykjavik (70%) by specifying its coordinates and applying <code>.sample</code>.</p>
-<div id="c790df1b" class="cell" data-execution_count="42">
+<div id="6dc2aead" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>coords <span class="op">=</span> (<span class="op">-</span><span class="fl">21.94</span>, <span class="fl">64.15</span>)</span>
 <span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a>values <span class="op">=</span> src.sample([coords])</span>
 <span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>(values)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1652,7 +1652,7 @@ <h3 data-number="7.5.2" class="anchored" data-anchor-id="sec-input-raster"><span
 </div>
 <p>The example above efficiently extracts and downloads a single value instead of the entire GeoTIFF file, saving valuable resources.</p>
 <p>Note that URIs can also identify <em>vector</em> datasets, enabling you to import datasets from online storage with <strong>geopandas</strong>, including datasets within ZIP archives hosted on the web.</p>
-<div id="49ee4186" class="cell" data-execution_count="43">
+<div id="652154fa" class="cell" data-execution_count="43">
 <div class="sourceCode cell-code" id="cb45"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>gpd.read_file(<span class="st">"zip+https://github.com/Toblerity/Fiona/files/11151652/coutwildrnp.zip"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="39">
 <div>
@@ -1742,32 +1742,32 @@ <h2 data-number="7.6" class="anchored" data-anchor-id="sec-data-output"><span cl
 <h3 data-number="7.6.1" class="anchored" data-anchor-id="vector-data"><span class="header-section-number">7.6.1</span> Vector data</h3>
 <p>The counterpart of <code>gpd.read_file</code> is the <code>.to_file</code> method that a <code>GeoDataFrame</code> has. It allows you to write <code>GeoDataFrame</code> objects to a wide range of geographic vector file formats, including the most common ones, such as <code>.geojson</code>, <code>.shp</code> and <code>.gpkg</code>. Based on the file name, <code>.to_file</code> decides automatically which driver to use. The speed of the writing process depends also on the driver.</p>
 <p>For example, to export the <code>world</code> layer to a GeoPackage file, we can use <code>.to_file</code> and specify the output file name.</p>
-<div id="f8351b61" class="cell" data-execution_count="44">
+<div id="07784337" class="cell" data-execution_count="44">
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>world.to_file(<span class="st">'output/world.gpkg'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Note, that if you try to write to the same data source again, the function will overwrite the file.</p>
-<div id="0facde33" class="cell" data-execution_count="45">
+<div id="ceed5b2a" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a>world.to_file(<span class="st">'output/world.gpkg'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Instead of overwriting the file, we could add new rows to the file with <code>mode='a'</code> (‘append’ mode, as opposed to the default <code>mode='w'</code> for the ‘write’ mode). Appending is supported by several spatial formats, including GeoPackage.</p>
-<div id="0b79ce56" class="cell" data-execution_count="46">
+<div id="bc4fd574" class="cell" data-execution_count="46">
 <div class="sourceCode cell-code" id="cb48"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a>world.to_file(<span class="st">'output/w_many_features.gpkg'</span>)</span>
 <span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a>world.to_file(<span class="st">'output/w_many_features.gpkg'</span>, mode<span class="op">=</span><span class="st">'a'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, <code>w_many_features.gpkg</code> contains a polygonal layer named <code>world</code> with two ‘copies’ of each country (that is 177×2=354 features, whereas the <code>world</code> layer has 177 features).</p>
-<div id="c96cd66a" class="cell" data-execution_count="47">
+<div id="5fd799a9" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb49"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>gpd.read_file(<span class="st">'output/w_many_features.gpkg'</span>).shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="43">
 <pre><code>(354, 11)</code></pre>
 </div>
 </div>
 <p>Alternatively, you can create another, separate, layer, within the same file, which is supported by some formats, including GeoPackage.</p>
-<div id="12c059d3" class="cell" data-execution_count="48">
+<div id="700f2c32" class="cell" data-execution_count="48">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>world.to_file(<span class="st">'output/w_many_layers.gpkg'</span>)</span>
 <span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a>world.to_file(<span class="st">'output/w_many_layers.gpkg'</span>, layer<span class="op">=</span><span class="st">'world2'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>In this case, <code>w_many_layers.gpkg</code> has two ‘layers’: <code>w_many_layers</code> (same as the file name, when <code>layer</code> is unspecified) and <code>world2</code>. Incidentally, the contents of the two layers are identical, but this does not have to be. Each layer from such a file can be imported separately using the <code>layer</code> argument of <code>gpd.read_file</code>.</p>
-<div id="e492d679" class="cell" data-execution_count="49">
+<div id="4be3e327" class="cell" data-execution_count="49">
 <div class="sourceCode cell-code" id="cb52"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a>layer1 <span class="op">=</span> gpd.read_file(<span class="st">'output/w_many_layers.gpkg'</span>, layer<span class="op">=</span><span class="st">'w_many_layers'</span>)</span>
 <span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a>layer2 <span class="op">=</span> gpd.read_file(<span class="st">'output/w_many_layers.gpkg'</span>, layer<span class="op">=</span><span class="st">'world2'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -1825,7 +1825,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </ul></li>
 </ol>
 <p>A miminal example of writing a raster file named <code>r.tif</code> from scratch, to remind the main concepts, is given below. First, we create a small <span class="math inline">\(2 \times 2\)</span> array.</p>
-<div id="6556fbdd" class="cell" data-execution_count="50">
+<div id="7bda405e" class="cell" data-execution_count="50">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> np.array([<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>]).reshape(<span class="dv">2</span>,<span class="dv">2</span>).astype(np.int8)</span>
 <span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a>r</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="46">
@@ -1834,7 +1834,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Next, we define a transformation matrix, specifying the origin and resolution.</p>
-<div id="bf3e7d99" class="cell" data-execution_count="51">
+<div id="931e197c" class="cell" data-execution_count="51">
 <div class="sourceCode cell-code" id="cb55"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>new_transform <span class="op">=</span> rasterio.transform.from_origin(</span>
 <span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a>    west<span class="op">=-</span><span class="fl">0.5</span>, </span>
 <span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a>    north<span class="op">=</span><span class="fl">51.5</span>, </span>
@@ -1848,7 +1848,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Then, we establish the writing-mode file connection to <code>r.tif</code>, which will be either created or overwritten.</p>
-<div id="8a98185a" class="cell" data-execution_count="52">
+<div id="8ddc73a0" class="cell" data-execution_count="52">
 <div class="sourceCode cell-code" id="cb57"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>dst <span class="op">=</span> rasterio.<span class="bu">open</span>(</span>
 <span id="cb57-2"><a href="#cb57-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">'output/r.tif'</span>, <span class="st">'w'</span>, </span>
 <span id="cb57-3"><a href="#cb57-3" aria-hidden="true" tabindex="-1"></a>    driver <span class="op">=</span> <span class="st">'GTiff'</span>,</span>
@@ -1865,11 +1865,11 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Next, we write the array of values into the file connection with the <code>.write</code> method. Keep in mind that <code>r</code> here is a two-dimensional array representing one band, and <code>1</code> is the band index where the array is written into the file.</p>
-<div id="995ba9be" class="cell" data-execution_count="53">
+<div id="3d8a0304" class="cell" data-execution_count="53">
 <div class="sourceCode cell-code" id="cb59"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a>dst.write(r, <span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Finally, we close the connection.</p>
-<div id="d37727af" class="cell" data-execution_count="54">
+<div id="b96eb21b" class="cell" data-execution_count="54">
 <div class="sourceCode cell-code" id="cb60"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb60-1"><a href="#cb60-1" aria-hidden="true" tabindex="-1"></a>dst.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>These expressions, taken together, create a new file <code>output/r.tif</code>, which is a <span class="math inline">\(2 \times 2\)</span> raster, having a 2 decimal degree resolution, with the top-left corner placed over London.</p>
@@ -1956,21 +1956,21 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </figure>
 </div>
 <p>The raster data type needs to be specified when writing a raster, typically using the same type as that of the array to be written (e.g., see the <code>dtype=r.dtype</code> part in the last example). For an existing raster file, the data type can be queried through the <code>.dtype</code> property of the metadata (<code>.meta['dtype']</code>).</p>
-<div id="6687976a" class="cell" data-execution_count="55">
+<div id="7f34d0e0" class="cell" data-execution_count="55">
 <div class="sourceCode cell-code" id="cb61"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a>rasterio.<span class="bu">open</span>(<span class="st">'output/r.tif'</span>).meta[<span class="st">'dtype'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="51">
 <pre><code>'int8'</code></pre>
 </div>
 </div>
 <p>The above expression shows that the GeoTIFF file <code>r.tif</code> has the data type <code>np.int8</code>, as specified when creating the file with <code>rasterio.open</code>, according to the data type of the array we wrote into the file (<code>dtype=r.dtype</code>).</p>
-<div id="872b7513" class="cell" data-execution_count="56">
+<div id="53b551ff" class="cell" data-execution_count="56">
 <div class="sourceCode cell-code" id="cb63"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a>r.dtype</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="52">
 <pre><code>dtype('int8')</code></pre>
 </div>
 </div>
 <p>When reading the raster file back into the Python session, the exact same array is recreated.</p>
-<div id="3e97237a" class="cell" data-execution_count="57">
+<div id="303275a5" class="cell" data-execution_count="57">
 <div class="sourceCode cell-code" id="cb65"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a>rasterio.<span class="bu">open</span>(<span class="st">'output/r.tif'</span>).read().dtype</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="53">
 <pre><code>dtype('int8')</code></pre>
@@ -1983,7 +1983,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 <li>Execute the <code>.write</code> method multiple times, once for each layer</li>
 </ul>
 <p>For completeness, let’s demonstrate writing a multi-band raster named <code>r3.tif</code>, which is similar to <code>r.tif</code>, but having three bands with values <code>r*1</code>, <code>r*2</code>, and <code>r*3</code> (i.e., the array <code>r</code> multiplied by <code>1</code>, <code>2</code>, or <code>3</code>). Since most of the metadata is going to be the same, this is also a good opportunity to (re-)demonstrate updating an existing metadata object rather than creating one from scratch. First, let’s make a copy of the metadata we already have in <code>r.tif</code>.</p>
-<div id="150d1019" class="cell" data-execution_count="58">
+<div id="abff2072" class="cell" data-execution_count="58">
 <div class="sourceCode cell-code" id="cb67"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>dst_kwds <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/r.tif'</span>).meta</span>
 <span id="cb67-2"><a href="#cb67-2" aria-hidden="true" tabindex="-1"></a>dst_kwds</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="54">
@@ -1999,7 +1999,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Second, we update the <code>count</code> entry, replacing <code>1</code> (single-band) with <code>3</code> (three-band) using the <code>.update</code> method.</p>
-<div id="dd9817b8" class="cell" data-execution_count="59">
+<div id="b57bf3a9" class="cell" data-execution_count="59">
 <div class="sourceCode cell-code" id="cb69"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a>dst_kwds.update(count<span class="op">=</span><span class="dv">3</span>)</span>
 <span id="cb69-2"><a href="#cb69-2" aria-hidden="true" tabindex="-1"></a>dst_kwds</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="55">
@@ -2015,7 +2015,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Finally, we can create a file connection using the updated metadata, write the values of the three bands, and close the connection (note that we are switching to the ‘keyword argument’ syntax of Python function calls here; see note in <a href="04-geometry-operations.html#sec-raster-agg-disagg" class="quarto-xref"><span>Section 4.3.2</span></a>).</p>
-<div id="62cf82e1" class="cell" data-execution_count="60">
+<div id="55c776af" class="cell" data-execution_count="60">
 <div class="sourceCode cell-code" id="cb71"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" tabindex="-1"></a>dst <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/r3.tif'</span>, <span class="st">'w'</span>, <span class="op">**</span>dst_kwds)</span>
 <span id="cb71-2"><a href="#cb71-2" aria-hidden="true" tabindex="-1"></a>dst.write(r<span class="op">*</span><span class="dv">1</span>, <span class="dv">1</span>)</span>
 <span id="cb71-3"><a href="#cb71-3" aria-hidden="true" tabindex="-1"></a>dst.write(r<span class="op">*</span><span class="dv">2</span>, <span class="dv">2</span>)</span>
@@ -2025,7 +2025,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 <p>As a result, a three-band raster named <code>r3.tif</code> is created.</p>
 <p>Rasters often contain ‘No Data’ values, representing missing data, for example, unreliable measurements due to clouds or pixels outside of the photographed extent. In a <strong>numpy</strong> <code>ndarray</code> object, ‘No Data’ values may be represented by the special <code>np.nan</code> value. However, due to computer memory limitations, only arrays of type <code>float</code> can contain <code>np.nan</code>, while arrays of type <code>int</code> cannot. For <code>int</code> rasters containing ‘No Data’, we typically mark missing data with a specific value beyond the valid range (e.g., <code>-9999</code>). The missing data ‘flag’ definition is stored in the file (set through the <code>nodata</code> property of the file connection, see above). When reading an <code>int</code> raster with ‘No Data’ back into Python, we need to be aware of the flag, if any. Let’s demonstrate it through examples.</p>
 <p>We will start with the simpler case, rasters of type <code>float</code>. Since <code>float</code> arrays may contain the ‘native’ value <code>np.nan</code>, representing ‘No Data’ is straightforward. For example, suppose that we have a <code>float</code> array of size <span class="math inline">\(2 \times 2\)</span> containing one <code>np.nan</code> value.</p>
-<div id="44061b80" class="cell" data-execution_count="61">
+<div id="d7a73bf7" class="cell" data-execution_count="61">
 <div class="sourceCode cell-code" id="cb72"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb72-1"><a href="#cb72-1" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> np.array([<span class="fl">1.1</span>,<span class="fl">2.1</span>,np.nan,<span class="fl">4.1</span>]).reshape(<span class="dv">2</span>,<span class="dv">2</span>)</span>
 <span id="cb72-2"><a href="#cb72-2" aria-hidden="true" tabindex="-1"></a>r</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="57">
@@ -2033,14 +2033,14 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
        [nan, 4.1]])</code></pre>
 </div>
 </div>
-<div id="122041c0" class="cell" data-execution_count="62">
+<div id="6b6db26b" class="cell" data-execution_count="62">
 <div class="sourceCode cell-code" id="cb74"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb74-1"><a href="#cb74-1" aria-hidden="true" tabindex="-1"></a>r.dtype</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="58">
 <pre><code>dtype('float64')</code></pre>
 </div>
 </div>
 <p>When writing this type of array to a raster file, we do not need to specify any particular <code>nodata</code> ‘flag’ value.</p>
-<div id="21f940e6" class="cell" data-execution_count="63">
+<div id="6de780e1" class="cell" data-execution_count="63">
 <div class="sourceCode cell-code" id="cb76"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb76-1"><a href="#cb76-1" aria-hidden="true" tabindex="-1"></a>dst <span class="op">=</span> rasterio.<span class="bu">open</span>(</span>
 <span id="cb76-2"><a href="#cb76-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">'output/r_nodata_float.tif'</span>, <span class="st">'w'</span>, </span>
 <span id="cb76-3"><a href="#cb76-3" aria-hidden="true" tabindex="-1"></a>    driver <span class="op">=</span> <span class="st">'GTiff'</span>,</span>
@@ -2055,7 +2055,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 <span id="cb76-12"><a href="#cb76-12" aria-hidden="true" tabindex="-1"></a>dst.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>This is equivalent to <code>nodata=None</code>.</p>
-<div id="00185036" class="cell" data-execution_count="64">
+<div id="fb618ef6" class="cell" data-execution_count="64">
 <div class="sourceCode cell-code" id="cb77"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb77-1"><a href="#cb77-1" aria-hidden="true" tabindex="-1"></a>rasterio.<span class="bu">open</span>(<span class="st">'output/r_nodata_float.tif'</span>).meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="60">
 <pre><code>{'driver': 'GTiff',
@@ -2070,7 +2070,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Reading from the raster back into the Python session reproduces the same exact array, including <code>np.nan</code>.</p>
-<div id="840cd4c4" class="cell" data-execution_count="65">
+<div id="3ed5a8ea" class="cell" data-execution_count="65">
 <div class="sourceCode cell-code" id="cb79"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb79-1"><a href="#cb79-1" aria-hidden="true" tabindex="-1"></a>rasterio.<span class="bu">open</span>(<span class="st">'output/r_nodata_float.tif'</span>).read()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="61">
 <pre><code>array([[[1.1, 2.1],
@@ -2078,7 +2078,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Now, conversely, suppose that we have an <code>int</code> array with missing data, where the ‘missing’ value must inevitably be marked using a specific <code>int</code> ‘flag’ value, such as <code>-9999</code> (remember that we can’t store <code>np.nan</code> in an <code>int</code> array!).</p>
-<div id="b7711a5e" class="cell" data-execution_count="66">
+<div id="a76a7f0e" class="cell" data-execution_count="66">
 <div class="sourceCode cell-code" id="cb81"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb81-1"><a href="#cb81-1" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> np.array([<span class="dv">1</span>,<span class="dv">2</span>,<span class="op">-</span><span class="dv">9999</span>,<span class="dv">4</span>]).reshape(<span class="dv">2</span>,<span class="dv">2</span>).astype(np.int32)</span>
 <span id="cb81-2"><a href="#cb81-2" aria-hidden="true" tabindex="-1"></a>r</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="62">
@@ -2086,14 +2086,14 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
        [-9999,     4]], dtype=int32)</code></pre>
 </div>
 </div>
-<div id="9c0f59a9" class="cell" data-execution_count="67">
+<div id="8737ca04" class="cell" data-execution_count="67">
 <div class="sourceCode cell-code" id="cb83"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb83-1"><a href="#cb83-1" aria-hidden="true" tabindex="-1"></a>r.dtype</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="63">
 <pre><code>dtype('int32')</code></pre>
 </div>
 </div>
 <p>When writing the array to file, we must specify <code>nodata=-9999</code> to keep track of our ‘No Data’ flag.</p>
-<div id="99d8cbd8" class="cell" data-execution_count="68">
+<div id="4b8eaaa4" class="cell" data-execution_count="68">
 <div class="sourceCode cell-code" id="cb85"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb85-1"><a href="#cb85-1" aria-hidden="true" tabindex="-1"></a>dst <span class="op">=</span> rasterio.<span class="bu">open</span>(</span>
 <span id="cb85-2"><a href="#cb85-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">'output/r_nodata_int.tif'</span>, <span class="st">'w'</span>, </span>
 <span id="cb85-3"><a href="#cb85-3" aria-hidden="true" tabindex="-1"></a>    driver <span class="op">=</span> <span class="st">'GTiff'</span>,</span>
@@ -2109,7 +2109,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 <span id="cb85-13"><a href="#cb85-13" aria-hidden="true" tabindex="-1"></a>dst.close()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Examining the metadata of the file we’ve just created confirms that the <code>nodata=-9999</code> setting was stored in the file <code>r_nodata_int.tif</code>.</p>
-<div id="15c33f06" class="cell" data-execution_count="69">
+<div id="0e427bcb" class="cell" data-execution_count="69">
 <div class="sourceCode cell-code" id="cb86"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb86-1"><a href="#cb86-1" aria-hidden="true" tabindex="-1"></a>rasterio.<span class="bu">open</span>(<span class="st">'output/r_nodata_int.tif'</span>).meta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="65">
 <pre><code>{'driver': 'GTiff',
@@ -2124,7 +2124,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>If you try to open the file in GIS software, such as QGIS, you will see the missing data interpreted (e.g., the pixel shown as blank), meaning that the software is aware of the flag. However, reading the data back into Python reproduces an <code>int</code> array with <code>-9999</code>, due to the limitation of <code>int</code> arrays stated before.</p>
-<div id="7d5f0e05" class="cell" data-execution_count="70">
+<div id="da193e92" class="cell" data-execution_count="70">
 <div class="sourceCode cell-code" id="cb88"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb88-1"><a href="#cb88-1" aria-hidden="true" tabindex="-1"></a>src <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'output/r_nodata_int.tif'</span>)</span>
 <span id="cb88-2"><a href="#cb88-2" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> src.read()</span>
 <span id="cb88-3"><a href="#cb88-3" aria-hidden="true" tabindex="-1"></a>r</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2134,14 +2134,14 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>The Python user must therefore be mindful of ‘No Data’ <code>int</code> rasters, for example to avoid interpreting the value <code>-9999</code> literally. For instance, if we ‘forget’ about the <code>nodata</code> flag, the literal calculation of the <code>.mean</code> would incorrectly include the value <code>-9999</code>.</p>
-<div id="734bf539" class="cell" data-execution_count="71">
+<div id="7eb5d385" class="cell" data-execution_count="71">
 <div class="sourceCode cell-code" id="cb90"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb90-1"><a href="#cb90-1" aria-hidden="true" tabindex="-1"></a>r.mean()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="67">
 <pre><code>np.float64(-2498.0)</code></pre>
 </div>
 </div>
 <p>There are two basic ways to deal with the situation: either converting the raster to <code>float</code>, or using a ‘No Data’ mask. The first approach, simple and particularly relevant for small rasters where memory constraints are irrelevant, is to go from <code>int</code> to <code>float</code>, to gain the ability of the natural <code>np.nan</code> representation. Here is how we can do this with <code>r_nodata_int.tif</code>. We detect the missing data flag, convert the raster to <code>float</code>, then assign <code>np.nan</code> into the cells that are supposed to be missing.</p>
-<div id="20d65b87" class="cell" data-execution_count="72">
+<div id="ec089e54" class="cell" data-execution_count="72">
 <div class="sourceCode cell-code" id="cb92"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a>mask <span class="op">=</span> r <span class="op">==</span> src.nodata</span>
 <span id="cb92-2"><a href="#cb92-2" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> r.astype(np.float64)</span>
 <span id="cb92-3"><a href="#cb92-3" aria-hidden="true" tabindex="-1"></a>r[mask] <span class="op">=</span> np.nan</span>
@@ -2152,14 +2152,14 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>From there on, we deal with <code>np.nan</code> the usual way, such as using <code>np.nanmean</code> to calculate the mean excluding ‘No Data’.</p>
-<div id="229990d5" class="cell" data-execution_count="73">
+<div id="6012e09b" class="cell" data-execution_count="73">
 <div class="sourceCode cell-code" id="cb94"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb94-1"><a href="#cb94-1" aria-hidden="true" tabindex="-1"></a>np.nanmean(r)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="69">
 <pre><code>np.float64(2.3333333333333335)</code></pre>
 </div>
 </div>
 <p>The second approach is to read the values into a so-called <em>‘masked’ array</em>, using the argument <code>masked=True</code> of the <code>.read</code> method. A masked array can be thought of as an extended <code>ndarray</code>, with two components: <code>.data</code> (the values) and <code>.mask</code> (a corresponding boolean array marking ‘No Data’ values).</p>
-<div id="bd9eefb8" class="cell" data-execution_count="74">
+<div id="4fe64431" class="cell" data-execution_count="74">
 <div class="sourceCode cell-code" id="cb96"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb96-1"><a href="#cb96-1" aria-hidden="true" tabindex="-1"></a>r <span class="op">=</span> src.read(masked<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb96-2"><a href="#cb96-2" aria-hidden="true" tabindex="-1"></a>r</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="70">
@@ -2173,7 +2173,7 @@ <h3 data-number="7.6.2" class="anchored" data-anchor-id="sec-data-output-raster"
 </div>
 </div>
 <p>Complete treatment of masked arrays is beyond the scope of this book. However, the basic idea is that many <strong>numpy</strong> operations ‘honor’ the mask, so that the user does not have to keep track of the way that ‘No Data’ values are marked, similarly to the natural <code>np.nan</code> representation and regardless of the data type. For example, the <code>.mean</code> of a masked array ignores the value <code>-9999</code>, because it is masked, taking into account just the valid values <code>1</code>, <code>2</code>, and <code>4</code>.</p>
-<div id="a76fa11a" class="cell" data-execution_count="75">
+<div id="43cd5439" class="cell" data-execution_count="75">
 <div class="sourceCode cell-code" id="cb98"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb98-1"><a href="#cb98-1" aria-hidden="true" tabindex="-1"></a>r.mean()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="71">
 <pre><code>np.float64(2.3333333333333335)</code></pre>
diff --git a/08-mapping.html b/08-mapping.html
index f634af44..c608a1ce 100644
--- a/08-mapping.html
+++ b/08-mapping.html
@@ -364,7 +364,7 @@ <h1 class="title"><span id="sec-map-making" class="quarto-section-identifier"><s
 <section id="prerequisites" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2>
 <p>This chapter requires importing the following packages:</p>
-<div id="6c8b2bd4" class="cell" data-execution_count="5">
+<div id="a9ffda3a" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> geopandas <span class="im">as</span> gpd</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio</span>
@@ -373,7 +373,7 @@ <h2 class="unnumbered anchored" data-anchor-id="prerequisites">Prerequisites</h2
 <span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> folium</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>It also relies on the following data files:</p>
-<div id="eb974b73" class="cell" data-execution_count="6">
+<div id="ae4922df" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>nz <span class="op">=</span> gpd.read_file(<span class="st">'data/nz.gpkg'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>nz_height <span class="op">=</span> gpd.read_file(<span class="st">'data/nz_height.gpkg'</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>nz_elev <span class="op">=</span> rasterio.<span class="bu">open</span>(<span class="st">'data/nz_elev.tif'</span>)</span>
@@ -706,7 +706,7 @@ <h3 data-number="8.2.3" class="anchored" data-anchor-id="sec-plot-symbology"><sp
 <h3 data-number="8.2.4" class="anchored" data-anchor-id="sec-plot-static-labels"><span class="header-section-number">8.2.4</span> Labels</h3>
 <p>Labels are often useful to annotate maps and identify the location of specific features. GIS software, as opposed to <strong>matplotlib</strong>, has specialized algorithms for label placement, e.g., to avoid overlaps between adjacent labels. Furthermore, editing in graphical editing software is sometimes used for fine-tuning of label placement. Nevertheless, simple labels added within the Python environment can be a good starting point, both for interactive exploration and sharing analysis results.</p>
 <p>To demonstrate it, suppose that we have a layer <code>nz1</code> of regions comprising the New Zealand southern Island.</p>
-<div id="9f8a5322" class="cell" data-execution_count="17">
+<div id="dfb52848" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>nz1 <span class="op">=</span> nz[nz[<span class="st">'Island'</span>] <span class="op">==</span> <span class="st">'South'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>To add a label in <strong>matplotlib</strong>, we use the <code>.annotate</code> method where the important arguments are the label string and the placement (a <code>tuple</code> of the form <code>(x,y)</code>). When labeling vector layers, we typically want to add numerous labels, based on (one or more) attribute of each feature. To do that, we can run a <code>for</code> loop, or use the <code>.apply</code> method, to pass the label text and the coordinates of each feature to <code>.annotate</code>. In the following example, we use the <code>.apply</code> method the pass the region name (<code>'Name'</code> attribute) and the geometry centroid coordinates, for each region, to <code>.annotate</code>. We are also using <code>ha</code>, short for <code>horizontalalignment</code>, with <code>'center'</code> (other options are <code>'right'</code> and <code>'left'</code>) (<a href="#fig-labels-polygon" class="quarto-xref">Figure&nbsp;<span>8.11</span></a>).</p>
@@ -735,7 +735,7 @@ <h3 data-number="8.2.4" class="anchored" data-anchor-id="sec-plot-static-labels"
 </div>
 </div>
 <p>As another example, let’s create a map of all regions of New Zealand, with labels for the island names. First, we will calculate the island centroids, which will be the label placement positions.</p>
-<div id="4cce8768" class="cell" data-execution_count="19">
+<div id="03a4ffe9" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>ctr <span class="op">=</span> nz[[<span class="st">'Island'</span>, <span class="st">'geometry'</span>]].dissolve(by<span class="op">=</span><span class="st">'Island'</span>).reset_index()</span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>ctr[<span class="st">'geometry'</span>] <span class="op">=</span> ctr.centroid</span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>ctr</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -955,7 +955,7 @@ <h3 data-number="8.2.5" class="anchored" data-anchor-id="sec-plot-static-layers"
 <h3 data-number="8.2.6" class="anchored" data-anchor-id="basemaps"><span class="header-section-number">8.2.6</span> Basemaps</h3>
 <p>Basemaps, or background layers, are often useful to provide context to the displayed layers (which are in the ‘foreground’). Basemaps are ubiquitous in interactive maps (see <a href="#sec-interactive-maps" class="quarto-xref"><span>Section 8.3</span></a>). However, they are often useful in static maps too.</p>
 <p>Basemaps can be added to <strong>geopandas</strong> static plots using the <strong>contextily</strong> package. A preliminary step is to convert our layers to <code>EPSG:3857</code> (‘Web Mercator’), to be in agreement with the basemaps, which are typically provided in this CRS<a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a>. For example, let’s take the small <code>"Nelson"</code> polygon from <code>nz</code>, and reproject it to <code>3857</code>.</p>
-<div id="8e6ac591" class="cell" data-execution_count="25">
+<div id="94a151e8" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>nzw <span class="op">=</span> nz[nz[<span class="st">'Name'</span>] <span class="op">==</span> <span class="st">'Nelson'</span>].to_crs(epsg<span class="op">=</span><span class="dv">3857</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>To add a basemap, we use the <code>contextily.add_basemap</code> function, similarly to the way we added multiple layers (<a href="#sec-plot-static-layers" class="quarto-xref"><span>Section 8.2.5</span></a>). The default basemap is ‘OpenStreetMap’. You can specify a different basemap using the <code>source</code> parameter, with one of the values in <code>cx.providers</code> (<a href="#fig-basemap" class="quarto-xref">Figure&nbsp;<span>8.17</span></a>).</p>
@@ -1007,7 +1007,7 @@ <h3 data-number="8.2.6" class="anchored" data-anchor-id="basemaps"><span class="
 <section id="sec-faceted-maps" class="level3" data-number="8.2.7">
 <h3 data-number="8.2.7" class="anchored" data-anchor-id="sec-faceted-maps"><span class="header-section-number">8.2.7</span> Faceted maps</h3>
 <p>Faceted maps are multiple maps displaying the same symbology for the same spatial layers, but with different data in each panel. The data displayed in the different panels typically refer to different properties, or time steps. For example, the <code>nz</code> layer has several different properties for each polygon, stored as separate attributes:</p>
-<div id="a08ef67a" class="cell" data-execution_count="27">
+<div id="8c184969" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="bu">vars</span> <span class="op">=</span> [<span class="st">'Land_area'</span>, <span class="st">'Population'</span>, <span class="st">'Median_income'</span>, <span class="st">'Sex_ratio'</span>]</span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>nz[<span class="bu">vars</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
@@ -1151,20 +1151,20 @@ <h3 data-number="8.2.7" class="anchored" data-anchor-id="sec-faceted-maps"><span
 <section id="sec-exporting-static-maps" class="level3" data-number="8.2.8">
 <h3 data-number="8.2.8" class="anchored" data-anchor-id="sec-exporting-static-maps"><span class="header-section-number">8.2.8</span> Exporting</h3>
 <p>Static maps can be exported to a file using the <code>matplotlib.pyplot.savefig</code> function. For example, the following code section recreates <a href="#fig-two-layers" class="quarto-xref">Figure&nbsp;<span>8.14</span></a>, but this time the last expression saves the image to a JPG image named <code>plot_geopandas.jpg</code>.</p>
-<div id="eb32b4e3" class="cell" data-execution_count="31">
+<div id="25da0888" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>base <span class="op">=</span> nz.plot(color<span class="op">=</span><span class="st">'none'</span>)</span>
 <span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>nz_height.plot(ax<span class="op">=</span>base, color<span class="op">=</span><span class="st">'red'</span>)<span class="op">;</span></span>
 <span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>plt.savefig(<span class="st">'output/plot_geopandas.jpg'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Figures with rasters can be exported exactly the same way. For example, the following code section (<a href="#sec-plot-static-layers" class="quarto-xref"><span>Section 8.2.5</span></a>) creates an image of a raster and a vector layer, which is then exported to a file named <code>plot_rasterio.jpg</code>.</p>
-<div id="aa02a6e1" class="cell" data-execution_count="32">
+<div id="5849a28a" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">5</span>, <span class="dv">5</span>))</span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>rasterio.plot.show(nz_elev, ax<span class="op">=</span>ax)</span>
 <span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>nz.to_crs(nz_elev.crs).plot(ax<span class="op">=</span>ax, facecolor<span class="op">=</span><span class="st">'none'</span>, edgecolor<span class="op">=</span><span class="st">'r'</span>)<span class="op">;</span></span>
 <span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>plt.savefig(<span class="st">'output/plot_rasterio.jpg'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Image file properties can be controlled through the <code>plt.subplots</code> and <code>plt.savefig</code> parameters. For example, the following code section exports the same raster plot to a file named <code>plot_rasterio2.svg</code>, which has different dimensions (width = 5 <span class="math inline">\(in\)</span>, height = 7 <span class="math inline">\(in\)</span>), a different format (SVG), and different resolution (300 <span class="math inline">\(DPI\)</span>).</p>
-<div id="e6ba9237" class="cell" data-execution_count="33">
+<div id="d9de19f9" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">5</span>, <span class="dv">7</span>))</span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>rasterio.plot.show(nz_elev, ax<span class="op">=</span>ax)</span>
 <span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>nz.to_crs(nz_elev.crs).plot(ax<span class="op">=</span>ax, facecolor<span class="op">=</span><span class="st">'none'</span>, edgecolor<span class="op">=</span><span class="st">'r'</span>)<span class="op">;</span></span>
@@ -1211,7 +1211,7 @@ <h3 data-number="8.3.1" class="anchored" data-anchor-id="minimal-example"><span
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_29956df017963fd59de131d279ed4aa6 {
+                #map_0ea21f0060a2073f9a2661f62e960fd9 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -1241,14 +1241,14 @@ <h3 data-number="8.3.1" class="anchored" data-anchor-id="minimal-example"><span
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_29956df017963fd59de131d279ed4aa6&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_0ea21f0060a2073f9a2661f62e960fd9&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_29956df017963fd59de131d279ed4aa6 = L.map(
-                &quot;map_29956df017963fd59de131d279ed4aa6&quot;,
+            var map_0ea21f0060a2073f9a2661f62e960fd9 = L.map(
+                &quot;map_0ea21f0060a2073f9a2661f62e960fd9&quot;,
                 {
                     center: [-40.84868556642058, 172.48833825792866],
                     crs: L.CRS.EPSG3857,
@@ -1257,79 +1257,79 @@ <h3 data-number="8.3.1" class="anchored" data-anchor-id="minimal-example"><span
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_29956df017963fd59de131d279ed4aa6);
+            L.control.scale().addTo(map_0ea21f0060a2073f9a2661f62e960fd9);
 
             
 
         
     
-            var tile_layer_39c7d54d981c6fcde4802457a9039282 = L.tileLayer(
+            var tile_layer_aece2c39598fd35fb5fe31b633e8d097 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_39c7d54d981c6fcde4802457a9039282.addTo(map_29956df017963fd59de131d279ed4aa6);
+            tile_layer_aece2c39598fd35fb5fe31b633e8d097.addTo(map_0ea21f0060a2073f9a2661f62e960fd9);
         
     
-            map_29956df017963fd59de131d279ed4aa6.fitBounds(
+            map_0ea21f0060a2073f9a2661f62e960fd9.fitBounds(
                 [[-47.28285241600581, 166.42630291457417], [-34.414518716835346, 178.55037360128316]],
                 {}
             );
         
     
-        function geo_json_68215712b15148a8981540ca56b7751f_styler(feature) {
+        function geo_json_860df3d2b2976457d1e4b7b3f414f2a3_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_68215712b15148a8981540ca56b7751f_highlighter(feature) {
+        function geo_json_860df3d2b2976457d1e4b7b3f414f2a3_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_68215712b15148a8981540ca56b7751f_pointToLayer(feature, latlng) {
+        function geo_json_860df3d2b2976457d1e4b7b3f414f2a3_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_68215712b15148a8981540ca56b7751f_styler(feature)
+            let style = geo_json_860df3d2b2976457d1e4b7b3f414f2a3_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_68215712b15148a8981540ca56b7751f_onEachFeature(feature, layer) {
+        function geo_json_860df3d2b2976457d1e4b7b3f414f2a3_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_68215712b15148a8981540ca56b7751f.resetStyle(e.target);
+                            geo_json_860df3d2b2976457d1e4b7b3f414f2a3.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_68215712b15148a8981540ca56b7751f_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_860df3d2b2976457d1e4b7b3f414f2a3_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_68215712b15148a8981540ca56b7751f = L.geoJson(null, {
-                onEachFeature: geo_json_68215712b15148a8981540ca56b7751f_onEachFeature,
+        var geo_json_860df3d2b2976457d1e4b7b3f414f2a3 = L.geoJson(null, {
+                onEachFeature: geo_json_860df3d2b2976457d1e4b7b3f414f2a3_onEachFeature,
             
-                style: geo_json_68215712b15148a8981540ca56b7751f_styler,
-                pointToLayer: geo_json_68215712b15148a8981540ca56b7751f_pointToLayer,
+                style: geo_json_860df3d2b2976457d1e4b7b3f414f2a3_styler,
+                pointToLayer: geo_json_860df3d2b2976457d1e4b7b3f414f2a3_pointToLayer,
         });
 
-        function geo_json_68215712b15148a8981540ca56b7751f_add (data) {
-            geo_json_68215712b15148a8981540ca56b7751f
+        function geo_json_860df3d2b2976457d1e4b7b3f414f2a3_add (data) {
+            geo_json_860df3d2b2976457d1e4b7b3f414f2a3
                 .addData(data);
         }
-            geo_json_68215712b15148a8981540ca56b7751f_add({&quot;bbox&quot;: [166.42630291457417, -47.28285241600581, 178.55037360128316, -34.414518716835346], &quot;features&quot;: [{&quot;bbox&quot;: [172.653880649817, -36.39288306661124, 174.61663880065558, -34.414518716835346], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[174.61663880065558, -36.117560466595535], [174.5628090505642, -36.179000182759175], [174.48174833315713, -36.22827699994316], [174.33896590023375, -36.316829716883234], [174.21457326690228, -36.25871166608587], [174.12877275047865, -36.17361239945035], [174.04599758389062, -36.17016955012603], [174.09597213385467, -36.24488793346581], [174.1553684830745, -36.27609723272722], [174.18186668339544, -36.36303589981312], [174.13535050025587, -36.39204991691119], [174.04052755054076, -36.39288306661124], [173.9836758838081, -36.233704999678864], [173.8660469165703, -36.09120011624559], [173.68809813355304, -35.89241744971263], [173.3651805831832, -35.54740169951351], [173.35661301733074, -35.522817733419544], [173.05424363403117, -35.191238166349635], [173.13055999973318, -35.17893233285171], [173.1680931826238, -35.136427332750515], [173.16744721718428, -35.036368649459554], [173.08100574991772, -34.89239194966177], [172.85122291782795, -34.629713366319244], [172.653880649817, -34.478007650204745], [172.69995103368962, -34.42638239948652], [172.79306545076662, -34.453994282761165], [172.86903523358103, -34.414518716835346], [173.01444231654213, -34.42413689980644], [172.98479144999794, -34.52780733266056], [173.0251309837217, -34.65351909975858], [173.12020441726813, -34.81668376714118], [173.19932491641322, -34.87096884981918], [173.28077288324707, -34.89252611725473], [173.2148148999132, -34.96125576674768], [173.28839824919172, -35.01323163320772], [173.34008769973812, -34.95823121645178], [173.2869148834167, -34.86349856609251], [173.429163483407, -34.82514861607076], [173.38970963384807, -34.953496332924786], 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{&quot;bbox&quot;: [172.05212681699254, -42.280602516819684, 173.30414935048913, -40.50811923348418], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[173.19888739972865, -41.3306196997791], [173.2974439833663, -41.39314151628407], [173.25083138412685, -41.46658011655971], [173.30414935048913, -41.50603208360206], [173.16870053359213, -41.53115469953437], [173.15161466698348, -41.585470983182375], [173.0866526169933, -41.6249252333708], [173.00378965079662, -41.72480326694655], [172.92623520065564, -41.75773361615605], [172.84757055019364, -41.87428746635026], [172.8610931325304, -41.92246296617297], [172.73529884909934, -42.05789531630681], [172.721472999322, -42.103491799414414], [172.6391830501169, -42.10079468294802], [172.61471343352423, -42.19546264988527], [172.4790309498186, -42.27702943310177], [172.36959353256754, -42.280602516819684], [172.33587583323282, -42.234207966040074], [172.33195021658258, -42.1506392829357], [172.26099479981994, -42.14831248343561], 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-41.39314151628407, 173.5938914497683, -41.05111993303416], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[173.2974439833663, -41.39314151628407], [173.19888739972865, -41.3306196997791], [173.22177646601222, -41.29301190001072], [173.31061888268826, -41.25075821635732], [173.32361723312974, -41.20374501659446], [173.41277646745212, -41.149451216417354], [173.46163113359117, -41.16685331632796], [173.5938914497683, -41.05111993303416], [173.56836581649645, -41.1897309661648], [173.52300785071225, -41.197991866751444], [173.41541929965712, -41.28914169950753], [173.40678916666, -41.330958350178484], [173.2974439833663, -41.39314151628407]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;14&quot;, &quot;properties&quot;: {&quot;Island&quot;: &quot;South&quot;, &quot;Land_area&quot;: 422.195242, &quot;Median_income&quot;: 27200, &quot;Name&quot;: &quot;Nelson&quot;, &quot;Population&quot;: 51400.0, &quot;Sex_ratio&quot;: 0.9259674007714321}, &quot;type&quot;: 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27900, &quot;Name&quot;: &quot;Marlborough&quot;, &quot;Population&quot;: 46200.0, &quot;Sex_ratio&quot;: 0.9577922077922078}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_68215712b15148a8981540ca56b7751f.bindTooltip(
+    geo_json_860df3d2b2976457d1e4b7b3f414f2a3.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -1353,7 +1353,7 @@ <h3 data-number="8.3.1" class="anchored" data-anchor-id="minimal-example"><span
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_68215712b15148a8981540ca56b7751f.addTo(map_29956df017963fd59de131d279ed4aa6);
+            geo_json_860df3d2b2976457d1e4b7b3f414f2a3.addTo(map_0ea21f0060a2073f9a2661f62e960fd9);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -1411,7 +1411,7 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_d3c6a82c7491b93972b7e16a36c00bae {
+                #map_17b1b99e30ccb8ac758a5310d873deed {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -1441,14 +1441,14 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_d3c6a82c7491b93972b7e16a36c00bae&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_17b1b99e30ccb8ac758a5310d873deed&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_d3c6a82c7491b93972b7e16a36c00bae = L.map(
-                &quot;map_d3c6a82c7491b93972b7e16a36c00bae&quot;,
+            var map_17b1b99e30ccb8ac758a5310d873deed = L.map(
+                &quot;map_17b1b99e30ccb8ac758a5310d873deed&quot;,
                 {
                     center: [-40.84868556642058, 172.48833825792866],
                     crs: L.CRS.EPSG3857,
@@ -1457,79 +1457,79 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_d3c6a82c7491b93972b7e16a36c00bae);
+            L.control.scale().addTo(map_17b1b99e30ccb8ac758a5310d873deed);
 
             
 
         
     
-            var tile_layer_1a2517e3ef8818b759a90926f0b73467 = L.tileLayer(
+            var tile_layer_c2d10cf0aecadaac4400a2661c293dc4 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_1a2517e3ef8818b759a90926f0b73467.addTo(map_d3c6a82c7491b93972b7e16a36c00bae);
+            tile_layer_c2d10cf0aecadaac4400a2661c293dc4.addTo(map_17b1b99e30ccb8ac758a5310d873deed);
         
     
-            map_d3c6a82c7491b93972b7e16a36c00bae.fitBounds(
+            map_17b1b99e30ccb8ac758a5310d873deed.fitBounds(
                 [[-47.28285241600581, 166.42630291457417], [-34.414518716835346, 178.55037360128316]],
                 {}
             );
         
     
-        function geo_json_b5ebb004f1b3f278c3981fc08dace0e7_styler(feature) {
+        function geo_json_b17aee5a5ea85982ea17291cf6cd9b26_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;color&quot;: &quot;black&quot;, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.5, &quot;opacity&quot;: 0.3, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_b5ebb004f1b3f278c3981fc08dace0e7_highlighter(feature) {
+        function geo_json_b17aee5a5ea85982ea17291cf6cd9b26_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_b5ebb004f1b3f278c3981fc08dace0e7_pointToLayer(feature, latlng) {
+        function geo_json_b17aee5a5ea85982ea17291cf6cd9b26_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_b5ebb004f1b3f278c3981fc08dace0e7_styler(feature)
+            let style = geo_json_b17aee5a5ea85982ea17291cf6cd9b26_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_b5ebb004f1b3f278c3981fc08dace0e7_onEachFeature(feature, layer) {
+        function geo_json_b17aee5a5ea85982ea17291cf6cd9b26_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_b5ebb004f1b3f278c3981fc08dace0e7.resetStyle(e.target);
+                            geo_json_b17aee5a5ea85982ea17291cf6cd9b26.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_b5ebb004f1b3f278c3981fc08dace0e7_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_b17aee5a5ea85982ea17291cf6cd9b26_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_b5ebb004f1b3f278c3981fc08dace0e7 = L.geoJson(null, {
-                onEachFeature: geo_json_b5ebb004f1b3f278c3981fc08dace0e7_onEachFeature,
+        var geo_json_b17aee5a5ea85982ea17291cf6cd9b26 = L.geoJson(null, {
+                onEachFeature: geo_json_b17aee5a5ea85982ea17291cf6cd9b26_onEachFeature,
             
-                style: geo_json_b5ebb004f1b3f278c3981fc08dace0e7_styler,
-                pointToLayer: geo_json_b5ebb004f1b3f278c3981fc08dace0e7_pointToLayer,
+                style: geo_json_b17aee5a5ea85982ea17291cf6cd9b26_styler,
+                pointToLayer: geo_json_b17aee5a5ea85982ea17291cf6cd9b26_pointToLayer,
         });
 
-        function geo_json_b5ebb004f1b3f278c3981fc08dace0e7_add (data) {
-            geo_json_b5ebb004f1b3f278c3981fc08dace0e7
+        function geo_json_b17aee5a5ea85982ea17291cf6cd9b26_add (data) {
+            geo_json_b17aee5a5ea85982ea17291cf6cd9b26
                 .addData(data);
         }
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-    geo_json_b5ebb004f1b3f278c3981fc08dace0e7.bindTooltip(
+    geo_json_b17aee5a5ea85982ea17291cf6cd9b26.bindTooltip(
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     let div = L.DomUtil.create('div');
     
@@ -1553,7 +1553,7 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_b5ebb004f1b3f278c3981fc08dace0e7.addTo(map_d3c6a82c7491b93972b7e16a36c00bae);
+            geo_json_b17aee5a5ea85982ea17291cf6cd9b26.addTo(map_17b1b99e30ccb8ac758a5310d873deed);
         
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@@ -1612,7 +1612,7 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
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                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_781f9b276816607c7624d766aa8d37eb {
+                #map_f5dd8f0b2ba5aace95eea1e71e7b4a4c {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -1642,14 +1642,14 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_781f9b276816607c7624d766aa8d37eb&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_f5dd8f0b2ba5aace95eea1e71e7b4a4c&quot; ></div>
         
 </body>
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-            var map_781f9b276816607c7624d766aa8d37eb = L.map(
-                &quot;map_781f9b276816607c7624d766aa8d37eb&quot;,
+            var map_f5dd8f0b2ba5aace95eea1e71e7b4a4c = L.map(
+                &quot;map_f5dd8f0b2ba5aace95eea1e71e7b4a4c&quot;,
                 {
                     center: [-41.94602395683266, 171.79575214283176],
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@@ -1658,79 +1658,79 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_781f9b276816607c7624d766aa8d37eb);
+            L.control.scale().addTo(map_f5dd8f0b2ba5aace95eea1e71e7b4a4c);
 
             
 
         
     
-            var tile_layer_9d1550c655379006118ec4cbb02aaa32 = L.tileLayer(
+            var tile_layer_23e6e07306feac67c17c674c1133660c = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_9d1550c655379006118ec4cbb02aaa32.addTo(map_781f9b276816607c7624d766aa8d37eb);
+            tile_layer_23e6e07306feac67c17c674c1133660c.addTo(map_f5dd8f0b2ba5aace95eea1e71e7b4a4c);
         
     
-            map_781f9b276816607c7624d766aa8d37eb.fitBounds(
+            map_f5dd8f0b2ba5aace95eea1e71e7b4a4c.fitBounds(
                 [[-44.62567245229895, 168.0125423302844], [-39.266375461366366, 175.57896195537916]],
                 {}
             );
         
     
-        function geo_json_7f3856b7022db136d0e08d9d53aae1d0_styler(feature) {
+        function geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;color&quot;: &quot;black&quot;, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.1, &quot;opacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_7f3856b7022db136d0e08d9d53aae1d0_highlighter(feature) {
+        function geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_7f3856b7022db136d0e08d9d53aae1d0_pointToLayer(feature, latlng) {
+        function geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 20, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_7f3856b7022db136d0e08d9d53aae1d0_styler(feature)
+            let style = geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_7f3856b7022db136d0e08d9d53aae1d0_onEachFeature(feature, layer) {
+        function geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_7f3856b7022db136d0e08d9d53aae1d0.resetStyle(e.target);
+                            geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_7f3856b7022db136d0e08d9d53aae1d0_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_7f3856b7022db136d0e08d9d53aae1d0 = L.geoJson(null, {
-                onEachFeature: geo_json_7f3856b7022db136d0e08d9d53aae1d0_onEachFeature,
+        var geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed = L.geoJson(null, {
+                onEachFeature: geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_onEachFeature,
             
-                style: geo_json_7f3856b7022db136d0e08d9d53aae1d0_styler,
-                pointToLayer: geo_json_7f3856b7022db136d0e08d9d53aae1d0_pointToLayer,
+                style: geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_styler,
+                pointToLayer: geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_pointToLayer,
         });
 
-        function geo_json_7f3856b7022db136d0e08d9d53aae1d0_add (data) {
-            geo_json_7f3856b7022db136d0e08d9d53aae1d0
+        function geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed_add (data) {
+            geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed
                 .addData(data);
         }
-            geo_json_7f3856b7022db136d0e08d9d53aae1d0_add({&quot;bbox&quot;: [168.0125423302844, -44.62567245229895, 175.57896195537916, -39.266375461366366], &quot;features&quot;: [{&quot;bbox&quot;: [168.0125423302844, -44.59459664512115, 168.0125423302844, -44.59459664512115], &quot;geometry&quot;: {&quot;coordinates&quot;: [168.0125423302844, -44.59459664512115], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2723, &quot;t50_fid&quot;: 2353944}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [168.39538609597136, -44.62567245229895, 168.39538609597136, -44.62567245229895], &quot;geometry&quot;: {&quot;coordinates&quot;: [168.39538609597136, -44.62567245229895], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;1&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2820, &quot;t50_fid&quot;: 2354404}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [168.41063763477916, -44.6223643789721, 168.41063763477916, -44.6223643789721], &quot;geometry&quot;: {&quot;coordinates&quot;: [168.41063763477916, -44.6223643789721], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;2&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2830, &quot;t50_fid&quot;: 2354405}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [168.72793147663336, -44.38421078343852, 168.72793147663336, -44.38421078343852], &quot;geometry&quot;: {&quot;coordinates&quot;: [168.72793147663336, -44.38421078343852], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;3&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3033, &quot;t50_fid&quot;: 2369113}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.24732502712885, -43.693450738185284, 170.24732502712885, -43.693450738185284], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.24732502712885, -43.693450738185284], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;4&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2749, &quot;t50_fid&quot;: 2362630}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.3912789656041, -43.604473437299745, 170.3912789656041, -43.604473437299745], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.3912789656041, -43.604473437299745], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;5&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2822, &quot;t50_fid&quot;: 2362814}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.40029807208043, -43.59822274625254, 170.40029807208043, -43.59822274625254], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.40029807208043, -43.59822274625254], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;6&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2778, &quot;t50_fid&quot;: 2362817}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1812770763295, -43.56365353010908, 170.1812770763295, -43.56365353010908], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1812770763295, -43.56365353010908], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;7&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3004, &quot;t50_fid&quot;: 2363991}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.17784749821956, -43.55899912688209, 170.17784749821956, -43.55899912688209], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.17784749821956, -43.55899912688209], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;8&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3114, &quot;t50_fid&quot;: 2363993}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.18717001192118, -43.55758271588051, 170.18717001192118, -43.55758271588051], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.18717001192118, -43.55758271588051], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;9&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2882, &quot;t50_fid&quot;: 2363994}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1845518910588, -43.55238560960953, 170.1845518910588, -43.55238560960953], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1845518910588, -43.55238560960953], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;10&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2796, &quot;t50_fid&quot;: 2363995}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.19362256604583, -43.53952467006276, 170.19362256604583, -43.53952467006276], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.19362256604583, -43.53952467006276], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;11&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2759, &quot;t50_fid&quot;: 2363996}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.199667935054, -43.55136659831522, 170.199667935054, -43.55136659831522], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.199667935054, -43.55136659831522], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;12&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3070, &quot;t50_fid&quot;: 2363997}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.201668460829, -43.55062580185723, 170.201668460829, -43.55062580185723], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.201668460829, -43.55062580185723], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;13&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3061, &quot;t50_fid&quot;: 2363998}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.20244488780347, -43.54167022305371, 170.20244488780347, -43.54167022305371], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.20244488780347, -43.54167022305371], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;14&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3077, &quot;t50_fid&quot;: 2363999}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.20966347834732, -43.53818499591937, 170.20966347834732, -43.53818499591937], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.20966347834732, -43.53818499591937], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;15&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3002, &quot;t50_fid&quot;: 2364000}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.27471517656934, -43.588549422092946, 170.27471517656934, -43.588549422092946], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.27471517656934, -43.588549422092946], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;16&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2913, &quot;t50_fid&quot;: 2364007}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.27854611638443, -43.587306950856565, 170.27854611638443, -43.587306950856565], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.27854611638443, -43.587306950856565], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;17&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2966, &quot;t50_fid&quot;: 2364008}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.28455475965097, -43.58347649665493, 170.28455475965097, -43.58347649665493], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.28455475965097, -43.58347649665493], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;18&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2868, &quot;t50_fid&quot;: 2364009}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.29172861027484, -43.57751195126995, 170.29172861027484, -43.57751195126995], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.29172861027484, -43.57751195126995], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;19&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2950, &quot;t50_fid&quot;: 2364010}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.3048921429157, -43.5623835866028, 170.3048921429157, -43.5623835866028], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.3048921429157, -43.5623835866028], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;20&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3199, &quot;t50_fid&quot;: 2364015}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.30125158632094, -43.554371590026165, 170.30125158632094, -43.554371590026165], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.30125158632094, -43.554371590026165], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;21&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2706, &quot;t50_fid&quot;: 2364016}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.21416950988242, -43.53292110403136, 170.21416950988242, -43.53292110403136], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.21416950988242, -43.53292110403136], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;22&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2891, &quot;t50_fid&quot;: 2364026}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.214215005439, -43.53088773781502, 170.214215005439, -43.53088773781502], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.214215005439, -43.53088773781502], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;23&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2899, &quot;t50_fid&quot;: 2364027}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.2057040303538, -43.524312395799846, 170.2057040303538, -43.524312395799846], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.2057040303538, -43.524312395799846], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;24&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2756, &quot;t50_fid&quot;: 2364028}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.20916321383908, -43.52618563904869, 170.20916321383908, -43.52618563904869], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.20916321383908, -43.52618563904869], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;25&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2800, &quot;t50_fid&quot;: 2364029}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.22104956457014, -43.523895389235626, 170.22104956457014, -43.523895389235626], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.22104956457014, -43.523895389235626], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;26&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2788, &quot;t50_fid&quot;: 2364031}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.2651263750595, -43.51707242311264, 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&quot;id&quot;: &quot;77&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3300, &quot;t50_fid&quot;: 2372300}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.15721557796982, -43.56573126437556, 170.15721557796982, -43.56573126437556], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.15721557796982, -43.56573126437556], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;78&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3497, &quot;t50_fid&quot;: 2372301}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.14643498482974, -43.55972369771026, 170.14643498482974, -43.55972369771026], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.14643498482974, -43.55972369771026], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;79&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3160, &quot;t50_fid&quot;: 2372302}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.15788394709503, -43.57014212078643, 170.15788394709503, -43.57014212078643], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.15788394709503, -43.57014212078643], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;80&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3265, &quot;t50_fid&quot;: 2372330}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.16032174709548, -43.552165899949046, 170.16032174709548, -43.552165899949046], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.16032174709548, -43.552165899949046], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;81&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2801, &quot;t50_fid&quot;: 2372333}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1604595829108, -43.550030350183576, 170.1604595829108, -43.550030350183576], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1604595829108, -43.550030350183576], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;82&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2776, &quot;t50_fid&quot;: 2372334}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1765533891842, -43.55531964623479, 170.1765533891842, -43.55531964623479], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1765533891842, -43.55531964623479], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;83&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3065, &quot;t50_fid&quot;: 2372335}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1696407842666, -43.55982299763808, 170.1696407842666, -43.55982299763808], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1696407842666, -43.55982299763808], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;84&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3194, &quot;t50_fid&quot;: 2372343}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.17694519856323, -43.556962636611566, 170.17694519856323, -43.556962636611566], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.17694519856323, -43.556962636611566], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;85&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 3099, &quot;t50_fid&quot;: 2372344}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.55483910661331, -43.551846785558894, 170.55483910661331, -43.551846785558894], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.55483910661331, -43.551846785558894], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;86&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2811, &quot;t50_fid&quot;: 2364211}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.58167010841973, -43.46491478376197, 170.58167010841973, -43.46491478376197], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58167010841973, -43.46491478376197], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;87&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2875, &quot;t50_fid&quot;: 2364344}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.58189765034132, -43.4620037098722, 170.58189765034132, -43.4620037098722], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58189765034132, -43.4620037098722], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;88&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2775, &quot;t50_fid&quot;: 2364350}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.97824996121315, -43.356937057620435, 170.97824996121315, -43.356937057620435], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.97824996121315, -43.356937057620435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;89&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2781, &quot;t50_fid&quot;: 2365398}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.98299286636154, -43.357841131919166, 170.98299286636154, -43.357841131919166], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.98299286636154, -43.357841131919166], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;90&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2723, &quot;t50_fid&quot;: 2365400}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [171.0102444593256, -43.33334451894231, 171.0102444593256, -43.33334451894231], &quot;geometry&quot;: {&quot;coordinates&quot;: [171.0102444593256, -43.33334451894231], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;91&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2706, &quot;t50_fid&quot;: 2365454}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.63749447009457, -42.00939180892975, 173.63749447009457, -42.00939180892975], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.63749447009457, -42.00939180892975], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;92&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2877, &quot;t50_fid&quot;: 2380298}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.65461506464104, -42.000482455103715, 173.65461506464104, -42.000482455103715], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.65461506464104, -42.000482455103715], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;93&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2711, &quot;t50_fid&quot;: 2380300}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66284198969112, -41.99593369122435, 173.66284198969112, -41.99593369122435], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66284198969112, -41.99593369122435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;94&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2885, &quot;t50_fid&quot;: 2380308}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66178005313157, -41.985114434386276, 173.66178005313157, -41.985114434386276], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66178005313157, -41.985114434386276], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;95&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2380426}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.56267367384544, -39.28945301498999, 175.56267367384544, -39.28945301498999], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.56267367384544, -39.28945301498999], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;96&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2797, &quot;t50_fid&quot;: 2408394}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55825450658153, -39.28630222220387, 175.55825450658153, -39.28630222220387], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55825450658153, -39.28630222220387], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;97&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2757, &quot;t50_fid&quot;: 2408395}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55807189825757, -39.275882311319535, 175.55807189825757, -39.275882311319535], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55807189825757, -39.275882311319535], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;98&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2751, &quot;t50_fid&quot;: 2408397}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57632420640633, -39.267007290301024, 175.57632420640633, -39.267007290301024], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57632420640633, -39.267007290301024], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;99&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2408406}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57896195537916, -39.266375461366366, 175.57896195537916, -39.266375461366366], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57896195537916, -39.266375461366366], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;100&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;green&quot;, &quot;elevation&quot;: 2732, &quot;t50_fid&quot;: 2408411}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_7f3856b7022db136d0e08d9d53aae1d0.bindTooltip(
+    geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -1754,7 +1754,7 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_7f3856b7022db136d0e08d9d53aae1d0.addTo(map_781f9b276816607c7624d766aa8d37eb);
+            geo_json_03ee2e65f8e9ef1dd4c6ed473da94aed.addTo(map_f5dd8f0b2ba5aace95eea1e71e7b4a4c);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -1798,7 +1798,7 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_6a00afd8ddfdd220a42312eee96c71fe {
+                #map_ef5318a49cf9354a4eeef14ffa4af703 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -1828,14 +1828,14 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_6a00afd8ddfdd220a42312eee96c71fe&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_ef5318a49cf9354a4eeef14ffa4af703&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_6a00afd8ddfdd220a42312eee96c71fe = L.map(
-                &quot;map_6a00afd8ddfdd220a42312eee96c71fe&quot;,
+            var map_ef5318a49cf9354a4eeef14ffa4af703 = L.map(
+                &quot;map_ef5318a49cf9354a4eeef14ffa4af703&quot;,
                 {
                     center: [-41.94602395683266, 171.79575214283176],
                     crs: L.CRS.EPSG3857,
@@ -1844,79 +1844,79 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_6a00afd8ddfdd220a42312eee96c71fe);
+            L.control.scale().addTo(map_ef5318a49cf9354a4eeef14ffa4af703);
 
             
 
         
     
-            var tile_layer_322f2030bf8ff42a217322d4337f5354 = L.tileLayer(
+            var tile_layer_6c191a31cab718eef828047edc7418d0 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_322f2030bf8ff42a217322d4337f5354.addTo(map_6a00afd8ddfdd220a42312eee96c71fe);
+            tile_layer_6c191a31cab718eef828047edc7418d0.addTo(map_ef5318a49cf9354a4eeef14ffa4af703);
         
     
-            map_6a00afd8ddfdd220a42312eee96c71fe.fitBounds(
+            map_ef5318a49cf9354a4eeef14ffa4af703.fitBounds(
                 [[-44.62567245229895, 168.0125423302844], [-39.266375461366366, 175.57896195537916]],
                 {}
             );
         
     
-        function geo_json_e9dd04d1dce1c5984c00dbd93142972b_styler(feature) {
+        function geo_json_dcf2d19d1a3da4e6657ab1204d31465c_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_e9dd04d1dce1c5984c00dbd93142972b_highlighter(feature) {
+        function geo_json_dcf2d19d1a3da4e6657ab1204d31465c_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_e9dd04d1dce1c5984c00dbd93142972b_pointToLayer(feature, latlng) {
+        function geo_json_dcf2d19d1a3da4e6657ab1204d31465c_pointToLayer(feature, latlng) {
             var opts = {&quot;fill&quot;: true, &quot;radius&quot;: 2};
             
-            let style = geo_json_e9dd04d1dce1c5984c00dbd93142972b_styler(feature)
+            let style = geo_json_dcf2d19d1a3da4e6657ab1204d31465c_styler(feature)
             Object.assign(opts, style)
             
             return new L.Marker(latlng, opts)
         }
 
-        function geo_json_e9dd04d1dce1c5984c00dbd93142972b_onEachFeature(feature, layer) {
+        function geo_json_dcf2d19d1a3da4e6657ab1204d31465c_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_e9dd04d1dce1c5984c00dbd93142972b.resetStyle(e.target);
+                            geo_json_dcf2d19d1a3da4e6657ab1204d31465c.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_e9dd04d1dce1c5984c00dbd93142972b_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_dcf2d19d1a3da4e6657ab1204d31465c_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_e9dd04d1dce1c5984c00dbd93142972b = L.geoJson(null, {
-                onEachFeature: geo_json_e9dd04d1dce1c5984c00dbd93142972b_onEachFeature,
+        var geo_json_dcf2d19d1a3da4e6657ab1204d31465c = L.geoJson(null, {
+                onEachFeature: geo_json_dcf2d19d1a3da4e6657ab1204d31465c_onEachFeature,
             
-                style: geo_json_e9dd04d1dce1c5984c00dbd93142972b_styler,
-                pointToLayer: geo_json_e9dd04d1dce1c5984c00dbd93142972b_pointToLayer,
+                style: geo_json_dcf2d19d1a3da4e6657ab1204d31465c_styler,
+                pointToLayer: geo_json_dcf2d19d1a3da4e6657ab1204d31465c_pointToLayer,
         });
 
-        function geo_json_e9dd04d1dce1c5984c00dbd93142972b_add (data) {
-            geo_json_e9dd04d1dce1c5984c00dbd93142972b
+        function geo_json_dcf2d19d1a3da4e6657ab1204d31465c_add (data) {
+            geo_json_dcf2d19d1a3da4e6657ab1204d31465c
                 .addData(data);
         }
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&quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.12910086934534, -43.58762670940774, 170.12910086934534, -43.58762670940774], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.12910086934534, -43.58762670940774], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;69&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3198, &quot;t50_fid&quot;: 2372292}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1418223746271, -43.58508797673217, 170.1418223746271, -43.58508797673217], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1418223746271, -43.58508797673217], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;70&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3309, &quot;t50_fid&quot;: 2372293}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1452623276185, -43.57828870046518, 170.1452623276185, -43.57828870046518], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1452623276185, -43.57828870046518], 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&quot;properties&quot;: {&quot;elevation&quot;: 3170, &quot;t50_fid&quot;: 2372299}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.15769755406035, -43.57169997333237, 170.15769755406035, -43.57169997333237], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.15769755406035, -43.57169997333237], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;77&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3300, &quot;t50_fid&quot;: 2372300}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.15721557796982, -43.56573126437556, 170.15721557796982, -43.56573126437556], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.15721557796982, -43.56573126437556], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;78&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3497, &quot;t50_fid&quot;: 2372301}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.14643498482974, -43.55972369771026, 170.14643498482974, -43.55972369771026], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.14643498482974, -43.55972369771026], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;79&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3160, &quot;t50_fid&quot;: 2372302}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.15788394709503, -43.57014212078643, 170.15788394709503, -43.57014212078643], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.15788394709503, -43.57014212078643], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;80&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3265, &quot;t50_fid&quot;: 2372330}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.16032174709548, -43.552165899949046, 170.16032174709548, -43.552165899949046], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.16032174709548, -43.552165899949046], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;81&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2801, &quot;t50_fid&quot;: 2372333}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1604595829108, -43.550030350183576, 170.1604595829108, -43.550030350183576], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1604595829108, -43.550030350183576], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;82&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2776, &quot;t50_fid&quot;: 2372334}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1765533891842, -43.55531964623479, 170.1765533891842, -43.55531964623479], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1765533891842, -43.55531964623479], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;83&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3065, &quot;t50_fid&quot;: 2372335}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1696407842666, -43.55982299763808, 170.1696407842666, -43.55982299763808], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1696407842666, -43.55982299763808], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;84&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3194, &quot;t50_fid&quot;: 2372343}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.17694519856323, -43.556962636611566, 170.17694519856323, -43.556962636611566], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.17694519856323, -43.556962636611566], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;85&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 3099, &quot;t50_fid&quot;: 2372344}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.55483910661331, -43.551846785558894, 170.55483910661331, -43.551846785558894], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.55483910661331, -43.551846785558894], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;86&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2811, &quot;t50_fid&quot;: 2364211}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.58167010841973, -43.46491478376197, 170.58167010841973, -43.46491478376197], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58167010841973, -43.46491478376197], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;87&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2875, &quot;t50_fid&quot;: 2364344}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.58189765034132, -43.4620037098722, 170.58189765034132, -43.4620037098722], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58189765034132, -43.4620037098722], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;88&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2775, &quot;t50_fid&quot;: 2364350}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.97824996121315, -43.356937057620435, 170.97824996121315, -43.356937057620435], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.97824996121315, -43.356937057620435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;89&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2781, &quot;t50_fid&quot;: 2365398}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.98299286636154, -43.357841131919166, 170.98299286636154, -43.357841131919166], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.98299286636154, -43.357841131919166], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;90&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2723, &quot;t50_fid&quot;: 2365400}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [171.0102444593256, -43.33334451894231, 171.0102444593256, -43.33334451894231], &quot;geometry&quot;: {&quot;coordinates&quot;: [171.0102444593256, -43.33334451894231], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;91&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2706, &quot;t50_fid&quot;: 2365454}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.63749447009457, -42.00939180892975, 173.63749447009457, -42.00939180892975], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.63749447009457, -42.00939180892975], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;92&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2877, &quot;t50_fid&quot;: 2380298}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.65461506464104, -42.000482455103715, 173.65461506464104, -42.000482455103715], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.65461506464104, -42.000482455103715], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;93&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2711, &quot;t50_fid&quot;: 2380300}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66284198969112, -41.99593369122435, 173.66284198969112, -41.99593369122435], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66284198969112, -41.99593369122435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;94&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2885, &quot;t50_fid&quot;: 2380308}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66178005313157, -41.985114434386276, 173.66178005313157, -41.985114434386276], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66178005313157, -41.985114434386276], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;95&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2380426}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.56267367384544, -39.28945301498999, 175.56267367384544, -39.28945301498999], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.56267367384544, -39.28945301498999], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;96&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2797, &quot;t50_fid&quot;: 2408394}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55825450658153, -39.28630222220387, 175.55825450658153, -39.28630222220387], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55825450658153, -39.28630222220387], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;97&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2757, &quot;t50_fid&quot;: 2408395}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55807189825757, -39.275882311319535, 175.55807189825757, -39.275882311319535], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55807189825757, -39.275882311319535], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;98&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2751, &quot;t50_fid&quot;: 2408397}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57632420640633, -39.267007290301024, 175.57632420640633, -39.267007290301024], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57632420640633, -39.267007290301024], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;99&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2408406}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57896195537916, -39.266375461366366, 175.57896195537916, -39.266375461366366], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57896195537916, -39.266375461366366], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;100&quot;, &quot;properties&quot;: {&quot;elevation&quot;: 2732, &quot;t50_fid&quot;: 2408411}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_e9dd04d1dce1c5984c00dbd93142972b.bindTooltip(
+    geo_json_dcf2d19d1a3da4e6657ab1204d31465c.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -1940,7 +1940,7 @@ <h3 data-number="8.3.2" class="anchored" data-anchor-id="sec-interactive-styling
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_e9dd04d1dce1c5984c00dbd93142972b.addTo(map_6a00afd8ddfdd220a42312eee96c71fe);
+            geo_json_dcf2d19d1a3da4e6657ab1204d31465c.addTo(map_ef5318a49cf9354a4eeef14ffa4af703);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -1988,7 +1988,7 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_c7afc429678908028b0f05b3a6a8aa7c {
+                #map_66bf7eee7f6cacf80e719f177d10bd5f {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -2034,14 +2034,14 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_c7afc429678908028b0f05b3a6a8aa7c&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_66bf7eee7f6cacf80e719f177d10bd5f&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_c7afc429678908028b0f05b3a6a8aa7c = L.map(
-                &quot;map_c7afc429678908028b0f05b3a6a8aa7c&quot;,
+            var map_66bf7eee7f6cacf80e719f177d10bd5f = L.map(
+                &quot;map_66bf7eee7f6cacf80e719f177d10bd5f&quot;,
                 {
                     center: [-40.84868556642058, 172.48833825792866],
                     crs: L.CRS.EPSG3857,
@@ -2050,79 +2050,79 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_c7afc429678908028b0f05b3a6a8aa7c);
+            L.control.scale().addTo(map_66bf7eee7f6cacf80e719f177d10bd5f);
 
             
 
         
     
-            var tile_layer_56ee61c3954e1686de11fadc3e30deb7 = L.tileLayer(
+            var tile_layer_665da05cfde561654dfac1abc0398d03 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_56ee61c3954e1686de11fadc3e30deb7.addTo(map_c7afc429678908028b0f05b3a6a8aa7c);
+            tile_layer_665da05cfde561654dfac1abc0398d03.addTo(map_66bf7eee7f6cacf80e719f177d10bd5f);
         
     
-            map_c7afc429678908028b0f05b3a6a8aa7c.fitBounds(
+            map_66bf7eee7f6cacf80e719f177d10bd5f.fitBounds(
                 [[-47.28285241600581, 166.42630291457417], [-34.414518716835346, 178.55037360128316]],
                 {}
             );
         
     
-        function geo_json_9ab3f0767051ae6b307d4a2ae8347e15_styler(feature) {
+        function geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_9ab3f0767051ae6b307d4a2ae8347e15_highlighter(feature) {
+        function geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_9ab3f0767051ae6b307d4a2ae8347e15_pointToLayer(feature, latlng) {
+        function geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_9ab3f0767051ae6b307d4a2ae8347e15_styler(feature)
+            let style = geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_9ab3f0767051ae6b307d4a2ae8347e15_onEachFeature(feature, layer) {
+        function geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_9ab3f0767051ae6b307d4a2ae8347e15.resetStyle(e.target);
+                            geo_json_cc341fbb3f7d9d6ee15f22fed71487cf.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_9ab3f0767051ae6b307d4a2ae8347e15_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_9ab3f0767051ae6b307d4a2ae8347e15 = L.geoJson(null, {
-                onEachFeature: geo_json_9ab3f0767051ae6b307d4a2ae8347e15_onEachFeature,
+        var geo_json_cc341fbb3f7d9d6ee15f22fed71487cf = L.geoJson(null, {
+                onEachFeature: geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_onEachFeature,
             
-                style: geo_json_9ab3f0767051ae6b307d4a2ae8347e15_styler,
-                pointToLayer: geo_json_9ab3f0767051ae6b307d4a2ae8347e15_pointToLayer,
+                style: geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_styler,
+                pointToLayer: geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_pointToLayer,
         });
 
-        function geo_json_9ab3f0767051ae6b307d4a2ae8347e15_add (data) {
-            geo_json_9ab3f0767051ae6b307d4a2ae8347e15
+        function geo_json_cc341fbb3f7d9d6ee15f22fed71487cf_add (data) {
+            geo_json_cc341fbb3f7d9d6ee15f22fed71487cf
                 .addData(data);
         }
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27900, &quot;Name&quot;: &quot;Marlborough&quot;, &quot;Population&quot;: 46200.0, &quot;Sex_ratio&quot;: 0.9577922077922078}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_9ab3f0767051ae6b307d4a2ae8347e15.bindTooltip(
+    geo_json_cc341fbb3f7d9d6ee15f22fed71487cf.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -2146,61 +2146,61 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_9ab3f0767051ae6b307d4a2ae8347e15.addTo(map_c7afc429678908028b0f05b3a6a8aa7c);
+            geo_json_cc341fbb3f7d9d6ee15f22fed71487cf.addTo(map_66bf7eee7f6cacf80e719f177d10bd5f);
         
     
-        function geo_json_d2282fffa94c1e43e0699705a0a7d364_styler(feature) {
+        function geo_json_c896f68e8c0acb776f3d1daa7891aa5f_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;color&quot;: &quot;red&quot;, &quot;fillColor&quot;: &quot;red&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_d2282fffa94c1e43e0699705a0a7d364_highlighter(feature) {
+        function geo_json_c896f68e8c0acb776f3d1daa7891aa5f_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_d2282fffa94c1e43e0699705a0a7d364_pointToLayer(feature, latlng) {
+        function geo_json_c896f68e8c0acb776f3d1daa7891aa5f_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_d2282fffa94c1e43e0699705a0a7d364_styler(feature)
+            let style = geo_json_c896f68e8c0acb776f3d1daa7891aa5f_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_d2282fffa94c1e43e0699705a0a7d364_onEachFeature(feature, layer) {
+        function geo_json_c896f68e8c0acb776f3d1daa7891aa5f_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_d2282fffa94c1e43e0699705a0a7d364.resetStyle(e.target);
+                            geo_json_c896f68e8c0acb776f3d1daa7891aa5f.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_d2282fffa94c1e43e0699705a0a7d364_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_c896f68e8c0acb776f3d1daa7891aa5f_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_d2282fffa94c1e43e0699705a0a7d364 = L.geoJson(null, {
-                onEachFeature: geo_json_d2282fffa94c1e43e0699705a0a7d364_onEachFeature,
+        var geo_json_c896f68e8c0acb776f3d1daa7891aa5f = L.geoJson(null, {
+                onEachFeature: geo_json_c896f68e8c0acb776f3d1daa7891aa5f_onEachFeature,
             
-                style: geo_json_d2282fffa94c1e43e0699705a0a7d364_styler,
-                pointToLayer: geo_json_d2282fffa94c1e43e0699705a0a7d364_pointToLayer,
+                style: geo_json_c896f68e8c0acb776f3d1daa7891aa5f_styler,
+                pointToLayer: geo_json_c896f68e8c0acb776f3d1daa7891aa5f_pointToLayer,
         });
 
-        function geo_json_d2282fffa94c1e43e0699705a0a7d364_add (data) {
-            geo_json_d2282fffa94c1e43e0699705a0a7d364
+        function geo_json_c896f68e8c0acb776f3d1daa7891aa5f_add (data) {
+            geo_json_c896f68e8c0acb776f3d1daa7891aa5f
                 .addData(data);
         }
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&quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [168.41063763477916, -44.6223643789721, 168.41063763477916, -44.6223643789721], &quot;geometry&quot;: {&quot;coordinates&quot;: [168.41063763477916, -44.6223643789721], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;2&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2830, &quot;t50_fid&quot;: 2354405}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [168.72793147663336, -44.38421078343852, 168.72793147663336, -44.38421078343852], &quot;geometry&quot;: {&quot;coordinates&quot;: [168.72793147663336, -44.38421078343852], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;3&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 3033, &quot;t50_fid&quot;: 2369113}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.24732502712885, -43.693450738185284, 170.24732502712885, -43.693450738185284], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.24732502712885, -43.693450738185284], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;4&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2749, &quot;t50_fid&quot;: 2362630}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.3912789656041, -43.604473437299745, 170.3912789656041, -43.604473437299745], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.3912789656041, -43.604473437299745], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;5&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2822, &quot;t50_fid&quot;: 2362814}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.40029807208043, -43.59822274625254, 170.40029807208043, -43.59822274625254], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.40029807208043, -43.59822274625254], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;6&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2778, &quot;t50_fid&quot;: 2362817}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1812770763295, -43.56365353010908, 170.1812770763295, -43.56365353010908], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1812770763295, -43.56365353010908], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;7&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 3004, &quot;t50_fid&quot;: 2363991}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.17784749821956, -43.55899912688209, 170.17784749821956, -43.55899912688209], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.17784749821956, -43.55899912688209], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;8&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 3114, &quot;t50_fid&quot;: 2363993}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.18717001192118, -43.55758271588051, 170.18717001192118, -43.55758271588051], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.18717001192118, -43.55758271588051], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;9&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2882, &quot;t50_fid&quot;: 2363994}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.1845518910588, -43.55238560960953, 170.1845518910588, -43.55238560960953], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.1845518910588, -43.55238560960953], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;10&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2796, &quot;t50_fid&quot;: 2363995}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.19362256604583, -43.53952467006276, 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&quot;Point&quot;}, &quot;id&quot;: &quot;13&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 3061, &quot;t50_fid&quot;: 2363998}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.20244488780347, -43.54167022305371, 170.20244488780347, -43.54167022305371], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.20244488780347, -43.54167022305371], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;14&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 3077, &quot;t50_fid&quot;: 2363999}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.20966347834732, -43.53818499591937, 170.20966347834732, -43.53818499591937], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.20966347834732, -43.53818499591937], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;15&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 3002, &quot;t50_fid&quot;: 2364000}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.27471517656934, -43.588549422092946, 170.27471517656934, -43.588549422092946], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.27471517656934, -43.588549422092946], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;16&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2913, &quot;t50_fid&quot;: 2364007}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.27854611638443, -43.587306950856565, 170.27854611638443, -43.587306950856565], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.27854611638443, -43.587306950856565], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;17&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2966, &quot;t50_fid&quot;: 2364008}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: 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[170.3048921429157, -43.5623835866028], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;20&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 3199, &quot;t50_fid&quot;: 2364015}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.30125158632094, -43.554371590026165, 170.30125158632094, -43.554371590026165], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.30125158632094, -43.554371590026165], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;21&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2706, &quot;t50_fid&quot;: 2364016}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.21416950988242, -43.53292110403136, 170.21416950988242, -43.53292110403136], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.21416950988242, -43.53292110403136], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;22&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2891, &quot;t50_fid&quot;: 2364026}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.214215005439, -43.53088773781502, 170.214215005439, -43.53088773781502], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.214215005439, -43.53088773781502], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;23&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2899, &quot;t50_fid&quot;: 2364027}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.2057040303538, -43.524312395799846, 170.2057040303538, -43.524312395799846], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.2057040303538, -43.524312395799846], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;24&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2756, &quot;t50_fid&quot;: 2364028}, &quot;type&quot;: 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170.28974442462942, -43.50361483393035], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.28974442462942, -43.50361483393035], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;34&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2716, &quot;t50_fid&quot;: 2364061}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.31177327695895, -43.49500566248833, 170.31177327695895, -43.49500566248833], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.31177327695895, -43.49500566248833], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;35&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2837, &quot;t50_fid&quot;: 2364063}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.32931591951927, -43.55457095590802, 170.32931591951927, -43.55457095590802], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.32931591951927, -43.55457095590802], 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-43.46491478376197, 170.58167010841973, -43.46491478376197], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58167010841973, -43.46491478376197], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;87&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2875, &quot;t50_fid&quot;: 2364344}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.58189765034132, -43.4620037098722, 170.58189765034132, -43.4620037098722], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58189765034132, -43.4620037098722], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;88&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2775, &quot;t50_fid&quot;: 2364350}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.97824996121315, -43.356937057620435, 170.97824996121315, -43.356937057620435], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.97824996121315, -43.356937057620435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;89&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2781, &quot;t50_fid&quot;: 2365398}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.98299286636154, -43.357841131919166, 170.98299286636154, -43.357841131919166], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.98299286636154, -43.357841131919166], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;90&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2723, &quot;t50_fid&quot;: 2365400}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [171.0102444593256, -43.33334451894231, 171.0102444593256, -43.33334451894231], &quot;geometry&quot;: {&quot;coordinates&quot;: [171.0102444593256, -43.33334451894231], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;91&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2706, &quot;t50_fid&quot;: 2365454}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.63749447009457, -42.00939180892975, 173.63749447009457, -42.00939180892975], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.63749447009457, -42.00939180892975], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;92&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2877, &quot;t50_fid&quot;: 2380298}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.65461506464104, -42.000482455103715, 173.65461506464104, -42.000482455103715], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.65461506464104, -42.000482455103715], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;93&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2711, &quot;t50_fid&quot;: 2380300}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66284198969112, -41.99593369122435, 173.66284198969112, -41.99593369122435], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66284198969112, -41.99593369122435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;94&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2885, &quot;t50_fid&quot;: 2380308}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66178005313157, -41.985114434386276, 173.66178005313157, -41.985114434386276], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66178005313157, -41.985114434386276], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;95&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2380426}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.56267367384544, -39.28945301498999, 175.56267367384544, -39.28945301498999], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.56267367384544, -39.28945301498999], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;96&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2797, &quot;t50_fid&quot;: 2408394}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55825450658153, -39.28630222220387, 175.55825450658153, -39.28630222220387], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55825450658153, -39.28630222220387], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;97&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2757, &quot;t50_fid&quot;: 2408395}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55807189825757, -39.275882311319535, 175.55807189825757, -39.275882311319535], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55807189825757, -39.275882311319535], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;98&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2751, &quot;t50_fid&quot;: 2408397}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57632420640633, -39.267007290301024, 175.57632420640633, -39.267007290301024], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57632420640633, -39.267007290301024], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;99&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2408406}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57896195537916, -39.266375461366366, 175.57896195537916, -39.266375461366366], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57896195537916, -39.266375461366366], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;100&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2732, &quot;t50_fid&quot;: 2408411}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_d2282fffa94c1e43e0699705a0a7d364.bindTooltip(
+    geo_json_c896f68e8c0acb776f3d1daa7891aa5f.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -2224,7 +2224,7 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_d2282fffa94c1e43e0699705a0a7d364.addTo(map_c7afc429678908028b0f05b3a6a8aa7c);
+            geo_json_c896f68e8c0acb776f3d1daa7891aa5f.addTo(map_66bf7eee7f6cacf80e719f177d10bd5f);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -2271,7 +2271,7 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_7d3a8db677bdec60139ef1276988901e {
+                #map_b71f301a24f9bec9c013f8d6df723872 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -2317,14 +2317,14 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_7d3a8db677bdec60139ef1276988901e&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_b71f301a24f9bec9c013f8d6df723872&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_7d3a8db677bdec60139ef1276988901e = L.map(
-                &quot;map_7d3a8db677bdec60139ef1276988901e&quot;,
+            var map_b71f301a24f9bec9c013f8d6df723872 = L.map(
+                &quot;map_b71f301a24f9bec9c013f8d6df723872&quot;,
                 {
                     center: [-40.84868556642058, 172.48833825792866],
                     crs: L.CRS.EPSG3857,
@@ -2333,79 +2333,79 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_7d3a8db677bdec60139ef1276988901e);
+            L.control.scale().addTo(map_b71f301a24f9bec9c013f8d6df723872);
 
             
 
         
     
-            var tile_layer_4be586ba919b5128dd221edfb6ad06db = L.tileLayer(
+            var tile_layer_1ce8ce3df5c36758f1beb25d188971ea = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_4be586ba919b5128dd221edfb6ad06db.addTo(map_7d3a8db677bdec60139ef1276988901e);
+            tile_layer_1ce8ce3df5c36758f1beb25d188971ea.addTo(map_b71f301a24f9bec9c013f8d6df723872);
         
     
-            map_7d3a8db677bdec60139ef1276988901e.fitBounds(
+            map_b71f301a24f9bec9c013f8d6df723872.fitBounds(
                 [[-47.28285241600581, 166.42630291457417], [-34.414518716835346, 178.55037360128316]],
                 {}
             );
         
     
-        function geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_styler(feature) {
+        function geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_highlighter(feature) {
+        function geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_pointToLayer(feature, latlng) {
+        function geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_styler(feature)
+            let style = geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_onEachFeature(feature, layer) {
+        function geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c.resetStyle(e.target);
+                            geo_json_ff89e9a0a4b431c695d55eb39fc0fc07.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c = L.geoJson(null, {
-                onEachFeature: geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_onEachFeature,
+        var geo_json_ff89e9a0a4b431c695d55eb39fc0fc07 = L.geoJson(null, {
+                onEachFeature: geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_onEachFeature,
             
-                style: geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_styler,
-                pointToLayer: geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_pointToLayer,
+                style: geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_styler,
+                pointToLayer: geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_pointToLayer,
         });
 
-        function geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_add (data) {
-            geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c
+        function geo_json_ff89e9a0a4b431c695d55eb39fc0fc07_add (data) {
+            geo_json_ff89e9a0a4b431c695d55eb39fc0fc07
                 .addData(data);
         }
-            geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c_add({&quot;bbox&quot;: [166.42630291457417, -47.28285241600581, 178.55037360128316, -34.414518716835346], &quot;features&quot;: [{&quot;bbox&quot;: [172.653880649817, -36.39288306661124, 174.61663880065558, -34.414518716835346], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[174.61663880065558, -36.117560466595535], [174.5628090505642, -36.179000182759175], [174.48174833315713, -36.22827699994316], [174.33896590023375, -36.316829716883234], [174.21457326690228, -36.25871166608587], [174.12877275047865, -36.17361239945035], [174.04599758389062, -36.17016955012603], [174.09597213385467, -36.24488793346581], [174.1553684830745, -36.27609723272722], [174.18186668339544, -36.36303589981312], [174.13535050025587, -36.39204991691119], [174.04052755054076, -36.39288306661124], [173.9836758838081, -36.233704999678864], [173.8660469165703, -36.09120011624559], [173.68809813355304, -35.89241744971263], [173.3651805831832, 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{&quot;bbox&quot;: [172.05212681699254, -42.280602516819684, 173.30414935048913, -40.50811923348418], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[173.19888739972865, -41.3306196997791], [173.2974439833663, -41.39314151628407], [173.25083138412685, -41.46658011655971], [173.30414935048913, -41.50603208360206], [173.16870053359213, -41.53115469953437], [173.15161466698348, -41.585470983182375], [173.0866526169933, -41.6249252333708], [173.00378965079662, -41.72480326694655], [172.92623520065564, -41.75773361615605], [172.84757055019364, -41.87428746635026], [172.8610931325304, -41.92246296617297], [172.73529884909934, -42.05789531630681], [172.721472999322, -42.103491799414414], [172.6391830501169, -42.10079468294802], [172.61471343352423, -42.19546264988527], [172.4790309498186, -42.27702943310177], [172.36959353256754, -42.280602516819684], [172.33587583323282, -42.234207966040074], [172.33195021658258, -42.1506392829357], [172.26099479981994, -42.14831248343561], 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-41.39314151628407, 173.5938914497683, -41.05111993303416], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[173.2974439833663, -41.39314151628407], [173.19888739972865, -41.3306196997791], [173.22177646601222, -41.29301190001072], [173.31061888268826, -41.25075821635732], [173.32361723312974, -41.20374501659446], [173.41277646745212, -41.149451216417354], [173.46163113359117, -41.16685331632796], [173.5938914497683, -41.05111993303416], [173.56836581649645, -41.1897309661648], [173.52300785071225, -41.197991866751444], [173.41541929965712, -41.28914169950753], [173.40678916666, -41.330958350178484], [173.2974439833663, -41.39314151628407]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;14&quot;, &quot;properties&quot;: {&quot;Island&quot;: &quot;South&quot;, &quot;Land_area&quot;: 422.195242, &quot;Median_income&quot;: 27200, &quot;Name&quot;: &quot;Nelson&quot;, &quot;Population&quot;: 51400.0, &quot;Sex_ratio&quot;: 0.9259674007714321}, &quot;type&quot;: 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27900, &quot;Name&quot;: &quot;Marlborough&quot;, &quot;Population&quot;: 46200.0, &quot;Sex_ratio&quot;: 0.9577922077922078}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c.bindTooltip(
+    geo_json_ff89e9a0a4b431c695d55eb39fc0fc07.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -2429,61 +2429,61 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c.addTo(map_7d3a8db677bdec60139ef1276988901e);
+            geo_json_ff89e9a0a4b431c695d55eb39fc0fc07.addTo(map_b71f301a24f9bec9c013f8d6df723872);
         
     
-        function geo_json_54d91bed36b1a6973491a4f6e844eb77_styler(feature) {
+        function geo_json_4c820a70e0abf84fc9394b59552981ee_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;color&quot;: &quot;red&quot;, &quot;fillColor&quot;: &quot;red&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_54d91bed36b1a6973491a4f6e844eb77_highlighter(feature) {
+        function geo_json_4c820a70e0abf84fc9394b59552981ee_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_54d91bed36b1a6973491a4f6e844eb77_pointToLayer(feature, latlng) {
+        function geo_json_4c820a70e0abf84fc9394b59552981ee_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_54d91bed36b1a6973491a4f6e844eb77_styler(feature)
+            let style = geo_json_4c820a70e0abf84fc9394b59552981ee_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_54d91bed36b1a6973491a4f6e844eb77_onEachFeature(feature, layer) {
+        function geo_json_4c820a70e0abf84fc9394b59552981ee_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_54d91bed36b1a6973491a4f6e844eb77.resetStyle(e.target);
+                            geo_json_4c820a70e0abf84fc9394b59552981ee.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_54d91bed36b1a6973491a4f6e844eb77_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_4c820a70e0abf84fc9394b59552981ee_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_54d91bed36b1a6973491a4f6e844eb77 = L.geoJson(null, {
-                onEachFeature: geo_json_54d91bed36b1a6973491a4f6e844eb77_onEachFeature,
+        var geo_json_4c820a70e0abf84fc9394b59552981ee = L.geoJson(null, {
+                onEachFeature: geo_json_4c820a70e0abf84fc9394b59552981ee_onEachFeature,
             
-                style: geo_json_54d91bed36b1a6973491a4f6e844eb77_styler,
-                pointToLayer: geo_json_54d91bed36b1a6973491a4f6e844eb77_pointToLayer,
+                style: geo_json_4c820a70e0abf84fc9394b59552981ee_styler,
+                pointToLayer: geo_json_4c820a70e0abf84fc9394b59552981ee_pointToLayer,
         });
 
-        function geo_json_54d91bed36b1a6973491a4f6e844eb77_add (data) {
-            geo_json_54d91bed36b1a6973491a4f6e844eb77
+        function geo_json_4c820a70e0abf84fc9394b59552981ee_add (data) {
+            geo_json_4c820a70e0abf84fc9394b59552981ee
                 .addData(data);
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-43.46491478376197, 170.58167010841973, -43.46491478376197], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58167010841973, -43.46491478376197], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;87&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2875, &quot;t50_fid&quot;: 2364344}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.58189765034132, -43.4620037098722, 170.58189765034132, -43.4620037098722], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.58189765034132, -43.4620037098722], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;88&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2775, &quot;t50_fid&quot;: 2364350}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.97824996121315, -43.356937057620435, 170.97824996121315, -43.356937057620435], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.97824996121315, -43.356937057620435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;89&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2781, &quot;t50_fid&quot;: 2365398}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [170.98299286636154, -43.357841131919166, 170.98299286636154, -43.357841131919166], &quot;geometry&quot;: {&quot;coordinates&quot;: [170.98299286636154, -43.357841131919166], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;90&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2723, &quot;t50_fid&quot;: 2365400}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [171.0102444593256, -43.33334451894231, 171.0102444593256, -43.33334451894231], &quot;geometry&quot;: {&quot;coordinates&quot;: [171.0102444593256, -43.33334451894231], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;91&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2706, &quot;t50_fid&quot;: 2365454}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.63749447009457, -42.00939180892975, 173.63749447009457, -42.00939180892975], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.63749447009457, -42.00939180892975], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;92&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2877, &quot;t50_fid&quot;: 2380298}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.65461506464104, -42.000482455103715, 173.65461506464104, -42.000482455103715], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.65461506464104, -42.000482455103715], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;93&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2711, &quot;t50_fid&quot;: 2380300}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66284198969112, -41.99593369122435, 173.66284198969112, -41.99593369122435], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66284198969112, -41.99593369122435], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;94&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2885, &quot;t50_fid&quot;: 2380308}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [173.66178005313157, -41.985114434386276, 173.66178005313157, -41.985114434386276], &quot;geometry&quot;: {&quot;coordinates&quot;: [173.66178005313157, -41.985114434386276], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;95&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2380426}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.56267367384544, -39.28945301498999, 175.56267367384544, -39.28945301498999], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.56267367384544, -39.28945301498999], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;96&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2797, &quot;t50_fid&quot;: 2408394}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55825450658153, -39.28630222220387, 175.55825450658153, -39.28630222220387], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55825450658153, -39.28630222220387], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;97&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2757, &quot;t50_fid&quot;: 2408395}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.55807189825757, -39.275882311319535, 175.55807189825757, -39.275882311319535], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.55807189825757, -39.275882311319535], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;98&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2751, &quot;t50_fid&quot;: 2408397}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57632420640633, -39.267007290301024, 175.57632420640633, -39.267007290301024], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57632420640633, -39.267007290301024], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;99&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2720, &quot;t50_fid&quot;: 2408406}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [175.57896195537916, -39.266375461366366, 175.57896195537916, -39.266375461366366], &quot;geometry&quot;: {&quot;coordinates&quot;: [175.57896195537916, -39.266375461366366], &quot;type&quot;: &quot;Point&quot;}, &quot;id&quot;: &quot;100&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;red&quot;, &quot;elevation&quot;: 2732, &quot;t50_fid&quot;: 2408411}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_54d91bed36b1a6973491a4f6e844eb77.bindTooltip(
+    geo_json_4c820a70e0abf84fc9394b59552981ee.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -2507,23 +2507,23 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="layers"><span class="he
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_54d91bed36b1a6973491a4f6e844eb77.addTo(map_7d3a8db677bdec60139ef1276988901e);
+            geo_json_4c820a70e0abf84fc9394b59552981ee.addTo(map_b71f301a24f9bec9c013f8d6df723872);
         
     
-            var layer_control_47530b40ac1a317d5b15551dcc1cd0e8_layers = {
+            var layer_control_ed9c9d39adf51113af5cab36885664cd_layers = {
                 base_layers : {
-                    &quot;openstreetmap&quot; : tile_layer_4be586ba919b5128dd221edfb6ad06db,
+                    &quot;openstreetmap&quot; : tile_layer_1ce8ce3df5c36758f1beb25d188971ea,
                 },
                 overlays :  {
-                    &quot;Polygons (adm. areas)&quot; : geo_json_d7faf3b90ee69d4f2ee0527e0da8e75c,
-                    &quot;Points (elevation)&quot; : geo_json_54d91bed36b1a6973491a4f6e844eb77,
+                    &quot;Polygons (adm. areas)&quot; : geo_json_ff89e9a0a4b431c695d55eb39fc0fc07,
+                    &quot;Points (elevation)&quot; : geo_json_4c820a70e0abf84fc9394b59552981ee,
                 },
             };
-            let layer_control_47530b40ac1a317d5b15551dcc1cd0e8 = L.control.layers(
-                layer_control_47530b40ac1a317d5b15551dcc1cd0e8_layers.base_layers,
-                layer_control_47530b40ac1a317d5b15551dcc1cd0e8_layers.overlays,
+            let layer_control_ed9c9d39adf51113af5cab36885664cd = L.control.layers(
+                layer_control_ed9c9d39adf51113af5cab36885664cd_layers.base_layers,
+                layer_control_ed9c9d39adf51113af5cab36885664cd_layers.overlays,
                 {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: false, &quot;position&quot;: &quot;topright&quot;}
-            ).addTo(map_7d3a8db677bdec60139ef1276988901e);
+            ).addTo(map_b71f301a24f9bec9c013f8d6df723872);
 
         
 </script>
@@ -2571,7 +2571,7 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_99b57011f4b0b78a5971542d2288eab0 {
+                #map_cee764b413a0c9d0c496d779588d2be1 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -2602,14 +2602,14 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_99b57011f4b0b78a5971542d2288eab0&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_cee764b413a0c9d0c496d779588d2be1&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_99b57011f4b0b78a5971542d2288eab0 = L.map(
-                &quot;map_99b57011f4b0b78a5971542d2288eab0&quot;,
+            var map_cee764b413a0c9d0c496d779588d2be1 = L.map(
+                &quot;map_cee764b413a0c9d0c496d779588d2be1&quot;,
                 {
                     center: [-40.84868556642058, 172.48833825792866],
                     crs: L.CRS.EPSG3857,
@@ -2618,28 +2618,28 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_99b57011f4b0b78a5971542d2288eab0);
+            L.control.scale().addTo(map_cee764b413a0c9d0c496d779588d2be1);
 
             
 
         
     
-            var tile_layer_eb0b79acdc58ed2dcc7d6d631038409a = L.tileLayer(
+            var tile_layer_6c6014d393692ec9d9a6ffb2f4fd9523 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_eb0b79acdc58ed2dcc7d6d631038409a.addTo(map_99b57011f4b0b78a5971542d2288eab0);
+            tile_layer_6c6014d393692ec9d9a6ffb2f4fd9523.addTo(map_cee764b413a0c9d0c496d779588d2be1);
         
     
-            map_99b57011f4b0b78a5971542d2288eab0.fitBounds(
+            map_cee764b413a0c9d0c496d779588d2be1.fitBounds(
                 [[-47.28285241600581, 166.42630291457417], [-34.414518716835346, 178.55037360128316]],
                 {}
             );
         
     
-        function geo_json_4305f46ae44442581c0617c9225a211f_styler(feature) {
+        function geo_json_e458852d8b368e64bf30b6d4eac19f8c_styler(feature) {
             switch(feature.id) {
                 case &quot;0&quot;: 
                     return {&quot;color&quot;: &quot;#fff5f0&quot;, &quot;fillColor&quot;: &quot;#fff5f0&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
@@ -2673,52 +2673,52 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
                     return {&quot;color&quot;: &quot;#fc9272&quot;, &quot;fillColor&quot;: &quot;#fc9272&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_4305f46ae44442581c0617c9225a211f_highlighter(feature) {
+        function geo_json_e458852d8b368e64bf30b6d4eac19f8c_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_4305f46ae44442581c0617c9225a211f_pointToLayer(feature, latlng) {
+        function geo_json_e458852d8b368e64bf30b6d4eac19f8c_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_4305f46ae44442581c0617c9225a211f_styler(feature)
+            let style = geo_json_e458852d8b368e64bf30b6d4eac19f8c_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_4305f46ae44442581c0617c9225a211f_onEachFeature(feature, layer) {
+        function geo_json_e458852d8b368e64bf30b6d4eac19f8c_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_4305f46ae44442581c0617c9225a211f.resetStyle(e.target);
+                            geo_json_e458852d8b368e64bf30b6d4eac19f8c.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_4305f46ae44442581c0617c9225a211f_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_e458852d8b368e64bf30b6d4eac19f8c_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_4305f46ae44442581c0617c9225a211f = L.geoJson(null, {
-                onEachFeature: geo_json_4305f46ae44442581c0617c9225a211f_onEachFeature,
+        var geo_json_e458852d8b368e64bf30b6d4eac19f8c = L.geoJson(null, {
+                onEachFeature: geo_json_e458852d8b368e64bf30b6d4eac19f8c_onEachFeature,
             
-                style: geo_json_4305f46ae44442581c0617c9225a211f_styler,
-                pointToLayer: geo_json_4305f46ae44442581c0617c9225a211f_pointToLayer,
+                style: geo_json_e458852d8b368e64bf30b6d4eac19f8c_styler,
+                pointToLayer: geo_json_e458852d8b368e64bf30b6d4eac19f8c_pointToLayer,
         });
 
-        function geo_json_4305f46ae44442581c0617c9225a211f_add (data) {
-            geo_json_4305f46ae44442581c0617c9225a211f
+        function geo_json_e458852d8b368e64bf30b6d4eac19f8c_add (data) {
+            geo_json_e458852d8b368e64bf30b6d4eac19f8c
                 .addData(data);
         }
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-    geo_json_4305f46ae44442581c0617c9225a211f.bindTooltip(
+    geo_json_e458852d8b368e64bf30b6d4eac19f8c.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -2742,46 +2742,46 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_4305f46ae44442581c0617c9225a211f.addTo(map_99b57011f4b0b78a5971542d2288eab0);
+            geo_json_e458852d8b368e64bf30b6d4eac19f8c.addTo(map_cee764b413a0c9d0c496d779588d2be1);
         
     
-    var color_map_9219cfafe72868251cfaa13278fb3f8f = {};
+    var color_map_dfe4d58a409dd07493297de02959c112 = {};
 
     
-    color_map_9219cfafe72868251cfaa13278fb3f8f.color = d3.scale.threshold()
+    color_map_dfe4d58a409dd07493297de02959c112.color = d3.scale.threshold()
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-    color_map_9219cfafe72868251cfaa13278fb3f8f.x = d3.scale.linear()
+    color_map_dfe4d58a409dd07493297de02959c112.x = d3.scale.linear()
               .domain([23400.0, 32700.0])
               .range([0, 450 - 50]);
 
-    color_map_9219cfafe72868251cfaa13278fb3f8f.legend = L.control({position: 'topright'});
-    color_map_9219cfafe72868251cfaa13278fb3f8f.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
-    color_map_9219cfafe72868251cfaa13278fb3f8f.legend.addTo(map_99b57011f4b0b78a5971542d2288eab0);
+    color_map_dfe4d58a409dd07493297de02959c112.legend = L.control({position: 'topright'});
+    color_map_dfe4d58a409dd07493297de02959c112.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
+    color_map_dfe4d58a409dd07493297de02959c112.legend.addTo(map_cee764b413a0c9d0c496d779588d2be1);
 
-    color_map_9219cfafe72868251cfaa13278fb3f8f.xAxis = d3.svg.axis()
-        .scale(color_map_9219cfafe72868251cfaa13278fb3f8f.x)
+    color_map_dfe4d58a409dd07493297de02959c112.xAxis = d3.svg.axis()
+        .scale(color_map_dfe4d58a409dd07493297de02959c112.x)
         .orient(&quot;top&quot;)
         .tickSize(1)
         .tickValues([23400.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 24348.235294117647, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 25296.470588235294, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 26244.70588235294, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 27192.941176470587, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 28141.176470588234, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 29089.41176470588, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 30037.64705882353, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 30985.882352941175, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 31934.117647058825, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']);
 
-    color_map_9219cfafe72868251cfaa13278fb3f8f.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)
+    color_map_dfe4d58a409dd07493297de02959c112.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)
         .attr(&quot;id&quot;, 'legend')
         .attr(&quot;width&quot;, 450)
         .attr(&quot;height&quot;, 40);
 
-    color_map_9219cfafe72868251cfaa13278fb3f8f.g = color_map_9219cfafe72868251cfaa13278fb3f8f.svg.append(&quot;g&quot;)
+    color_map_dfe4d58a409dd07493297de02959c112.g = color_map_dfe4d58a409dd07493297de02959c112.svg.append(&quot;g&quot;)
         .attr(&quot;class&quot;, &quot;key&quot;)
         .attr(&quot;fill&quot;, &quot;black&quot;)
         .attr(&quot;transform&quot;, &quot;translate(25,16)&quot;);
 
-    color_map_9219cfafe72868251cfaa13278fb3f8f.g.selectAll(&quot;rect&quot;)
-        .data(color_map_9219cfafe72868251cfaa13278fb3f8f.color.range().map(function(d, i) {
+    color_map_dfe4d58a409dd07493297de02959c112.g.selectAll(&quot;rect&quot;)
+        .data(color_map_dfe4d58a409dd07493297de02959c112.color.range().map(function(d, i) {
           return {
-            x0: i ? color_map_9219cfafe72868251cfaa13278fb3f8f.x(color_map_9219cfafe72868251cfaa13278fb3f8f.color.domain()[i - 1]) : color_map_9219cfafe72868251cfaa13278fb3f8f.x.range()[0],
-            x1: i < color_map_9219cfafe72868251cfaa13278fb3f8f.color.domain().length ? color_map_9219cfafe72868251cfaa13278fb3f8f.x(color_map_9219cfafe72868251cfaa13278fb3f8f.color.domain()[i]) : color_map_9219cfafe72868251cfaa13278fb3f8f.x.range()[1],
+            x0: i ? color_map_dfe4d58a409dd07493297de02959c112.x(color_map_dfe4d58a409dd07493297de02959c112.color.domain()[i - 1]) : color_map_dfe4d58a409dd07493297de02959c112.x.range()[0],
+            x1: i < color_map_dfe4d58a409dd07493297de02959c112.color.domain().length ? color_map_dfe4d58a409dd07493297de02959c112.x(color_map_dfe4d58a409dd07493297de02959c112.color.domain()[i]) : color_map_dfe4d58a409dd07493297de02959c112.x.range()[1],
             z: d
           };
         }))
@@ -2791,7 +2791,7 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
         .attr(&quot;width&quot;, function(d) { return d.x1 - d.x0; })
         .style(&quot;fill&quot;, function(d) { return d.z; });
 
-    color_map_9219cfafe72868251cfaa13278fb3f8f.g.call(color_map_9219cfafe72868251cfaa13278fb3f8f.xAxis).append(&quot;text&quot;)
+    color_map_dfe4d58a409dd07493297de02959c112.g.call(color_map_dfe4d58a409dd07493297de02959c112.xAxis).append(&quot;text&quot;)
         .attr(&quot;class&quot;, &quot;caption&quot;)
         .attr(&quot;y&quot;, 21)
         .attr(&quot;fill&quot;, &quot;black&quot;)
@@ -2843,7 +2843,7 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_3fcace7aee14b72731f5492a3e5c4833 {
+                #map_ec983132d076df5fda6fb0b3342a26ea {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -2874,14 +2874,14 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_3fcace7aee14b72731f5492a3e5c4833&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_ec983132d076df5fda6fb0b3342a26ea&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_3fcace7aee14b72731f5492a3e5c4833 = L.map(
-                &quot;map_3fcace7aee14b72731f5492a3e5c4833&quot;,
+            var map_ec983132d076df5fda6fb0b3342a26ea = L.map(
+                &quot;map_ec983132d076df5fda6fb0b3342a26ea&quot;,
                 {
                     center: [-40.84868556642058, 172.48833825792866],
                     crs: L.CRS.EPSG3857,
@@ -2890,28 +2890,28 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_3fcace7aee14b72731f5492a3e5c4833);
+            L.control.scale().addTo(map_ec983132d076df5fda6fb0b3342a26ea);
 
             
 
         
     
-            var tile_layer_d4dbc6a159ebb272b07d1cbaf22db0f4 = L.tileLayer(
+            var tile_layer_66868397d199aa060b06d85ab1b68621 = L.tileLayer(
                 &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_d4dbc6a159ebb272b07d1cbaf22db0f4.addTo(map_3fcace7aee14b72731f5492a3e5c4833);
+            tile_layer_66868397d199aa060b06d85ab1b68621.addTo(map_ec983132d076df5fda6fb0b3342a26ea);
         
     
-            map_3fcace7aee14b72731f5492a3e5c4833.fitBounds(
+            map_ec983132d076df5fda6fb0b3342a26ea.fitBounds(
                 [[-47.28285241600581, 166.42630291457417], [-34.414518716835346, 178.55037360128316]],
                 {}
             );
         
     
-        function geo_json_46eaee6361d917dedd0ea16a0f9a64f4_styler(feature) {
+        function geo_json_b1975aaec10d301377ede919891a38ca_styler(feature) {
             switch(feature.id) {
                 case &quot;0&quot;: 
                     return {&quot;color&quot;: &quot;black&quot;, &quot;fillColor&quot;: &quot;#fff5f0&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 0.5};
@@ -2945,52 +2945,52 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
                     return {&quot;color&quot;: &quot;black&quot;, &quot;fillColor&quot;: &quot;#fc9272&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 0.5};
             }
         }
-        function geo_json_46eaee6361d917dedd0ea16a0f9a64f4_highlighter(feature) {
+        function geo_json_b1975aaec10d301377ede919891a38ca_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_46eaee6361d917dedd0ea16a0f9a64f4_pointToLayer(feature, latlng) {
+        function geo_json_b1975aaec10d301377ede919891a38ca_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_46eaee6361d917dedd0ea16a0f9a64f4_styler(feature)
+            let style = geo_json_b1975aaec10d301377ede919891a38ca_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_46eaee6361d917dedd0ea16a0f9a64f4_onEachFeature(feature, layer) {
+        function geo_json_b1975aaec10d301377ede919891a38ca_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_46eaee6361d917dedd0ea16a0f9a64f4.resetStyle(e.target);
+                            geo_json_b1975aaec10d301377ede919891a38ca.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_46eaee6361d917dedd0ea16a0f9a64f4_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_b1975aaec10d301377ede919891a38ca_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_46eaee6361d917dedd0ea16a0f9a64f4 = L.geoJson(null, {
-                onEachFeature: geo_json_46eaee6361d917dedd0ea16a0f9a64f4_onEachFeature,
+        var geo_json_b1975aaec10d301377ede919891a38ca = L.geoJson(null, {
+                onEachFeature: geo_json_b1975aaec10d301377ede919891a38ca_onEachFeature,
             
-                style: geo_json_46eaee6361d917dedd0ea16a0f9a64f4_styler,
-                pointToLayer: geo_json_46eaee6361d917dedd0ea16a0f9a64f4_pointToLayer,
+                style: geo_json_b1975aaec10d301377ede919891a38ca_styler,
+                pointToLayer: geo_json_b1975aaec10d301377ede919891a38ca_pointToLayer,
         });
 
-        function geo_json_46eaee6361d917dedd0ea16a0f9a64f4_add (data) {
-            geo_json_46eaee6361d917dedd0ea16a0f9a64f4
+        function geo_json_b1975aaec10d301377ede919891a38ca_add (data) {
+            geo_json_b1975aaec10d301377ede919891a38ca
                 .addData(data);
         }
-            geo_json_46eaee6361d917dedd0ea16a0f9a64f4_add({&quot;bbox&quot;: [166.42630291457417, -47.28285241600581, 178.55037360128316, -34.414518716835346], &quot;features&quot;: [{&quot;bbox&quot;: [172.653880649817, -36.39288306661124, 174.61663880065558, -34.414518716835346], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[174.61663880065558, -36.117560466595535], [174.5628090505642, -36.179000182759175], [174.48174833315713, -36.22827699994316], [174.33896590023375, -36.316829716883234], [174.21457326690228, -36.25871166608587], [174.12877275047865, -36.17361239945035], [174.04599758389062, -36.17016955012603], [174.09597213385467, -36.24488793346581], [174.1553684830745, -36.27609723272722], [174.18186668339544, -36.36303589981312], [174.13535050025587, -36.39204991691119], [174.04052755054076, -36.39288306661124], [173.9836758838081, -36.233704999678864], [173.8660469165703, -36.09120011624559], [173.68809813355304, -35.89241744971263], [173.3651805831832, -35.54740169951351], [173.35661301733074, -35.522817733419544], [173.05424363403117, -35.191238166349635], [173.13055999973318, -35.17893233285171], [173.1680931826238, -35.136427332750515], [173.16744721718428, -35.036368649459554], [173.08100574991772, -34.89239194966177], [172.85122291782795, -34.629713366319244], [172.653880649817, -34.478007650204745], [172.69995103368962, -34.42638239948652], [172.79306545076662, -34.453994282761165], [172.86903523358103, -34.414518716835346], [173.01444231654213, -34.42413689980644], [172.98479144999794, -34.52780733266056], [173.0251309837217, -34.65351909975858], [173.12020441726813, -34.81668376714118], [173.19932491641322, -34.87096884981918], [173.28077288324707, -34.89252611725473], [173.2148148999132, -34.96125576674768], [173.28839824919172, -35.01323163320772], [173.34008769973812, -34.95823121645178], [173.2869148834167, -34.86349856609251], [173.429163483407, -34.82514861607076], [173.38970963384807, -34.953496332924786], [173.43403079967044, -34.98593614971705], [173.535566349955, -34.998453066528455], [173.56263480026917, -34.92156796652915], [173.64403248328355, -34.966194082924126], [173.7011422000139, -34.95247671611629], [173.7400850006401, -35.03720648344937], [173.84127288406202, -34.99430931619474], [173.93127771670152, -35.047298649845054], [173.96328455017542, -35.11886259980355], [174.07853963272868, -35.11613150012657], [174.07995838261888, -35.27309775008877], [174.12546294997819, -35.29403403322612], [174.28978491698982, -35.275148000145364], [174.35016953163944, -35.32641874893325], [174.33965530012713, -35.37687990040389], [174.46839201688135, -35.49075376687877], [174.4747193838012, -35.5563464661239], [174.53675399955168, -35.58768178348709], [174.50237205005004, -35.64083380019665], [174.55760146664247, -35.724016733106474], [174.555188500026, -35.86089100005855], [174.48556345038355, -35.786374216362894], [174.42268665044037, -35.78483209983785], [174.40483026652885, -35.83101551717666], [174.50080928337724, -35.84106318298678], [174.46014496670307, -35.91453494982014], [174.51000050039923, -36.03046544956882], [174.58978090047464, -36.048189866051466], [174.61663880065558, -36.117560466595535]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;Island&quot;: &quot;North&quot;, &quot;Land_area&quot;: 12500.5611491, &quot;Median_income&quot;: 23400, &quot;Name&quot;: &quot;Northland&quot;, &quot;Population&quot;: 175500.0, &quot;Sex_ratio&quot;: 0.9424532288425339, &quot;__folium_color&quot;: &quot;#fff5f0&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [174.16870360025004, -37.2924320832104, 175.54321218369878, -36.049713299334], &quot;geometry&quot;: {&quot;coordinates&quot;: [[[[175.2913041330846, -37.0240384665269], [175.15209103343886, -37.02155834992554], [175.14112033357597, -37.175603383298196], [175.04824261719523, -37.18227119963404], [174.99025808255493, -37.21658549964009], [174.86895123279626, -37.244986600243344], [174.81548804984917, -37.22863088286862], [174.6596283674953, -37.2924320832104], [174.53863788279378, -37.076632400180756], [174.62704846647915, -37.03673408325686], [174.69063416727937, -37.136649000059386], [174.7395015340902, -37.12622546684114], [174.85765245014278, -37.05109428303421], [174.82687481700577, -37.0129061169001], [174.744100215986, -36.98433388285374], [174.68155210008374, -36.93381471603755], [174.58382766705083, -37.00731111635251], [174.48303651744965, -37.0480030494191], [174.46861143366093, -36.9540757993889], [174.4244018664584, -36.82421951647431], [174.2736150837212, -36.61134365055641], [174.16870360025004, -36.494430850037226], [174.18791933376852, -36.43449401649246], [174.23583539982297, -36.428090616840876], [174.31274595016188, -36.53649259944757], [174.40477638388748, -36.624236499982295], [174.43132198401986, -36.51100546620635], [174.41488555051643, -36.40366161618788], 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-    geo_json_46eaee6361d917dedd0ea16a0f9a64f4.bindTooltip(
+    geo_json_b1975aaec10d301377ede919891a38ca.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -3014,46 +3014,46 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_46eaee6361d917dedd0ea16a0f9a64f4.addTo(map_3fcace7aee14b72731f5492a3e5c4833);
+            geo_json_b1975aaec10d301377ede919891a38ca.addTo(map_ec983132d076df5fda6fb0b3342a26ea);
         
     
-    var color_map_5cd83bc7fea987b4056012f5ffcbe6ae = {};
+    var color_map_ede6085354529046307d01d28b7569ed = {};
 
     
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.color = d3.scale.threshold()
+    color_map_ede6085354529046307d01d28b7569ed.color = d3.scale.threshold()
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29047.094188376752, 29065.73146292585, 29084.36873747495, 29103.00601202405, 29121.643286573148, 29140.280561122243, 29158.917835671342, 29177.55511022044, 29196.19238476954, 29214.82965931864, 29233.466933867734, 29252.104208416833, 29270.741482965932, 29289.37875751503, 29308.01603206413, 29326.653306613225, 29345.290581162324, 29363.927855711423, 29382.565130260522, 29401.20240480962, 29419.839679358716, 29438.476953907815, 29457.114228456914, 29475.751503006013, 29494.388777555112, 29513.026052104207, 29531.663326653306, 29550.300601202405, 29568.937875751504, 29587.575150300603, 29606.2124248497, 29624.849699398797, 29643.486973947896, 29662.124248496995, 29680.761523046094, 29699.39879759519, 29718.03607214429, 29736.673346693387, 29755.310621242483, 29773.947895791585, 29792.58517034068, 29811.22244488978, 29829.85971943888, 29848.496993987974, 29867.134268537073, 29885.77154308617, 29904.40881763527, 29923.04609218437, 29941.683366733465, 29960.320641282564, 29978.957915831663, 29997.59519038076, 30016.23246492986, 30034.869739478956, 30053.507014028055, 30072.144288577154, 30090.781563126253, 30109.418837675352, 30128.056112224447, 30146.693386773546, 30165.330661322645, 30183.967935871744, 30202.605210420843, 30221.24248496994, 30239.879759519037, 30258.517034068136, 30277.154308617235, 30295.791583166334, 30314.42885771543, 30333.06613226453, 30351.703406813627, 30370.340681362726, 30388.977955911825, 30407.61523046092, 30426.25250501002, 30444.88977955912, 30463.527054108217, 30482.164328657316, 30500.80160320641, 30519.43887775551, 30538.07615230461, 30556.71342685371, 30575.350701402807, 30593.987975951903, 30612.625250501, 30631.2625250501, 30649.8997995992, 30668.5370741483, 30687.174348697394, 30705.811623246493, 30724.44889779559, 30743.08617234469, 30761.72344689379, 30780.360721442885, 30798.997995991984, 30817.635270541083, 30836.27254509018, 30854.90981963928, 30873.547094188376, 30892.184368737475, 30910.821643286574, 30929.45891783567, 30948.09619238477, 30966.733466933867, 30985.370741482966, 31004.008016032065, 31022.64529058116, 31041.28256513026, 31059.919839679358, 31078.557114228457, 31097.194388777556, 31115.83166332665, 31134.46893787575, 31153.10621242485, 31171.743486973948, 31190.380761523047, 31209.018036072142, 31227.65531062124, 31246.29258517034, 31264.92985971944, 31283.56713426854, 31302.204408817634, 31320.841683366732, 31339.47895791583, 31358.11623246493, 31376.75350701403, 31395.390781563125, 31414.028056112224, 31432.665330661323, 31451.30260521042, 31469.93987975952, 31488.577154308616, 31507.214428857715, 31525.851703406814, 31544.488977955913, 31563.12625250501, 31581.763527054107, 31600.400801603206, 31619.038076152305, 31637.675350701404, 31656.312625250503, 31674.949899799598, 31693.587174348697, 31712.224448897796, 31730.86172344689, 31749.498997995994, 31768.13627254509, 31786.773547094188, 31805.410821643287, 31824.048096192382, 31842.685370741485, 31861.32264529058, 31879.95991983968, 31898.597194388778, 31917.234468937873, 31935.871743486976, 31954.50901803607, 31973.14629258517, 31991.78356713427, 32010.420841683364, 32029.058116232467, 32047.695390781562, 32066.33266533066, 32084.96993987976, 32103.607214428856, 32122.244488977958, 32140.881763527053, 32159.519038076152, 32178.15631262525, 32196.793587174347, 32215.43086172345, 32234.068136272545, 32252.705410821643, 32271.342685370742, 32289.979959919838, 32308.61723446894, 32327.254509018036, 32345.891783567135, 32364.529058116234, 32383.16633266533, 32401.80360721443, 32420.440881763527, 32439.078156312626, 32457.715430861725, 32476.35270541082, 32494.989979959922, 32513.627254509018, 32532.264529058117, 32550.901803607216, 32569.53907815631, 32588.17635270541, 32606.81362725451, 32625.450901803608, 32644.088176352707, 32662.725450901802, 32681.3627254509, 32700.0])
               .range(['#fff5f0ff', '#fff5f0ff', '#fff4efff', '#fff4efff', '#fff4eeff', '#fff4eeff', '#fff3edff', '#fff3edff', '#fff2ecff', '#fff2ecff', '#fff2ebff', '#fff2ebff', '#fff1eaff', '#fff1eaff', '#fff0e9ff', '#fff0e9ff', '#fff0e8ff', '#fff0e8ff', '#ffefe8ff', '#ffefe8ff', '#ffeee7ff', '#ffeee7ff', '#ffeee6ff', '#ffeee6ff', '#ffede5ff', '#ffede5ff', '#ffece4ff', '#ffece4ff', '#ffece3ff', '#ffece3ff', '#ffebe2ff', '#ffebe2ff', '#feeae1ff', '#feeae1ff', '#feeae0ff', '#feeadfff', '#fee9dfff', '#fee9deff', '#fee8deff', '#fee8ddff', '#fee8ddff', '#fee7dcff', '#fee7dcff', '#fee7dbff', '#fee7dbff', '#fee6daff', '#fee6daff', '#fee5d9ff', '#fee5d9ff', '#fee5d8ff', '#fee5d8ff', '#fee4d8ff', '#fee4d8ff', '#fee3d7ff', '#fee3d7ff', '#fee3d6ff', '#fee3d6ff', '#fee2d5ff', '#fee2d5ff', '#fee1d4ff', '#fee1d4ff', '#fee1d3ff', '#fee1d3ff', '#fee0d2ff', '#fee0d1ff', '#fedfd0ff', '#fedfd0ff', '#fedecfff', '#feddceff', '#fedccdff', '#fedccdff', '#fedbccff', '#fedbcbff', '#fedacaff', '#fedac9ff', '#fed9c9ff', '#fed9c8ff', '#fed8c7ff', '#fed7c6ff', '#fdd7c6ff', '#fdd6c5ff', '#fdd5c3ff', '#fdd4c2ff', '#fdd4c2ff', '#fdd3c1ff', '#fdd3c0ff', '#fdd2bfff', '#fdd2bfff', '#fdd1beff', '#fdd1bdff', '#fdd0bcff', '#fdcfbcff', '#fdcebbff', '#fdcebaff', '#fdcdb9ff', '#fdcdb9ff', '#fdccb8ff', '#fdccb7ff', '#fdcbb6ff', '#fdcbb6ff', '#fdcab5ff', '#fdcab4ff', '#fdc9b3ff', '#fdc8b3ff', '#fdc7b2ff', '#fdc7b1ff', '#fdc6b0ff', '#fdc6afff', '#fdc5aeff', '#fdc5adff', '#fcc4adff', '#fcc4acff', '#fcc3abff', '#fcc3aaff', '#fcc2aaff', '#fcc1a9ff', '#fcc1a8ff', '#fcc0a7ff', '#fcbfa7ff', '#fcbea6ff', '#fcbea5ff', '#fcbda4ff', '#fcbda3ff', '#fcbca2ff', '#fcbca2ff', '#fcbba1ff', '#fcbaa0ff', '#fcb99fff', '#fcb99fff', '#fcb89eff', '#fcb89dff', '#fcb79cff', '#fcb79cff', '#fcb69bff', '#fcb59aff', '#fcb499ff', '#fcb499ff', '#fcb398ff', '#fcb397ff', '#fcb296ff', '#fcb196ff', '#fcb095ff', '#fcb094ff', '#fcaf93ff', '#fcaf92ff', '#fcae92ff', '#fcae91ff', '#fcad90ff', '#fcac8fff', '#fcab8fff', '#fcab8eff', '#fcaa8dff', '#fca98cff', '#fca98cff', '#fca88bff', '#fca78bff', '#fca68aff', '#fca689ff', '#fca588ff', '#fca588ff', '#fca487ff', '#fca486ff', '#fca385ff', '#fca284ff', '#fca183ff', '#fca183ff', '#fca082ff', '#fc9f81ff', '#fc9e80ff', '#fc9e80ff', '#fc9d7fff', '#fc9d7eff', '#fc9c7dff', '#fc9c7dff', '#fc9b7cff', '#fc9a7bff', '#fc997aff', '#fc997aff', '#fc9879ff', '#fc9878ff', '#fc9777ff', '#fc9676ff', '#fc9576ff', '#fc9575ff', '#fc9474ff', '#fc9473ff', '#fc9373ff', '#fc9372ff', '#fc9272ff', '#fc9171ff', '#fc9070ff', '#fc8f6fff', '#fc8f6fff', '#fc8e6eff', '#fc8e6eff', '#fc8d6dff', '#fc8d6dff', '#fc8c6cff', '#fc8b6bff', '#fc8a6aff', '#fc8a6aff', '#fc8969ff', '#fc8968ff', '#fc8867ff', '#fc8767ff', '#fc8666ff', '#fc8666ff', '#fc8565ff', '#fc8565ff', '#fc8464ff', '#fc8363ff', '#fc8262ff', '#fc8262ff', '#fc8161ff', '#fc8161ff', '#fc8060ff', '#fc805fff', '#fc7f5fff', '#fc7e5eff', '#fc7d5dff', '#fb7d5cff', '#fb7c5cff', '#fb7c5bff', '#fb7b5bff', '#fb7b5aff', '#fb7a5aff', '#fb7959ff', '#fb7858ff', '#fb7757ff', '#fb7757ff', '#fb7656ff', '#fb7656ff', '#fb7555ff', '#fb7555ff', '#fb7454ff', '#fb7353ff', '#fb7252ff', '#fb7252ff', '#fb7151ff', '#fb7151ff', '#fb7050ff', '#fb704fff', '#fb6f4eff', '#fb6e4eff', '#fb6d4dff', '#fb6d4dff', '#fb6c4cff', '#fb6c4cff', '#fb6b4bff', '#fb6a4bff', '#fb694aff', '#fb694aff', '#fb6849ff', '#fa6748ff', '#fa6648ff', '#fa6647ff', '#fa6547ff', '#fa6446ff', '#fa6346ff', '#f96345ff', '#f96245ff', '#f96144ff', '#f96044ff', '#f95f43ff', '#f95f43ff', '#f85e42ff', '#f85d42ff', '#f85c41ff', '#f85c41ff', '#f75b40ff', '#f75b40ff', '#f75a3fff', '#f7593fff', '#f7583eff', '#f6583eff', '#f6573dff', '#f6563dff', '#f6553cff', '#f6553cff', '#f6543bff', '#f5533bff', '#f5523aff', '#f5523aff', '#f5513aff', '#f4503aff', '#f44f39ff', '#f44f39ff', '#f44d38ff', '#f44d38ff', '#f44c37ff', '#f34b37ff', '#f34a36ff', '#f34a35ff', '#f34935ff', '#f34834ff', '#f34734ff', '#f24733ff', '#f24633ff', '#f24532ff', '#f24432ff', '#f14331ff', '#f14331ff', '#f14230ff', '#f14130ff', '#f1402fff', '#f1402fff', '#f03f2eff', '#f03f2eff', '#f03e2dff', '#f03d2dff', '#f03c2cff', '#f03c2cff', '#ef3b2cff', '#ee3a2cff', '#ee392bff', '#ed392bff', '#ed382bff', '#ec382bff', '#ec372aff', '#eb372aff', '#eb362aff', '#ea362aff', '#ea3529ff', '#e93529ff', '#e93429ff', '#e83429ff', '#e73328ff', '#e63328ff', '#e63228ff', '#e53128ff', '#e53027ff', '#e43027ff', '#e42f27ff', '#e32f27ff', '#e32e27ff', '#e22d26ff', '#e22d26ff', '#e12c26ff', '#e12c26ff', '#e02b25ff', '#df2b25ff', '#de2a25ff', '#de2a25ff', '#dd2924ff', '#dd2924ff', '#dc2824ff', '#dc2824ff', '#db2723ff', '#db2723ff', '#da2623ff', '#d92523ff', '#d92422ff', '#d82422ff', '#d82322ff', '#d72322ff', '#d72221ff', '#d52221ff', '#d52121ff', '#d42121ff', '#d42020ff', '#d32020ff', '#d31f20ff', '#d21f20ff', '#d21e1fff', '#d11e1fff', '#d11d1fff', '#d01d1fff', '#d01c1fff', '#cf1b1fff', '#cf1a1eff', '#ce1a1eff', '#cd191eff', '#cc181eff', '#cc181dff', '#cb181dff', '#cb181dff', '#ca181dff', '#ca181dff', '#c9171cff', '#c9171cff', '#c8171cff', '#c8171cff', '#c7171cff', '#c6171cff', '#c5161cff', '#c5161cff', '#c4161bff', '#c4161bff', '#c3161bff', '#c2161bff', '#c2161bff', '#c1161bff', '#c1151bff', '#c0151bff', '#bf151aff', '#be151aff', '#be151aff', '#bd151aff', '#bd141aff', '#bc141aff', '#bc141aff', '#bb141aff', '#ba1419ff', '#b91419ff', '#b91419ff', '#b81419ff', '#b81319ff', '#b71319ff', '#b71319ff', '#b61319ff', '#b61318ff', '#b51318ff', '#b41218ff', '#b31218ff', '#b31218ff', '#b21218ff', '#b21218ff', '#b11217ff', '#b11217ff', '#b01117ff', '#b01117ff', '#af1117ff', '#ae1117ff', '#ad1117ff', '#ad1117ff', '#ac1016ff', '#ab1016ff', '#ab1016ff', '#aa1016ff', '#aa1016ff', '#a91016ff', '#a91016ff', '#a81016ff', '#a80f15ff', '#a70f15ff', '#a60f15ff', '#a50f15ff', '#a50f15ff', '#a30f15ff', '#a20e15ff', '#a10e15ff', '#a00e14ff', '#9f0e14ff', '#9e0d14ff', '#9d0d14ff', '#9d0d14ff', '#9c0d14ff', '#9b0c14ff', '#9a0c14ff', '#990c13ff', '#980c13ff', '#970b13ff', '#960b13ff', '#950b13ff', '#940b13ff', '#930a13ff', '#920a13ff', '#910a12ff', '#900912ff', '#8f0912ff', '#8e0912ff', '#8d0912ff', '#8c0812ff', '#8b0812ff', '#8a0811ff', '#890811ff', '#880811ff', '#870811ff', '#860711ff', '#850711ff', '#840711ff', '#830711ff', '#820610ff', '#810610ff', '#800610ff', '#7f0610ff', '#7d0510ff', '#7c0510ff', '#7b0510ff', '#7a0510ff', '#7a040fff', '#79040fff', '#78040fff', '#77040fff', '#76030fff', '#75030fff', '#74030fff', '#73030fff', '#72020eff', '#71020eff', '#70020eff', '#6f020eff', '#6e010eff', '#6d010eff', '#6c010eff', '#6b010eff', '#6a000dff', '#69000dff', '#68000dff', '#67000dff']);
     
 
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.x = d3.scale.linear()
+    color_map_ede6085354529046307d01d28b7569ed.x = d3.scale.linear()
               .domain([23400.0, 32700.0])
               .range([0, 450 - 50]);
 
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.legend = L.control({position: 'topright'});
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.legend.addTo(map_3fcace7aee14b72731f5492a3e5c4833);
+    color_map_ede6085354529046307d01d28b7569ed.legend = L.control({position: 'topright'});
+    color_map_ede6085354529046307d01d28b7569ed.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
+    color_map_ede6085354529046307d01d28b7569ed.legend.addTo(map_ec983132d076df5fda6fb0b3342a26ea);
 
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.xAxis = d3.svg.axis()
-        .scale(color_map_5cd83bc7fea987b4056012f5ffcbe6ae.x)
+    color_map_ede6085354529046307d01d28b7569ed.xAxis = d3.svg.axis()
+        .scale(color_map_ede6085354529046307d01d28b7569ed.x)
         .orient(&quot;top&quot;)
         .tickSize(1)
         .tickValues([23400.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 24348.235294117647, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 25296.470588235294, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 26244.70588235294, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 27192.941176470587, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 28141.176470588234, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 29089.41176470588, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 30037.64705882353, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 30985.882352941175, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 31934.117647058825, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']);
 
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)
+    color_map_ede6085354529046307d01d28b7569ed.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)
         .attr(&quot;id&quot;, 'legend')
         .attr(&quot;width&quot;, 450)
         .attr(&quot;height&quot;, 40);
 
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.g = color_map_5cd83bc7fea987b4056012f5ffcbe6ae.svg.append(&quot;g&quot;)
+    color_map_ede6085354529046307d01d28b7569ed.g = color_map_ede6085354529046307d01d28b7569ed.svg.append(&quot;g&quot;)
         .attr(&quot;class&quot;, &quot;key&quot;)
         .attr(&quot;fill&quot;, &quot;black&quot;)
         .attr(&quot;transform&quot;, &quot;translate(25,16)&quot;);
 
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.g.selectAll(&quot;rect&quot;)
-        .data(color_map_5cd83bc7fea987b4056012f5ffcbe6ae.color.range().map(function(d, i) {
+    color_map_ede6085354529046307d01d28b7569ed.g.selectAll(&quot;rect&quot;)
+        .data(color_map_ede6085354529046307d01d28b7569ed.color.range().map(function(d, i) {
           return {
-            x0: i ? color_map_5cd83bc7fea987b4056012f5ffcbe6ae.x(color_map_5cd83bc7fea987b4056012f5ffcbe6ae.color.domain()[i - 1]) : color_map_5cd83bc7fea987b4056012f5ffcbe6ae.x.range()[0],
-            x1: i < color_map_5cd83bc7fea987b4056012f5ffcbe6ae.color.domain().length ? color_map_5cd83bc7fea987b4056012f5ffcbe6ae.x(color_map_5cd83bc7fea987b4056012f5ffcbe6ae.color.domain()[i]) : color_map_5cd83bc7fea987b4056012f5ffcbe6ae.x.range()[1],
+            x0: i ? color_map_ede6085354529046307d01d28b7569ed.x(color_map_ede6085354529046307d01d28b7569ed.color.domain()[i - 1]) : color_map_ede6085354529046307d01d28b7569ed.x.range()[0],
+            x1: i < color_map_ede6085354529046307d01d28b7569ed.color.domain().length ? color_map_ede6085354529046307d01d28b7569ed.x(color_map_ede6085354529046307d01d28b7569ed.color.domain()[i]) : color_map_ede6085354529046307d01d28b7569ed.x.range()[1],
             z: d
           };
         }))
@@ -3063,7 +3063,7 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="sec-explore-symbology">
         .attr(&quot;width&quot;, function(d) { return d.x1 - d.x0; })
         .style(&quot;fill&quot;, function(d) { return d.z; });
 
-    color_map_5cd83bc7fea987b4056012f5ffcbe6ae.g.call(color_map_5cd83bc7fea987b4056012f5ffcbe6ae.xAxis).append(&quot;text&quot;)
+    color_map_ede6085354529046307d01d28b7569ed.g.call(color_map_ede6085354529046307d01d28b7569ed.xAxis).append(&quot;text&quot;)
         .attr(&quot;class&quot;, &quot;caption&quot;)
         .attr(&quot;y&quot;, 21)
         .attr(&quot;fill&quot;, &quot;black&quot;)
@@ -3119,7 +3119,7 @@ <h3 data-number="8.3.5" class="anchored" data-anchor-id="basemaps-1"><span class
             <meta name=&quot;viewport&quot; content=&quot;width=device-width,
                 initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; />
             <style>
-                #map_e5c2c6c51b39433c5f381c5df4277652 {
+                #map_d1e3aa3be2f2ca6a5a0cb4dffabb6846 {
                     position: relative;
                     width: 100.0%;
                     height: 100.0%;
@@ -3149,14 +3149,14 @@ <h3 data-number="8.3.5" class="anchored" data-anchor-id="basemaps-1"><span class
 <body>
     
     
-            <div class=&quot;folium-map&quot; id=&quot;map_e5c2c6c51b39433c5f381c5df4277652&quot; ></div>
+            <div class=&quot;folium-map&quot; id=&quot;map_d1e3aa3be2f2ca6a5a0cb4dffabb6846&quot; ></div>
         
 </body>
 <script>
     
     
-            var map_e5c2c6c51b39433c5f381c5df4277652 = L.map(
-                &quot;map_e5c2c6c51b39433c5f381c5df4277652&quot;,
+            var map_d1e3aa3be2f2ca6a5a0cb4dffabb6846 = L.map(
+                &quot;map_d1e3aa3be2f2ca6a5a0cb4dffabb6846&quot;,
                 {
                     center: [-40.84868556642058, 172.48833825792866],
                     crs: L.CRS.EPSG3857,
@@ -3165,79 +3165,79 @@ <h3 data-number="8.3.5" class="anchored" data-anchor-id="basemaps-1"><span class
                     preferCanvas: false,
                 }
             );
-            L.control.scale().addTo(map_e5c2c6c51b39433c5f381c5df4277652);
+            L.control.scale().addTo(map_d1e3aa3be2f2ca6a5a0cb4dffabb6846);
 
             
 
         
     
-            var tile_layer_821e85dbaee810c1a6c0dc953db6ce0e = L.tileLayer(
+            var tile_layer_9e4726c93b80b82e2eddb188a0cd35e4 = L.tileLayer(
                 &quot;https://a.basemaps.cartocdn.com/light_all/{z}/{x}/{y}{r}.png&quot;,
                 {&quot;attribution&quot;: &quot;\u0026copy; \u003ca href=\&quot;https://www.openstreetmap.org/copyright\&quot;\u003eOpenStreetMap\u003c/a\u003e contributors \u0026copy; \u003ca href=\&quot;https://carto.com/attributions\&quot;\u003eCARTO\u003c/a\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxZoom&quot;: 20, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}
             );
         
     
-            tile_layer_821e85dbaee810c1a6c0dc953db6ce0e.addTo(map_e5c2c6c51b39433c5f381c5df4277652);
+            tile_layer_9e4726c93b80b82e2eddb188a0cd35e4.addTo(map_d1e3aa3be2f2ca6a5a0cb4dffabb6846);
         
     
-            map_e5c2c6c51b39433c5f381c5df4277652.fitBounds(
+            map_d1e3aa3be2f2ca6a5a0cb4dffabb6846.fitBounds(
                 [[-47.28285241600581, 166.42630291457417], [-34.414518716835346, 178.55037360128316]],
                 {}
             );
         
     
-        function geo_json_9219564aae289410228d3b57976957a2_styler(feature) {
+        function geo_json_7b995cf6901a9a90139edde41bb7fd7f_styler(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};
             }
         }
-        function geo_json_9219564aae289410228d3b57976957a2_highlighter(feature) {
+        function geo_json_7b995cf6901a9a90139edde41bb7fd7f_highlighter(feature) {
             switch(feature.id) {
                 default:
                     return {&quot;fillOpacity&quot;: 0.75};
             }
         }
-        function geo_json_9219564aae289410228d3b57976957a2_pointToLayer(feature, latlng) {
+        function geo_json_7b995cf6901a9a90139edde41bb7fd7f_pointToLayer(feature, latlng) {
             var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};
             
-            let style = geo_json_9219564aae289410228d3b57976957a2_styler(feature)
+            let style = geo_json_7b995cf6901a9a90139edde41bb7fd7f_styler(feature)
             Object.assign(opts, style)
             
             return new L.CircleMarker(latlng, opts)
         }
 
-        function geo_json_9219564aae289410228d3b57976957a2_onEachFeature(feature, layer) {
+        function geo_json_7b995cf6901a9a90139edde41bb7fd7f_onEachFeature(feature, layer) {
             layer.on({
                 mouseout: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                            geo_json_9219564aae289410228d3b57976957a2.resetStyle(e.target);
+                            geo_json_7b995cf6901a9a90139edde41bb7fd7f.resetStyle(e.target);
                     }
                 },
                 mouseover: function(e) {
                     if(typeof e.target.setStyle === &quot;function&quot;){
-                        const highlightStyle = geo_json_9219564aae289410228d3b57976957a2_highlighter(e.target.feature)
+                        const highlightStyle = geo_json_7b995cf6901a9a90139edde41bb7fd7f_highlighter(e.target.feature)
                         e.target.setStyle(highlightStyle);
                     }
                 },
             });
         };
-        var geo_json_9219564aae289410228d3b57976957a2 = L.geoJson(null, {
-                onEachFeature: geo_json_9219564aae289410228d3b57976957a2_onEachFeature,
+        var geo_json_7b995cf6901a9a90139edde41bb7fd7f = L.geoJson(null, {
+                onEachFeature: geo_json_7b995cf6901a9a90139edde41bb7fd7f_onEachFeature,
             
-                style: geo_json_9219564aae289410228d3b57976957a2_styler,
-                pointToLayer: geo_json_9219564aae289410228d3b57976957a2_pointToLayer,
+                style: geo_json_7b995cf6901a9a90139edde41bb7fd7f_styler,
+                pointToLayer: geo_json_7b995cf6901a9a90139edde41bb7fd7f_pointToLayer,
         });
 
-        function geo_json_9219564aae289410228d3b57976957a2_add (data) {
-            geo_json_9219564aae289410228d3b57976957a2
+        function geo_json_7b995cf6901a9a90139edde41bb7fd7f_add (data) {
+            geo_json_7b995cf6901a9a90139edde41bb7fd7f
                 .addData(data);
         }
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[174.12449421688015, -41.23625386669239], [174.13306226694434, -41.28532956618908], [174.04844790019618, -41.39310973275859], [174.03353568390273, -41.47634693394752], [174.0720461166079, -41.52395919956433], [174.15042035087913, -41.55943376675295], [174.1569606167201, -41.6476697164152], [174.19829926703636, -41.72323525008164], [174.27503525051202, -41.74288736685971], [174.19675833351914, -41.83560431682098], [174.0487869001062, -41.96810921626457]]], [[[173.9563588838677, -40.6928282325892], [173.96013975138328, -40.7644749495549], [173.90531894826952, -40.85723099987396], [173.81398396713217, -40.93046743316403], [173.7722063841715, -40.86195943251411], [173.8057295342654, -40.80273431644217], [173.93662750115968, -40.74750606645296], [173.9563588838677, -40.6928282325892]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;15&quot;, &quot;properties&quot;: {&quot;Island&quot;: &quot;South&quot;, &quot;Land_area&quot;: 10457.7454853, &quot;Median_income&quot;: 27900, &quot;Name&quot;: &quot;Marlborough&quot;, &quot;Population&quot;: 46200.0, &quot;Sex_ratio&quot;: 0.9577922077922078}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});
 
         
     
-    geo_json_9219564aae289410228d3b57976957a2.bindTooltip(
+    geo_json_7b995cf6901a9a90139edde41bb7fd7f.bindTooltip(
     function(layer){
     let div = L.DomUtil.create('div');
     
@@ -3261,7 +3261,7 @@ <h3 data-number="8.3.5" class="anchored" data-anchor-id="basemaps-1"><span class
     ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});
                      
     
-            geo_json_9219564aae289410228d3b57976957a2.addTo(map_e5c2c6c51b39433c5f381c5df4277652);
+            geo_json_7b995cf6901a9a90139edde41bb7fd7f.addTo(map_d1e3aa3be2f2ca6a5a0cb4dffabb6846);
         
 </script>
 </html>" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen="" webkitallowfullscreen="" mozallowfullscreen=""></iframe></div></div>
@@ -3277,7 +3277,7 @@ <h3 data-number="8.3.5" class="anchored" data-anchor-id="basemaps-1"><span class
 <h3 data-number="8.3.6" class="anchored" data-anchor-id="exporting"><span class="header-section-number">8.3.6</span> Exporting</h3>
 <p>An interactive map can be exported to an HTML file using the <code>.save</code> method of the <code>map</code> object. The HTML file can then be shared with other people, or published on a server and shared through a URL<a href="#fn6" class="footnote-ref" id="fnref6" role="doc-noteref"><sup>6</sup></a>. A good free option for publishing a web map is through GitHub Pages.</p>
 <p>For example, here is how we can export the map shown in <a href="#fig-explore-layers-controls" class="quarto-xref">Figure&nbsp;<span>8.26</span></a>, to a file named <code>map.html</code>.</p>
-<div id="35cf5a58" class="cell" data-execution_count="61">
+<div id="98c52fe8" class="cell" data-execution_count="61">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>m <span class="op">=</span> nz.explore(name<span class="op">=</span><span class="st">'Polygons (adm. areas)'</span>)</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>nz_height.explore(m<span class="op">=</span>m, color<span class="op">=</span><span class="st">'red'</span>, name<span class="op">=</span><span class="st">'Points (elevation)'</span>)</span>
 <span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>folium.LayerControl(collapsed<span class="op">=</span><span class="va">False</span>).add_to(m)</span>
diff --git a/08-mapping_files/figure-html/fig-basemap-output-1.png b/08-mapping_files/figure-html/fig-basemap-output-1.png
index 20703828..70a65cf3 100644
Binary files a/08-mapping_files/figure-html/fig-basemap-output-1.png and b/08-mapping_files/figure-html/fig-basemap-output-1.png differ
diff --git a/preface.html b/preface.html
index efd5bdae..58d56ffd 100644
--- a/preface.html
+++ b/preface.html
@@ -374,13 +374,13 @@ <h2 class="anchored" data-anchor-id="code-and-sample-data">Code and sample data<
 <section id="software" class="level2">
 <h2 class="anchored" data-anchor-id="software">Software</h2>
 <p>Python version used when rendering the book:</p>
-<div id="00a7e8ec" class="cell" data-execution_count="1">
+<div id="741c318b" class="cell" data-execution_count="1">
 <div class="cell-output cell-output-stdout">
 <pre><code>3.11.4 (main, Aug 16 2023, 05:31:52) [GCC 10.2.1 20210110]</code></pre>
 </div>
 </div>
 <p>Versions of the main packages used in the book:</p>
-<div id="b5d997b0" class="cell" data-execution_count="2">
+<div id="12696b09" class="cell" data-execution_count="2">
 <div class="cell-output cell-output-stdout">
 <pre><code>numpy==2.0.1
 pandas==2.2.2
diff --git a/search.json b/search.json
index 8b3c98fa..38658c73 100644
--- a/search.json
+++ b/search.json
@@ -224,7 +224,7 @@
     "href": "03-spatial-operations.html#sec-spatial-ras",
     "title": "3  Spatial data operations",
     "section": "3.3 Spatial operations on raster data",
-    "text": "3.3 Spatial operations on raster data\nThis section builds on Section 2.3, which highlights various basic methods for manipulating raster datasets, to demonstrate more advanced and explicitly spatial raster operations, and uses the elev.tif and grain.tif rasters manually created in Section 1.3.2.\n\n3.3.1 Spatial subsetting\nThe previous chapter (and especially Section 2.3) demonstrated how to retrieve values associated with specific row and column combinations from a raster. Raster values can also be extracted by location (coordinates) and other spatial objects. To use coordinates for subsetting, we can use the .sample method of a rasterio file connection object, combined with a list of coordinate tuples. The method is demonstrated below to find the value of the cell that covers a point located at coordinates of (0.1,0.1) in elev. The returned object is a generator. The rationale for returning a generator, rather than a list, is memory efficiency. The number of sampled points may be huge, in which case we would want to generate the values one at a time rather than all at once.\n\nsrc_elev.sample([(0.1, 0.1)])\n\n&lt;generator object sample_gen at 0x7f78f0147290&gt;\n\n\n\n\n\n\n\n\nNote\n\n\n\nThe technical terms iterable, iterator, and generator in Python may be confusing, so here is a short summary, ordered from most general to most specific:\n\nAn iterable is any object that we can iterate on, such as using a for loop. For example, a list is iterable.\nAn iterator is an object that represents a stream of data, which we can go over, each time getting the next element using next. Iterators are also iterable, meaning that you can over them in a loop, but they are stateful (e.g., they remember which item was obtained using next), meaning that you can go over them just once.\nA generator is a function that returns an iterator. For example, the .sample method in the above example is a generator. The rasterio package makes use of generators in some of its functions, as we will see later on (Section 5.5.1).\n\n\n\nIn case we nevertheless want all values at once, such as when the number of points is small, we can force the generatrion of all values from a generator at once, using list. Since there was just one point, the result is one extracted value, in this case 16.\n\nlist(src_elev.sample([(0.1, 0.1)]))\n\n[array([16], dtype=uint8)]\n\n\nWe can use the same technique to extract the values of multiple points at once. For example, here we extract the raster values at two points, (0.1,0.1) and (1.1,1.1). The resulting values are 16 and 6.\n\nlist(src_elev.sample([(0.1, 0.1), (1.1, 1.1)]))\n\n[array([16], dtype=uint8), array([6], dtype=uint8)]\n\n\nThe location of the two sample points on top of the elev.tif raster is illustrated in Figure 3.16.\n\nfig, ax = plt.subplots()\nrasterio.plot.show(src_elev, ax=ax)\ngpd.GeoSeries([shapely.Point(0.1, 0.1)]) \\\n    .plot(color='black', edgecolor='white', markersize=50, ax=ax)\ngpd.GeoSeries([shapely.Point(1.1, 1.1)]) \\\n    .plot(color='black', edgecolor='white', markersize=50, ax=ax);\n\n\n\n\n\n\n\nFigure 3.16: The elev.tif raster, and two points where we extract its values\n\n\n\n\n\n\n\n\n\n\n\nNote\n\n\n\nWe elaborate on the plotting technique used to display the points and the raster in Section 8.2.5. We will also introduce a more user-friendly and general method to extract raster values to points, using the rasterstats package, in Section 5.3.1.\n\n\nAnother common use case of spatial subsetting is using a boolean mask, based on another raster with the same extent and resolution, or the original one, as illustrated in Figure 3.17. To do that, we erase the values in the array of one raster, according to another corresponding mask raster. For example, let’s read (Section 1.3.1) the elev.tif raster values into an array named elev (Figure 3.17 (a)).\n\nelev = src_elev.read(1)\nelev\n\narray([[ 1,  2,  3,  4,  5,  6],\n       [ 7,  8,  9, 10, 11, 12],\n       [13, 14, 15, 16, 17, 18],\n       [19, 20, 21, 22, 23, 24],\n       [25, 26, 27, 28, 29, 30],\n       [31, 32, 33, 34, 35, 36]], dtype=uint8)\n\n\nand create a corresponding random boolean mask named mask (Figure 3.17 (b)), of the same shape as elev.tif with values randomly assigned to True and False.\n\nnp.random.seed(1)\nmask = np.random.choice([True, False], src_elev.shape)\nmask\n\narray([[False, False,  True,  True, False, False],\n       [False, False, False,  True,  True, False],\n       [ True, False, False,  True,  True, False],\n       [ True,  True,  True, False,  True,  True],\n       [False,  True,  True,  True, False,  True],\n       [ True,  True, False, False, False, False]])\n\n\nNext, suppose that we want to keep only those values of elev which are False in mask (i.e., they are not masked). In other words, we want to mask elev with mask. The result will be stored in a copy named masked_elev (Figure 3.17 (c)). In the case of elev.tif, to be able to store np.nan in the array of values, we also need to convert it to float (see Section 2.3.2). Afterwards, masking is a matter of assigning np.nan into a subset defined by the mask, using the ‘boolean array indexing’ syntax of numpy.\n\nmasked_elev = elev.copy()\nmasked_elev = masked_elev.astype('float64')\nmasked_elev[mask] = np.nan\nmasked_elev\n\narray([[ 1.,  2., nan, nan,  5.,  6.],\n       [ 7.,  8.,  9., nan, nan, 12.],\n       [nan, 14., 15., nan, nan, 18.],\n       [nan, nan, nan, 22., nan, nan],\n       [25., nan, nan, nan, 29., nan],\n       [nan, nan, 33., 34., 35., 36.]])\n\n\nFigure 3.17 shows the original elev raster, the mask raster, and the resulting masked_elev raster.\nrasterio.plot.show(elev);\nrasterio.plot.show(mask);\nrasterio.plot.show(masked_elev);\n\n\n\n\n\n\n\n\n\n\n\n(a) Original raster\n\n\n\n\n\n\n\n\n\n\n\n(b) Raster mask\n\n\n\n\n\n\n\n\n\n\n\n(c) Output masked raster\n\n\n\n\n\n\n\nFigure 3.17: Subsetting raster values using a boolean mask\n\n\n\nThe mask can be created from the array itself, using condition(s). That way, we can replace some values (e.g., values assumed to be wrong) with np.nan, such as in the following example.\n\nelev2 = elev.copy()\nelev2 = elev2.astype('float64')\nelev2[elev2 &lt; 20] = np.nan\nelev2\n\narray([[nan, nan, nan, nan, nan, nan],\n       [nan, nan, nan, nan, nan, nan],\n       [nan, nan, nan, nan, nan, nan],\n       [nan, 20., 21., 22., 23., 24.],\n       [25., 26., 27., 28., 29., 30.],\n       [31., 32., 33., 34., 35., 36.]])\n\n\nThis technique is also used to reclassify raster values (see Section 3.3.3).\n\n\n3.3.2 Map algebra\nThe term ‘map algebra’ was coined in the late 1970s to describe a ‘set of conventions, capabilities, and techniques’ for the analysis of geographic raster and (although less prominently) vector data (Tomlin 1994). In this context, we define map algebra more narrowly, as operations that modify or summarize raster cell values, with reference to surrounding cells, zones, or statistical functions that apply to every cell.\nMap algebra operations tend to be fast, because raster datasets only implicitly store coordinates, hence the old adage ‘raster is faster but vector is corrector’. The location of cells in raster datasets can be calculated by using its matrix position and the resolution and origin of the dataset (stored in the raster metadata, Section 1.3.1). For the processing, however, the geographic position of a cell is barely relevant as long as we make sure that the cell position is still the same after the processing. Additionally, if two or more raster datasets share the same extent, projection, and resolution, one could treat them as matrices for the processing.\nMap algebra (or cartographic modeling with raster data) divides raster operations into four subclasses (Tomlin 1990), with each working on one or several grids simultaneously:\n\nLocal or per-cell operations (Section 3.3.3)\nFocal or neighborhood operations. Most often the output cell value is the result of a \\(3 \\times 3\\) input cell block (Section 3.3.4)\nZonal operations are similar to focal operations, but the surrounding pixel grid on which new values are computed can have irregular sizes and shapes (Section 3.3.5)\nGlobal or per-raster operations; that means the output cell derives its value potentially from one or several entire rasters (Section 3.3.6)\n\nThis typology classifies map algebra operations by the number of cells used for each pixel processing step and the type of output. For the sake of completeness, we should mention that raster operations can also be classified by disciplines such as terrain, hydrological analysis, or image classification. The following sections explain how each type of map algebra operations can be used, with reference to worked examples.\n\n\n3.3.3 Local operations\nLocal operations comprise all cell-by-cell operations in one or several layers. Raster algebra is a classical use case of local operations—this includes adding or subtracting values from a raster, squaring,, and multiplying rasters. Raster algebra also allows logical operations such as finding all raster cells that are greater than a specific value (e.g., 5 in our example below). Local operations are applied using the numpy array operations syntax, as demonstrated below.\nFirst, let’s take the array of elev.tif raster values, which we already read earlier (Section 3.3.1).\n\nelev\n\narray([[ 1,  2,  3,  4,  5,  6],\n       [ 7,  8,  9, 10, 11, 12],\n       [13, 14, 15, 16, 17, 18],\n       [19, 20, 21, 22, 23, 24],\n       [25, 26, 27, 28, 29, 30],\n       [31, 32, 33, 34, 35, 36]], dtype=uint8)\n\n\nNow, any element-wise array operation can be applied using numpy arithmetic or conditional operators and functions, comprising local raster operations in spatial analysis terminology. For example, elev + elev adds the values of elev to itself, resulting in a raster with double values.\n\nelev + elev\n\narray([[ 2,  4,  6,  8, 10, 12],\n       [14, 16, 18, 20, 22, 24],\n       [26, 28, 30, 32, 34, 36],\n       [38, 40, 42, 44, 46, 48],\n       [50, 52, 54, 56, 58, 60],\n       [62, 64, 66, 68, 70, 72]], dtype=uint8)\n\n\nNote that some functions and operators automatically change the data type to accommodate the resulting values, while other operators do not, potentially resulting in overflow (i.e., incorrect values for results beyond the data type range, such as trying to accommodate values above 255 in an int8 array). For example, elev**2 (elev squared) results in overflow. Since the ** operator does not automatically change the data type, leaving it as int8, the resulting array has incorrect values for 16**2, 17**2, etc., which are above 255 and therefore cannot be accommodated.\n\nelev**2\n\narray([[  1,   4,   9,  16,  25,  36],\n       [ 49,  64,  81, 100, 121, 144],\n       [169, 196, 225,   0,  33,  68],\n       [105, 144, 185, 228,  17,  64],\n       [113, 164, 217,  16,  73, 132],\n       [193,   0,  65, 132, 201,  16]], dtype=uint8)\n\n\nTo avoid this situation, we can, for instance, transform elev to the standard int64 data type, using .astype before applying the ** operator. That way, all results, up to 36**2 (1296), can be easily accommodated, since the int64 data type supports values up to 9223372036854775807 (Table 7.2).\n\nelev.astype(int)**2\n\narray([[   1,    4,    9,   16,   25,   36],\n       [  49,   64,   81,  100,  121,  144],\n       [ 169,  196,  225,  256,  289,  324],\n       [ 361,  400,  441,  484,  529,  576],\n       [ 625,  676,  729,  784,  841,  900],\n       [ 961, 1024, 1089, 1156, 1225, 1296]])\n\n\nNow we get correct results.\nFigure 3.18 demonstrates the result of the last two examples (elev+elev and elev.astype(int)**2), and two other ones (np.log(elev) and elev&gt;5).\nrasterio.plot.show(elev + elev, cmap='Oranges');\nrasterio.plot.show(elev.astype(int)**2, cmap='Oranges');\nrasterio.plot.show(np.log(elev), cmap='Oranges');\nrasterio.plot.show(elev &gt; 5, cmap='Oranges');\n\n\n\n\n\n\n\n\n\n\n\n(a) elev+elev\n\n\n\n\n\n\n\n\n\n\n\n(b) elev.astype(int)**2\n\n\n\n\n\n\n\n\n\n\n\n(c) np.log(elev)\n\n\n\n\n\n\n\n\n\n\n\n(d) elev&gt;5\n\n\n\n\n\n\n\nFigure 3.18: Examples of different local operations of the elev raster object: adding two rasters, squaring, applying logarithmic transformation, and performing a logical operation.\n\n\n\nAnother good example of local operations is the classification of intervals of numeric values into groups such as grouping a digital elevation model into low (class 1), middle (class 2) and high (class 3) elevations. Here, the raster values in the ranges 0–12, 12–24, and 24–36 are reclassified to take values 1, 2, and 3, respectively.\n\nrecl = elev.copy()\nrecl[(elev &gt; 0)  & (elev &lt;= 12)] = 1\nrecl[(elev &gt; 12) & (elev &lt;= 24)] = 2\nrecl[(elev &gt; 24) & (elev &lt;= 36)] = 3\n\nFigure 3.19 compares the original elev raster with the reclassified recl one.\nrasterio.plot.show(elev, cmap='Oranges');\nrasterio.plot.show(recl, cmap='Oranges');\n\n\n\n\n\n\n\n\n\n\n\n(a) Original\n\n\n\n\n\n\n\n\n\n\n\n(b) Reclassified\n\n\n\n\n\n\n\nFigure 3.19: Reclassifying a continuous raster into three categories.\n\n\n\nThe calculation of the Normalized Difference Vegetation Index (NDVI)2 is a well-known local (pixel-by-pixel) raster operation. It returns a raster with values between -1 and 1; positive values indicate the presence of living plants (mostly &gt; 0.2). NDVI is calculated from red and near-infrared (NIR) bands of remotely sensed imagery, typically from satellite systems such as Landsat or Sentinel-2. Vegetation absorbs light heavily in the visible light spectrum, and especially in the red channel, while reflecting NIR light, which is emulated in the NVDI formula (Equation 3.1),\n\\[\nNDVI=\\frac{NIR-Red} {NIR+Red}\n\\tag{3.1}\\]\n, where \\(NIR\\) is the near-infrared band and \\(Red\\) is the red band.\nLet’s calculate NDVI for the multispectral Landsat satellite file (landsat.tif) of the Zion National Park. The file landsat.tif contains surface reflectance values (range 0-1) in the blue, green, red, and near-infrared (NIR) bands. We start by reading the file and extracting the NIR and red bands, which are the fourth and third bands, respectively. Next, we apply the formula to calculate the NDVI values.\n\nlandsat = src_landsat.read()\nnir = landsat[3]\nred = landsat[2]\nndvi = (nir-red)/(nir+red)\n\nWhen plotting an RGB image using the rasterio.plot.show function, the function assumes that values are in the range [0,1] for floats, or [0,255] for integers (otherwise clipped) and the order of bands is RGB. To prepare the multi-band raster for rasterio.plot.show, we, therefore, reverse the order of the first three bands (to go from B-G-R-NIR to R-G-B), using the [:3] slice to select the first three bands and then the [::-1] slice to reverse the bands order, and divide by the raster maximum to set the maximum value to 1.\n\nlandsat_rgb = landsat[:3][::-1] / landsat.max()\n\n\n\n\n\n\n\nNote\n\n\n\nPython slicing notation, which numpy, pandas and geopandas also follow, is object[start:stop:step]. The default is to start from the beginning, go to the end, and use steps of 1. Otherwise, start is inclusive and end is exclusive, whereas negative step values imply going backwards starting from the end. Also, always keep in mind that Python indices start from 0. When subsetting two- or three-dimensional objects, indices for each dimension are separated by commas, where either index can be set to : meaning ‘all values’. The last dimensions can also be omitted implying :, e.g., to subset the first three bands from a three-dimensional array a we can use either a[:3,:,:] or a[:3].\nIn the above example:\n\nThe slicing expression [:3] therefore means layers 0, 1, 2 (up to 3, exclusive)\nThe slicing expression [::-1] therefore means all (three) bands in reverse order\n\n\n\nFigure 3.20 shows the RGB image and the NDVI values calculated for the Landsat satellite image of the Zion National Park.\nrasterio.plot.show(landsat_rgb, cmap='RdYlGn');\nrasterio.plot.show(ndvi, cmap='Greens');\n\n\n\n\n\n\n\n\n\n\n\n(a) RGB image\n\n\n\n\n\n\n\n\n\n\n\n(b) NDVI\n\n\n\n\n\n\n\nFigure 3.20: RGB image and NDVI values calculated for the Landsat satellite image of the Zion National Park\n\n\n\n\n\n3.3.4 Focal operations\nWhile local functions operate on one cell at a time (though possibly from multiple layers), focal operations take into account a central (focal) cell and its neighbors. The neighborhood (also named kernel, filter, or moving window) under consideration is typically of \\(3 \\times 3\\) cells (that is, the central cell and its eight surrounding neighbors), but can take on any other (not necessarily rectangular) shape as defined by the user. A focal operation applies an aggregation function to all cells within the specified neighborhood, uses the corresponding output as the new value for the central cell, and moves on to the next central cell (Figure 3.21). Other names for this operation are spatial filtering and convolution (Burrough, McDonnell, and Lloyd 2015).\n\n\n\n\n\n\nFigure 3.21: Input raster (left) and resulting output raster (right) due to a focal operation—finding the minimum value in \\(3 \\times 3\\) moving windows.\n\n\n\nIn Python, the scipy.ndimage (Virtanen et al. 2020) package has a comprehensive collection of functions to perform filtering of numpy arrays, such as:\n\nscipy.ndimage.minimum_filter,\nscipy.ndimage.maximum_filter,\nscipy.ndimage.uniform_filter (i.e., mean filter),\nscipy.ndimage.median_filter, etc.\n\nIn this group of functions, we define the shape of the moving window with either one of size—a single number (e.g., 3), or tuple (e.g., (3,3)), implying a filter of those dimensions, or footprint—a boolean array, representing both the window shape and the identity of elements being included.\nIn addition to specific built-in filters, convolve—applies the sum function after multiplying by a custom weights array, and generic_filter—makes it possible to pass any custom function, where the user can specify any type of custom window-based calculation.\nFor example, here we apply the minimum filter with window size of 3 on elev. As a result, we now have a new array elev_min, where each value is the minimum in the corresponding \\(3 \\times 3\\) neighborhood in elev.\n\nelev_min = scipy.ndimage.minimum_filter(elev, size=3)\nelev_min\n\narray([[ 1,  1,  2,  3,  4,  5],\n       [ 1,  1,  2,  3,  4,  5],\n       [ 7,  7,  8,  9, 10, 11],\n       [13, 13, 14, 15, 16, 17],\n       [19, 19, 20, 21, 22, 23],\n       [25, 25, 26, 27, 28, 29]], dtype=uint8)\n\n\nSpecial care should be given to the edge pixels – how should they be calculated? The scipy.ndimage filtering functions give several options through the mode parameter (see the documentation of any filtering function, such as scipy.ndimage.median_filter, for the definition of each mode): reflect (the default), constant, nearest, mirror, wrap. Sometimes artificially extending raster edges is considered unsuitable. In other words, we may wish the resulting raster to contain pixel values with ‘complete’ windows only, for example, to have a uniform sample size or because values in all directions matter (such as in topographic calculations). There is no specific option not to extend edges in scipy.ndimage. However, to get the same effect, the edges of the filtered array can be assigned with np.nan, in a number of rows and columns according to filter size. For example, when using a filter of size=3, the outermost ‘layer’ of pixels may be assigned with np.nan, reflecting the fact that these pixels have incomplete \\(3 \\times 3\\) neighborhoods (Figure 3.21):\n\nelev_min = elev_min.astype(float)\nelev_min[:, [0, -1]] = np.nan\nelev_min[[0, -1], :] = np.nan\nelev_min\n\narray([[nan, nan, nan, nan, nan, nan],\n       [nan,  1.,  2.,  3.,  4., nan],\n       [nan,  7.,  8.,  9., 10., nan],\n       [nan, 13., 14., 15., 16., nan],\n       [nan, 19., 20., 21., 22., nan],\n       [nan, nan, nan, nan, nan, nan]])\n\n\nWe can quickly check if the output meets our expectations. In our example, the minimum value has to be always the upper left corner of the moving window (remember we have created the input raster by row-wise incrementing the cell values by one starting at the upper left corner).\nFocal functions or filters play a dominant role in image processing. For example, low-pass or smoothing filters use the mean function to remove extremes. By contrast, high-pass filters, often created with custom neighborhood weights, accentuate features.\nIn the case of categorical data, we can replace the mean with the mode, i.e., the most common value. To demonstrate applying a mode filter, let’s read the small sample categorical raster grain.tif.\n\ngrain = src_grain.read(1)\ngrain\n\narray([[1, 0, 1, 2, 2, 2],\n       [0, 2, 0, 0, 2, 1],\n       [0, 2, 2, 0, 0, 2],\n       [0, 0, 1, 1, 1, 1],\n       [1, 1, 1, 2, 1, 1],\n       [2, 1, 2, 2, 0, 2]], dtype=uint8)\n\n\nThere is no built-in filter function for a mode filter in scipy.ndimage, but we can use the scipy.ndimage.generic_filter function along with a custom filtering function, internally utilizing scipy.stats.mode.\n\ngrain_mode = scipy.ndimage.generic_filter(\n    grain, \n    lambda x: scipy.stats.mode(x.flatten())[0], \n    size=3\n)\ngrain_mode = grain_mode.astype(float)\ngrain_mode[:, [0, -1]] = np.nan\ngrain_mode[[0, -1], :] = np.nan\ngrain_mode\n\narray([[nan, nan, nan, nan, nan, nan],\n       [nan,  0.,  0.,  0.,  2., nan],\n       [nan,  0.,  0.,  0.,  1., nan],\n       [nan,  1.,  1.,  1.,  1., nan],\n       [nan,  1.,  1.,  1.,  1., nan],\n       [nan, nan, nan, nan, nan, nan]])\n\n\n\n\n\n\n\n\nNote\n\n\n\nscipy.stats.mode is a function to summarize array values, returning the mode (most common value). It is analogous to numpy summary functions and methods, such as .mean or .max. numpy itself does not provide the mode function, however, which is why we use scipy for that.\n\n\nTerrain processing is another important application of focal operations. Such functions are provided by multiple Python packages, including the general purpose xarray package, and more specialized packages such as richdem and pysheds. Useful terrain metrics include:\n\nSlope, measured in units of percent, degreees, or radians (Horn 1981)\nAspect, meaning each cell’s downward slope direction (Horn 1981)\nSlope curvature, including ‘planform’ and ‘profile’ curvature (Zevenbergen and Thorne 1987)\n\nFor example, each of these, and other, terrain metrics can be computed with the richdem package.\n\n\n\n\n\n\nNote\n\n\n\nTerrain metrics are essentially focal filters with customized functions. Using scipy.ndimage.generic_filter, along with such custom functions, is an option for those who would like to calculate terrain metric through coding by hand and/or limiting their code dependencies. For example, the How Aspect works3 and How Slope works4 pages from the ArcGIS Pro documentation provide explanations and formulas of the required functions for aspect and slope metrics (Figure 3.22), respectively, which can be translated to numpy-based functions to be used in scipy.ndimage.generic_filter to calculate those metrics.\n\n\nAnother extremely fast, memory-efficient, and concise, alternative, is to the use the GDAL program called gdaldem. gdaldem can be used to calculate slope, aspect, and other terrain metrics through a single command, accepting an input file path and exporting the result to a new file. This is our first example in the book where we demonstrate a situation where it may be worthwhile to leave the Python environment, and utilize a GDAL program directly, rather than through their wrappers (such as rasterio and other Python packages), whether to access a computational algorithm not easily accessible in a Python package, or for GDAL’s memory-efficiency and speed benefits.\n\n\n\n\n\n\nNote\n\n\n\nGDAL contains a collection of over 40 programs, mostly aimed at raster processing. These include programs for fundamental operations, such as:\n\ngdal_translate—convert between raster file formats\ngdalwarp—raster reprojection\ngdal_rasterize—rasterize vector features\ngdal_merge.py—raster mosaic\n\nIn this book, we use rasterio for the above-mentioned operations, although the GDAL programs are a good alternative for those who are more comfortable with the command line. However, we do use two GDAL programs for tasks that are lacking in rasterio and not well-implemented in other Python packages: gdaldem (this section), and gdal_contour (Section 5.5.3).\n\n\nGDAL, along with all of its programs should be available in your Python environment, since GDAL is a dependency of rasterio. The following example, which should be run from the command line, takes the srtm_32612.tif raster (which we are going to create in Section 6.8, therefore it is in the 'output' directory), calculates slope (in decimal degrees, between 0 and 90), and exports the result to a new file srtm_32612_slope.tif. Note that the arguments of gdaldem are the metric name (slope), then the input file path, and finally the output file path.\n\nos.system('gdaldem slope output/srtm_32612.tif output/srtm_32612_slope.tif')\n\nHere we ran the gdaldem command through os.system, in order to remain in the Python environment, even though we are calling an external program. Alternatively, you can run the standalone command in the command line interface you are using, such as the Anaconda Prompt:\ngdaldem slope output/srtm_32612.tif output/srtm_32612_slope.tif\nReplacing the metric name, we can calculate other terrain properties. For example, here is how we can calculate an aspect raster srtm_32612_aspect.tif, also in degrees (between 0 and 360).\n\nos.system('gdaldem aspect output/srtm_32612.tif output/srtm_32612_aspect.tif')\n\nFigure 3.22 shows the results, using our more familiar plotting methods from rasterio. The code section is relatively long due to the workaround to create a color key (see Section 8.2.3) and removing ‘No Data’ flag values from the arrays so that the color key does not include them. Also note that we are using one of matplotlib’s the cyclic color scales ('twilight') when plotting aspect (Figure 3.22 (c)).\n# Input DEM\nsrc_srtm = rasterio.open('output/srtm_32612.tif')\nsrtm = src_srtm.read(1).astype(float)\nsrtm[srtm == src_srtm.nodata] = np.nan\nfig, ax = plt.subplots()\nrasterio.plot.show(src_srtm, cmap='Spectral_r', ax=ax)\nfig.colorbar(ax.imshow(srtm, cmap='Spectral_r'), ax=ax);\n# Slope\nsrc_srtm_slope = rasterio.open('output/srtm_32612_slope.tif')\nsrtm_slope = src_srtm_slope.read(1)\nsrtm_slope[srtm_slope == src_srtm_slope.nodata] = np.nan\nfig, ax = plt.subplots()\nrasterio.plot.show(src_srtm_slope, cmap='Spectral_r', ax=ax)\nfig.colorbar(ax.imshow(srtm_slope, cmap='Spectral_r'), ax=ax);\n# Aspect\nsrc_srtm_aspect = rasterio.open('output/srtm_32612_aspect.tif')\nsrtm_aspect = src_srtm_aspect.read(1)\nsrtm_aspect[srtm_aspect == src_srtm_aspect.nodata] = np.nan\nfig, ax = plt.subplots()\nrasterio.plot.show(src_srtm_aspect, cmap='twilight', ax=ax)\nfig.colorbar(ax.imshow(srtm_aspect, cmap='twilight'), ax=ax);\n\n\n\n\n\n\n\n\n\n\n\n(a) Input DEM\n\n\n\n\n\n\n\n\n\n\n\n(b) Slope (degrees)\n\n\n\n\n\n\n\n\n\n\n\n(c) Aspect (degrees)\n\n\n\n\n\n\n\nFigure 3.22: Slope and aspect calculation from a DEM\n\n\n\n\n\n3.3.5 Zonal operations\nJust like focal operations, zonal operations apply an aggregation function to multiple raster cells. However, a second raster, usually with categorical values, defines the zonal filters (or ‘zones’) in the case of zonal operations, as opposed to a predefined neighborhood window in the case of focal operation presented in the previous section. Consequently, raster cells defining the zonal filter do not necessarily have to be neighbors. Our grain.tif raster is a good example, as illustrated in Figure 1.24: different grain sizes are spread irregularly throughout the raster. Finally, the result of a zonal operation is a summary table grouped by zone, which is why this operation is also known as zonal statistics in the GIS world. This is in contrast to focal operations (Section 3.3.4) which return a raster object.\nTo demonstrate, let’s get back to the grain.tif and elev.tif rasters. To calculate zonal statistics, we use the arrays with raster values, which we already imported earlier. Our intention is to calculate the average (or any other summary function, for that matter) of elevation in each zone defined by grain values. To do that, first we first obtain the unique values defining the zones using np.unique.\n\nnp.unique(grain)\n\narray([0, 1, 2], dtype=uint8)\n\n\nNow, we can use dictionary comprehension (see note below) to split the elev array into separate one-dimensional arrays with values per grain group, with keys being the unique grain values.\n\nz = {i: elev[grain == i] for i in np.unique(grain)}\nz\n\n{np.uint8(0): array([ 2,  7,  9, 10, 13, 16, 17, 19, 20, 35], dtype=uint8),\n np.uint8(1): array([ 1,  3, 12, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32], dtype=uint8),\n np.uint8(2): array([ 4,  5,  6,  8, 11, 14, 15, 18, 28, 31, 33, 34, 36], dtype=uint8)}\n\n\n\n\n\n\n\n\nNote\n\n\n\nList comprehension and dictionary comprehension are concise ways to create a list or a dict, respectively, from an iterable object. Both are, conceptually, a concise syntax to replace for loops where we iterate over an object and return a same-length object with the results. Here are minimal examples of list and dictionary comprehension, respectively, to demonstrate the idea:\n\n[i**2 for i in [2,4,6]]—Returns [4,16,36]\n{i: i**2 for i in [2,4,6]}—Returns {2:4, 4:16, 6:36}\n\nList comprehension is more commonly encountered in practice. We use it in Section 4.2.6, Section 5.4.2, Section 5.5.1, and Section 5.6. Dictionary comprehension is only used in one place in the book (this section).\n\n\nAt this stage, we can expand the dictionary comprehension expression to calculate the mean elevation associated with each grain size class. Namely, instead of placing the elevation values (elev[grain==i]) into the dictionary values, we place their (rounded) mean (elev[grain==i].mean().round(1)).\n\nz = {i: elev[grain == i].mean().round(1) for i in np.unique(grain)}\nz\n\n{np.uint8(0): np.float64(14.8),\n np.uint8(1): np.float64(21.2),\n np.uint8(2): np.float64(18.7)}\n\n\nThis returns the statistics for each category, here the mean elevation for each grain size class. For example, the mean elevation in pixels characterized by grain size 0 is 14.8, and so on.\n\n\n3.3.6 Global operations and distances\nGlobal operations are a special case of zonal operations with the entire raster dataset representing a single zone. The most common global operations are descriptive statistics for the entire raster dataset such as the minimum or maximum—we already discussed those in Section 2.3.2.\nAside from that, global operations are also useful for the computation of distance and weight rasters. In the first case, one can calculate the distance from each cell to specific target cells or vector geometries. For example, one might want to compute the distance to the nearest coast (see Section 5.6). We might also want to consider topography, that means, we are not only interested in the pure distance but would like also to avoid the crossing of mountain ranges when going to the coast. To do so, we can weight the distance with elevation so that each additional altitudinal meter ‘prolongs’ the Euclidean distance (this is beyond the scope of the book). Visibility and viewshed computations also belong to the family of global operations (also beyond the scope of the book).\n\n\n3.3.7 Map algebra counterparts in vector processing\nMany map algebra operations have a counterpart in vector processing (Liu and Mason 2009). Computing a distance raster (global operation) while only considering a maximum distance (logical focal operation) is the equivalent of a vector buffer operation (Section 4.2.3). Reclassifying raster data (either local or zonal function depending on the input) is equivalent to dissolving vector data (Section 4.2.7). Overlaying two rasters (local operation), where one contains ‘No Data’ values representing a mask, is similar to vector clipping (Section Section 4.2.5). Quite similar to spatial clipping is intersecting two layers (Section 3.2.1, Section 3.2.6). The difference is that these two layers (vector or raster) simply share an overlapping area. However, be careful with the wording. Sometimes the same words have slightly different meanings for raster and vector data models. While aggregating polygon geometries means dissolving boundaries, for raster data geometries it means increasing cell sizes and thereby reducing spatial resolution. Zonal operations dissolve the cells of one raster in accordance with the zones (categories) of another raster dataset using an aggregating function.\n\n\n3.3.8 Merging rasters\nSuppose we would like to compute the NDVI (see Section 3.3.3), and additionally want to compute terrain attributes from elevation data for observations within a study area. Such computations rely on remotely sensed information. The corresponding source imagery is often divided into scenes covering a specific spatial extent (i.e., tiles), and frequently, a study area covers more than one scene. Then, we would need to merge (also known as mosaic) the scenes covering our study area. In case when all scenes are aligned (i.e., share the same origin and resolution), this can be thought of as simply gluing them into one big raster; otherwise, all scenes need to be resampled (see Section 4.3.3) to the same grid (e.g., the one defined by the first scene).\nFor example, let’s merge digital elevation data from two SRTM elevation tiles, for Austria ('aut.tif') and Switzerland ('ch.tif'). Merging can be done using function rasterio.merge.merge, which accepts a list of raster file connections, and returns the new ndarray and the corresponding transform object, representing the resulting mosaic.\n\nsrc_1 = rasterio.open('data/aut.tif')\nsrc_2 = rasterio.open('data/ch.tif')\nout_image, out_transform = rasterio.merge.merge([src_1, src_2])\n\n\n\n\n\n\n\nNote\n\n\n\nSome Python packages (such as rasterio) are split into several so-called sub-modules. The sub-modules are installed collectively when installing the main package. However, each sub-module needs to be loaded separately to be able to use its functions. For example, the rasterio.merge.merge function (see last code block) comes from the rasterio.merge sub-module of rasterio. Loading rasterio with import rasterio does not expose the rasterio.merge.merge function; instead, we have to load rasterio.merge with import rasterio.merge, and only then use rasterio.merge.merge.\nAlso check out the first code block in this chapter, where we load rasterio as well as three sub-modules: rasterio.plot, rasterio.merge, and rasterio.features.\n\n\nFigure 3.23 shows both inputs and the resulting mosaic.\nrasterio.plot.show(src_1);\nrasterio.plot.show(src_2);\nrasterio.plot.show(out_image, transform=out_transform);\n\n\n\n\n\n\n\n\n\n\n\n(a) aut.tif\n\n\n\n\n\n\n\n\n\n\n\n(b) ch.tif\n\n\n\n\n\n\n\n\n\n\n\n(c) Mosaic (aut.tif+ch.tif)\n\n\n\n\n\n\n\nFigure 3.23: Raster merging\n\n\n\nBy default in rasterio.merge.merge, areas of overlap retain the value of the first raster (method='first'). Other possible methods are:\n\n'last'—Value of the last raster\n'min'—Minimum value\n'max'—Maximum value\n\nWhen dealing with non-overlapping tiles, such as aut.tif and ch.tif (above), the method argument has no practical effect. However, it becomes relevant when we want to combine spectral imagery from scenes that were taken on different dates. The above four options for method do not cover the commonly required scenario when we would like to compute the mean value—for example to calculate a seasonal average NDVI image from a set of partially overlapping satellite images (such as Landsat). An alternative workflow to rasterio.merge.merge, for calculating a mosaic as well as averaging any overlaps, is to go through two steps:\n\nResampling all scenes into a common ‘global’ grid (Section 4.3.3), thereby producing a series of matching rasters (with the area surrounding each scene set as ‘No Data’)\nAveraging the rasters through raster algebra (Section 3.3.3), using np.mean(m,axis=0) or np.nanmean(m,axis=0) (depending whether we prefer to ignore ‘No Data’ or not), where m is the multi-band array, which would return a single-band array of averages\n\n\n\n\n\n\nBurrough, P. A., Rachael McDonnell, and Christopher D. Lloyd. 2015. Principles of Geographical Information Systems. Third. Oxford, New York: Oxford University Press.\n\n\nDieck, Tammo tom. 2008. Algebraic Topology. EMS Textbooks in Mathematics. Zürich: European Mathematical Society. https://www.maths.ed.ac.uk/~v1ranick/papers/diecktop.pdf.\n\n\nEgenhofer, Max, and John Herring. 1990. “A Mathematical Framework for the Definition of Topological Relations.” In Proc. The Fourth International Symposium on Spatial Data Handing, 803–13.\n\n\nHorn, B. K. P. 1981. “Hill Shading and the Reflectance Map.” Proceedings of the IEEE 69 (1): 14–47. https://doi.org/10.1109/PROC.1981.11918.\n\n\nLiu, Jian-Guo, and Philippa J. Mason. 2009. Essential Image Processing and GIS for Remote Sensing. Chichester, West Sussex, UK, Hoboken, NJ: Wiley-Blackwell.\n\n\nLovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2019. Geocomputation with R. CRC Press. http://robinlovelace.net/geocompr.\n\n\nPebesma, Edzer, and Roger Bivand. 2023. Spatial Data Science: With Applications in R. CRC Press. https://r-spatial.org/book/.\n\n\nQiu, Fang, Caiyun Zhang, and Yuhong Zhou. 2012. “The Development of an Areal Interpolation ArcGIS Extension and a Comparative Study.” GIScience & Remote Sensing 49 (5): 644–63. https://doi.org/gkb5fn.\n\n\nSpanier, Edwin Henry. 1995. Algebraic Topology. 1. corr. Springer ed. New York Berlin Barcelona Budapest: Springer.\n\n\nTobler, Waldo R. 1979. “Smooth Pycnophylactic Interpolation for Geographical Regions.” Journal of the American Statistical Association 74 (367): 519–30. https://doi.org/ghz78f.\n\n\nTomlin, C. Dana. 1990. Geographic Information Systems and Cartographic Modeling. Englewood Cliffs, N.J: Prentice Hall.\n\n\n———. 1994. “Map Algebra: One Perspective.” Landscape and Urban Planning 30 (1-2): 3–12. https://doi.org/dm2qm2.\n\n\nVirtanen, Pauli, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, et al. 2020. “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.” Nature Methods 17: 261–72. https://doi.org/10.1038/s41592-019-0686-2.\n\n\nZevenbergen, Lyle W., and Colin R. Thorne. 1987. “Quantitative Analysis of Land Surface Topography.” Earth Surface Processes and Landforms 12 (1): 47–56. https://doi.org/https://doi.org/10.1002/esp.3290120107.",
+    "text": "3.3 Spatial operations on raster data\nThis section builds on Section 2.3, which highlights various basic methods for manipulating raster datasets, to demonstrate more advanced and explicitly spatial raster operations, and uses the elev.tif and grain.tif rasters manually created in Section 1.3.2.\n\n3.3.1 Spatial subsetting\nThe previous chapter (and especially Section 2.3) demonstrated how to retrieve values associated with specific row and column combinations from a raster. Raster values can also be extracted by location (coordinates) and other spatial objects. To use coordinates for subsetting, we can use the .sample method of a rasterio file connection object, combined with a list of coordinate tuples. The method is demonstrated below to find the value of the cell that covers a point located at coordinates of (0.1,0.1) in elev. The returned object is a generator. The rationale for returning a generator, rather than a list, is memory efficiency. The number of sampled points may be huge, in which case we would want to generate the values one at a time rather than all at once.\n\nsrc_elev.sample([(0.1, 0.1)])\n\n&lt;generator object sample_gen at 0x7fdd06d83290&gt;\n\n\n\n\n\n\n\n\nNote\n\n\n\nThe technical terms iterable, iterator, and generator in Python may be confusing, so here is a short summary, ordered from most general to most specific:\n\nAn iterable is any object that we can iterate on, such as using a for loop. For example, a list is iterable.\nAn iterator is an object that represents a stream of data, which we can go over, each time getting the next element using next. Iterators are also iterable, meaning that you can over them in a loop, but they are stateful (e.g., they remember which item was obtained using next), meaning that you can go over them just once.\nA generator is a function that returns an iterator. For example, the .sample method in the above example is a generator. The rasterio package makes use of generators in some of its functions, as we will see later on (Section 5.5.1).\n\n\n\nIn case we nevertheless want all values at once, such as when the number of points is small, we can force the generatrion of all values from a generator at once, using list. Since there was just one point, the result is one extracted value, in this case 16.\n\nlist(src_elev.sample([(0.1, 0.1)]))\n\n[array([16], dtype=uint8)]\n\n\nWe can use the same technique to extract the values of multiple points at once. For example, here we extract the raster values at two points, (0.1,0.1) and (1.1,1.1). The resulting values are 16 and 6.\n\nlist(src_elev.sample([(0.1, 0.1), (1.1, 1.1)]))\n\n[array([16], dtype=uint8), array([6], dtype=uint8)]\n\n\nThe location of the two sample points on top of the elev.tif raster is illustrated in Figure 3.16.\n\nfig, ax = plt.subplots()\nrasterio.plot.show(src_elev, ax=ax)\ngpd.GeoSeries([shapely.Point(0.1, 0.1)]) \\\n    .plot(color='black', edgecolor='white', markersize=50, ax=ax)\ngpd.GeoSeries([shapely.Point(1.1, 1.1)]) \\\n    .plot(color='black', edgecolor='white', markersize=50, ax=ax);\n\n\n\n\n\n\n\nFigure 3.16: The elev.tif raster, and two points where we extract its values\n\n\n\n\n\n\n\n\n\n\n\nNote\n\n\n\nWe elaborate on the plotting technique used to display the points and the raster in Section 8.2.5. We will also introduce a more user-friendly and general method to extract raster values to points, using the rasterstats package, in Section 5.3.1.\n\n\nAnother common use case of spatial subsetting is using a boolean mask, based on another raster with the same extent and resolution, or the original one, as illustrated in Figure 3.17. To do that, we erase the values in the array of one raster, according to another corresponding mask raster. For example, let’s read (Section 1.3.1) the elev.tif raster values into an array named elev (Figure 3.17 (a)).\n\nelev = src_elev.read(1)\nelev\n\narray([[ 1,  2,  3,  4,  5,  6],\n       [ 7,  8,  9, 10, 11, 12],\n       [13, 14, 15, 16, 17, 18],\n       [19, 20, 21, 22, 23, 24],\n       [25, 26, 27, 28, 29, 30],\n       [31, 32, 33, 34, 35, 36]], dtype=uint8)\n\n\nand create a corresponding random boolean mask named mask (Figure 3.17 (b)), of the same shape as elev.tif with values randomly assigned to True and False.\n\nnp.random.seed(1)\nmask = np.random.choice([True, False], src_elev.shape)\nmask\n\narray([[False, False,  True,  True, False, False],\n       [False, False, False,  True,  True, False],\n       [ True, False, False,  True,  True, False],\n       [ True,  True,  True, False,  True,  True],\n       [False,  True,  True,  True, False,  True],\n       [ True,  True, False, False, False, False]])\n\n\nNext, suppose that we want to keep only those values of elev which are False in mask (i.e., they are not masked). In other words, we want to mask elev with mask. The result will be stored in a copy named masked_elev (Figure 3.17 (c)). In the case of elev.tif, to be able to store np.nan in the array of values, we also need to convert it to float (see Section 2.3.2). Afterwards, masking is a matter of assigning np.nan into a subset defined by the mask, using the ‘boolean array indexing’ syntax of numpy.\n\nmasked_elev = elev.copy()\nmasked_elev = masked_elev.astype('float64')\nmasked_elev[mask] = np.nan\nmasked_elev\n\narray([[ 1.,  2., nan, nan,  5.,  6.],\n       [ 7.,  8.,  9., nan, nan, 12.],\n       [nan, 14., 15., nan, nan, 18.],\n       [nan, nan, nan, 22., nan, nan],\n       [25., nan, nan, nan, 29., nan],\n       [nan, nan, 33., 34., 35., 36.]])\n\n\nFigure 3.17 shows the original elev raster, the mask raster, and the resulting masked_elev raster.\nrasterio.plot.show(elev);\nrasterio.plot.show(mask);\nrasterio.plot.show(masked_elev);\n\n\n\n\n\n\n\n\n\n\n\n(a) Original raster\n\n\n\n\n\n\n\n\n\n\n\n(b) Raster mask\n\n\n\n\n\n\n\n\n\n\n\n(c) Output masked raster\n\n\n\n\n\n\n\nFigure 3.17: Subsetting raster values using a boolean mask\n\n\n\nThe mask can be created from the array itself, using condition(s). That way, we can replace some values (e.g., values assumed to be wrong) with np.nan, such as in the following example.\n\nelev2 = elev.copy()\nelev2 = elev2.astype('float64')\nelev2[elev2 &lt; 20] = np.nan\nelev2\n\narray([[nan, nan, nan, nan, nan, nan],\n       [nan, nan, nan, nan, nan, nan],\n       [nan, nan, nan, nan, nan, nan],\n       [nan, 20., 21., 22., 23., 24.],\n       [25., 26., 27., 28., 29., 30.],\n       [31., 32., 33., 34., 35., 36.]])\n\n\nThis technique is also used to reclassify raster values (see Section 3.3.3).\n\n\n3.3.2 Map algebra\nThe term ‘map algebra’ was coined in the late 1970s to describe a ‘set of conventions, capabilities, and techniques’ for the analysis of geographic raster and (although less prominently) vector data (Tomlin 1994). In this context, we define map algebra more narrowly, as operations that modify or summarize raster cell values, with reference to surrounding cells, zones, or statistical functions that apply to every cell.\nMap algebra operations tend to be fast, because raster datasets only implicitly store coordinates, hence the old adage ‘raster is faster but vector is corrector’. The location of cells in raster datasets can be calculated by using its matrix position and the resolution and origin of the dataset (stored in the raster metadata, Section 1.3.1). For the processing, however, the geographic position of a cell is barely relevant as long as we make sure that the cell position is still the same after the processing. Additionally, if two or more raster datasets share the same extent, projection, and resolution, one could treat them as matrices for the processing.\nMap algebra (or cartographic modeling with raster data) divides raster operations into four subclasses (Tomlin 1990), with each working on one or several grids simultaneously:\n\nLocal or per-cell operations (Section 3.3.3)\nFocal or neighborhood operations. Most often the output cell value is the result of a \\(3 \\times 3\\) input cell block (Section 3.3.4)\nZonal operations are similar to focal operations, but the surrounding pixel grid on which new values are computed can have irregular sizes and shapes (Section 3.3.5)\nGlobal or per-raster operations; that means the output cell derives its value potentially from one or several entire rasters (Section 3.3.6)\n\nThis typology classifies map algebra operations by the number of cells used for each pixel processing step and the type of output. For the sake of completeness, we should mention that raster operations can also be classified by disciplines such as terrain, hydrological analysis, or image classification. The following sections explain how each type of map algebra operations can be used, with reference to worked examples.\n\n\n3.3.3 Local operations\nLocal operations comprise all cell-by-cell operations in one or several layers. Raster algebra is a classical use case of local operations—this includes adding or subtracting values from a raster, squaring,, and multiplying rasters. Raster algebra also allows logical operations such as finding all raster cells that are greater than a specific value (e.g., 5 in our example below). Local operations are applied using the numpy array operations syntax, as demonstrated below.\nFirst, let’s take the array of elev.tif raster values, which we already read earlier (Section 3.3.1).\n\nelev\n\narray([[ 1,  2,  3,  4,  5,  6],\n       [ 7,  8,  9, 10, 11, 12],\n       [13, 14, 15, 16, 17, 18],\n       [19, 20, 21, 22, 23, 24],\n       [25, 26, 27, 28, 29, 30],\n       [31, 32, 33, 34, 35, 36]], dtype=uint8)\n\n\nNow, any element-wise array operation can be applied using numpy arithmetic or conditional operators and functions, comprising local raster operations in spatial analysis terminology. For example, elev + elev adds the values of elev to itself, resulting in a raster with double values.\n\nelev + elev\n\narray([[ 2,  4,  6,  8, 10, 12],\n       [14, 16, 18, 20, 22, 24],\n       [26, 28, 30, 32, 34, 36],\n       [38, 40, 42, 44, 46, 48],\n       [50, 52, 54, 56, 58, 60],\n       [62, 64, 66, 68, 70, 72]], dtype=uint8)\n\n\nNote that some functions and operators automatically change the data type to accommodate the resulting values, while other operators do not, potentially resulting in overflow (i.e., incorrect values for results beyond the data type range, such as trying to accommodate values above 255 in an int8 array). For example, elev**2 (elev squared) results in overflow. Since the ** operator does not automatically change the data type, leaving it as int8, the resulting array has incorrect values for 16**2, 17**2, etc., which are above 255 and therefore cannot be accommodated.\n\nelev**2\n\narray([[  1,   4,   9,  16,  25,  36],\n       [ 49,  64,  81, 100, 121, 144],\n       [169, 196, 225,   0,  33,  68],\n       [105, 144, 185, 228,  17,  64],\n       [113, 164, 217,  16,  73, 132],\n       [193,   0,  65, 132, 201,  16]], dtype=uint8)\n\n\nTo avoid this situation, we can, for instance, transform elev to the standard int64 data type, using .astype before applying the ** operator. That way, all results, up to 36**2 (1296), can be easily accommodated, since the int64 data type supports values up to 9223372036854775807 (Table 7.2).\n\nelev.astype(int)**2\n\narray([[   1,    4,    9,   16,   25,   36],\n       [  49,   64,   81,  100,  121,  144],\n       [ 169,  196,  225,  256,  289,  324],\n       [ 361,  400,  441,  484,  529,  576],\n       [ 625,  676,  729,  784,  841,  900],\n       [ 961, 1024, 1089, 1156, 1225, 1296]])\n\n\nNow we get correct results.\nFigure 3.18 demonstrates the result of the last two examples (elev+elev and elev.astype(int)**2), and two other ones (np.log(elev) and elev&gt;5).\nrasterio.plot.show(elev + elev, cmap='Oranges');\nrasterio.plot.show(elev.astype(int)**2, cmap='Oranges');\nrasterio.plot.show(np.log(elev), cmap='Oranges');\nrasterio.plot.show(elev &gt; 5, cmap='Oranges');\n\n\n\n\n\n\n\n\n\n\n\n(a) elev+elev\n\n\n\n\n\n\n\n\n\n\n\n(b) elev.astype(int)**2\n\n\n\n\n\n\n\n\n\n\n\n(c) np.log(elev)\n\n\n\n\n\n\n\n\n\n\n\n(d) elev&gt;5\n\n\n\n\n\n\n\nFigure 3.18: Examples of different local operations of the elev raster object: adding two rasters, squaring, applying logarithmic transformation, and performing a logical operation.\n\n\n\nAnother good example of local operations is the classification of intervals of numeric values into groups such as grouping a digital elevation model into low (class 1), middle (class 2) and high (class 3) elevations. Here, the raster values in the ranges 0–12, 12–24, and 24–36 are reclassified to take values 1, 2, and 3, respectively.\n\nrecl = elev.copy()\nrecl[(elev &gt; 0)  & (elev &lt;= 12)] = 1\nrecl[(elev &gt; 12) & (elev &lt;= 24)] = 2\nrecl[(elev &gt; 24) & (elev &lt;= 36)] = 3\n\nFigure 3.19 compares the original elev raster with the reclassified recl one.\nrasterio.plot.show(elev, cmap='Oranges');\nrasterio.plot.show(recl, cmap='Oranges');\n\n\n\n\n\n\n\n\n\n\n\n(a) Original\n\n\n\n\n\n\n\n\n\n\n\n(b) Reclassified\n\n\n\n\n\n\n\nFigure 3.19: Reclassifying a continuous raster into three categories.\n\n\n\nThe calculation of the Normalized Difference Vegetation Index (NDVI)2 is a well-known local (pixel-by-pixel) raster operation. It returns a raster with values between -1 and 1; positive values indicate the presence of living plants (mostly &gt; 0.2). NDVI is calculated from red and near-infrared (NIR) bands of remotely sensed imagery, typically from satellite systems such as Landsat or Sentinel-2. Vegetation absorbs light heavily in the visible light spectrum, and especially in the red channel, while reflecting NIR light, which is emulated in the NVDI formula (Equation 3.1),\n\\[\nNDVI=\\frac{NIR-Red} {NIR+Red}\n\\tag{3.1}\\]\n, where \\(NIR\\) is the near-infrared band and \\(Red\\) is the red band.\nLet’s calculate NDVI for the multispectral Landsat satellite file (landsat.tif) of the Zion National Park. The file landsat.tif contains surface reflectance values (range 0-1) in the blue, green, red, and near-infrared (NIR) bands. We start by reading the file and extracting the NIR and red bands, which are the fourth and third bands, respectively. Next, we apply the formula to calculate the NDVI values.\n\nlandsat = src_landsat.read()\nnir = landsat[3]\nred = landsat[2]\nndvi = (nir-red)/(nir+red)\n\nWhen plotting an RGB image using the rasterio.plot.show function, the function assumes that values are in the range [0,1] for floats, or [0,255] for integers (otherwise clipped) and the order of bands is RGB. To prepare the multi-band raster for rasterio.plot.show, we, therefore, reverse the order of the first three bands (to go from B-G-R-NIR to R-G-B), using the [:3] slice to select the first three bands and then the [::-1] slice to reverse the bands order, and divide by the raster maximum to set the maximum value to 1.\n\nlandsat_rgb = landsat[:3][::-1] / landsat.max()\n\n\n\n\n\n\n\nNote\n\n\n\nPython slicing notation, which numpy, pandas and geopandas also follow, is object[start:stop:step]. The default is to start from the beginning, go to the end, and use steps of 1. Otherwise, start is inclusive and end is exclusive, whereas negative step values imply going backwards starting from the end. Also, always keep in mind that Python indices start from 0. When subsetting two- or three-dimensional objects, indices for each dimension are separated by commas, where either index can be set to : meaning ‘all values’. The last dimensions can also be omitted implying :, e.g., to subset the first three bands from a three-dimensional array a we can use either a[:3,:,:] or a[:3].\nIn the above example:\n\nThe slicing expression [:3] therefore means layers 0, 1, 2 (up to 3, exclusive)\nThe slicing expression [::-1] therefore means all (three) bands in reverse order\n\n\n\nFigure 3.20 shows the RGB image and the NDVI values calculated for the Landsat satellite image of the Zion National Park.\nrasterio.plot.show(landsat_rgb, cmap='RdYlGn');\nrasterio.plot.show(ndvi, cmap='Greens');\n\n\n\n\n\n\n\n\n\n\n\n(a) RGB image\n\n\n\n\n\n\n\n\n\n\n\n(b) NDVI\n\n\n\n\n\n\n\nFigure 3.20: RGB image and NDVI values calculated for the Landsat satellite image of the Zion National Park\n\n\n\n\n\n3.3.4 Focal operations\nWhile local functions operate on one cell at a time (though possibly from multiple layers), focal operations take into account a central (focal) cell and its neighbors. The neighborhood (also named kernel, filter, or moving window) under consideration is typically of \\(3 \\times 3\\) cells (that is, the central cell and its eight surrounding neighbors), but can take on any other (not necessarily rectangular) shape as defined by the user. A focal operation applies an aggregation function to all cells within the specified neighborhood, uses the corresponding output as the new value for the central cell, and moves on to the next central cell (Figure 3.21). Other names for this operation are spatial filtering and convolution (Burrough, McDonnell, and Lloyd 2015).\n\n\n\n\n\n\nFigure 3.21: Input raster (left) and resulting output raster (right) due to a focal operation—finding the minimum value in \\(3 \\times 3\\) moving windows.\n\n\n\nIn Python, the scipy.ndimage (Virtanen et al. 2020) package has a comprehensive collection of functions to perform filtering of numpy arrays, such as:\n\nscipy.ndimage.minimum_filter,\nscipy.ndimage.maximum_filter,\nscipy.ndimage.uniform_filter (i.e., mean filter),\nscipy.ndimage.median_filter, etc.\n\nIn this group of functions, we define the shape of the moving window with either one of size—a single number (e.g., 3), or tuple (e.g., (3,3)), implying a filter of those dimensions, or footprint—a boolean array, representing both the window shape and the identity of elements being included.\nIn addition to specific built-in filters, convolve—applies the sum function after multiplying by a custom weights array, and generic_filter—makes it possible to pass any custom function, where the user can specify any type of custom window-based calculation.\nFor example, here we apply the minimum filter with window size of 3 on elev. As a result, we now have a new array elev_min, where each value is the minimum in the corresponding \\(3 \\times 3\\) neighborhood in elev.\n\nelev_min = scipy.ndimage.minimum_filter(elev, size=3)\nelev_min\n\narray([[ 1,  1,  2,  3,  4,  5],\n       [ 1,  1,  2,  3,  4,  5],\n       [ 7,  7,  8,  9, 10, 11],\n       [13, 13, 14, 15, 16, 17],\n       [19, 19, 20, 21, 22, 23],\n       [25, 25, 26, 27, 28, 29]], dtype=uint8)\n\n\nSpecial care should be given to the edge pixels – how should they be calculated? The scipy.ndimage filtering functions give several options through the mode parameter (see the documentation of any filtering function, such as scipy.ndimage.median_filter, for the definition of each mode): reflect (the default), constant, nearest, mirror, wrap. Sometimes artificially extending raster edges is considered unsuitable. In other words, we may wish the resulting raster to contain pixel values with ‘complete’ windows only, for example, to have a uniform sample size or because values in all directions matter (such as in topographic calculations). There is no specific option not to extend edges in scipy.ndimage. However, to get the same effect, the edges of the filtered array can be assigned with np.nan, in a number of rows and columns according to filter size. For example, when using a filter of size=3, the outermost ‘layer’ of pixels may be assigned with np.nan, reflecting the fact that these pixels have incomplete \\(3 \\times 3\\) neighborhoods (Figure 3.21):\n\nelev_min = elev_min.astype(float)\nelev_min[:, [0, -1]] = np.nan\nelev_min[[0, -1], :] = np.nan\nelev_min\n\narray([[nan, nan, nan, nan, nan, nan],\n       [nan,  1.,  2.,  3.,  4., nan],\n       [nan,  7.,  8.,  9., 10., nan],\n       [nan, 13., 14., 15., 16., nan],\n       [nan, 19., 20., 21., 22., nan],\n       [nan, nan, nan, nan, nan, nan]])\n\n\nWe can quickly check if the output meets our expectations. In our example, the minimum value has to be always the upper left corner of the moving window (remember we have created the input raster by row-wise incrementing the cell values by one starting at the upper left corner).\nFocal functions or filters play a dominant role in image processing. For example, low-pass or smoothing filters use the mean function to remove extremes. By contrast, high-pass filters, often created with custom neighborhood weights, accentuate features.\nIn the case of categorical data, we can replace the mean with the mode, i.e., the most common value. To demonstrate applying a mode filter, let’s read the small sample categorical raster grain.tif.\n\ngrain = src_grain.read(1)\ngrain\n\narray([[1, 0, 1, 2, 2, 2],\n       [0, 2, 0, 0, 2, 1],\n       [0, 2, 2, 0, 0, 2],\n       [0, 0, 1, 1, 1, 1],\n       [1, 1, 1, 2, 1, 1],\n       [2, 1, 2, 2, 0, 2]], dtype=uint8)\n\n\nThere is no built-in filter function for a mode filter in scipy.ndimage, but we can use the scipy.ndimage.generic_filter function along with a custom filtering function, internally utilizing scipy.stats.mode.\n\ngrain_mode = scipy.ndimage.generic_filter(\n    grain, \n    lambda x: scipy.stats.mode(x.flatten())[0], \n    size=3\n)\ngrain_mode = grain_mode.astype(float)\ngrain_mode[:, [0, -1]] = np.nan\ngrain_mode[[0, -1], :] = np.nan\ngrain_mode\n\narray([[nan, nan, nan, nan, nan, nan],\n       [nan,  0.,  0.,  0.,  2., nan],\n       [nan,  0.,  0.,  0.,  1., nan],\n       [nan,  1.,  1.,  1.,  1., nan],\n       [nan,  1.,  1.,  1.,  1., nan],\n       [nan, nan, nan, nan, nan, nan]])\n\n\n\n\n\n\n\n\nNote\n\n\n\nscipy.stats.mode is a function to summarize array values, returning the mode (most common value). It is analogous to numpy summary functions and methods, such as .mean or .max. numpy itself does not provide the mode function, however, which is why we use scipy for that.\n\n\nTerrain processing is another important application of focal operations. Such functions are provided by multiple Python packages, including the general purpose xarray package, and more specialized packages such as richdem and pysheds. Useful terrain metrics include:\n\nSlope, measured in units of percent, degreees, or radians (Horn 1981)\nAspect, meaning each cell’s downward slope direction (Horn 1981)\nSlope curvature, including ‘planform’ and ‘profile’ curvature (Zevenbergen and Thorne 1987)\n\nFor example, each of these, and other, terrain metrics can be computed with the richdem package.\n\n\n\n\n\n\nNote\n\n\n\nTerrain metrics are essentially focal filters with customized functions. Using scipy.ndimage.generic_filter, along with such custom functions, is an option for those who would like to calculate terrain metric through coding by hand and/or limiting their code dependencies. For example, the How Aspect works3 and How Slope works4 pages from the ArcGIS Pro documentation provide explanations and formulas of the required functions for aspect and slope metrics (Figure 3.22), respectively, which can be translated to numpy-based functions to be used in scipy.ndimage.generic_filter to calculate those metrics.\n\n\nAnother extremely fast, memory-efficient, and concise, alternative, is to the use the GDAL program called gdaldem. gdaldem can be used to calculate slope, aspect, and other terrain metrics through a single command, accepting an input file path and exporting the result to a new file. This is our first example in the book where we demonstrate a situation where it may be worthwhile to leave the Python environment, and utilize a GDAL program directly, rather than through their wrappers (such as rasterio and other Python packages), whether to access a computational algorithm not easily accessible in a Python package, or for GDAL’s memory-efficiency and speed benefits.\n\n\n\n\n\n\nNote\n\n\n\nGDAL contains a collection of over 40 programs, mostly aimed at raster processing. These include programs for fundamental operations, such as:\n\ngdal_translate—convert between raster file formats\ngdalwarp—raster reprojection\ngdal_rasterize—rasterize vector features\ngdal_merge.py—raster mosaic\n\nIn this book, we use rasterio for the above-mentioned operations, although the GDAL programs are a good alternative for those who are more comfortable with the command line. However, we do use two GDAL programs for tasks that are lacking in rasterio and not well-implemented in other Python packages: gdaldem (this section), and gdal_contour (Section 5.5.3).\n\n\nGDAL, along with all of its programs should be available in your Python environment, since GDAL is a dependency of rasterio. The following example, which should be run from the command line, takes the srtm_32612.tif raster (which we are going to create in Section 6.8, therefore it is in the 'output' directory), calculates slope (in decimal degrees, between 0 and 90), and exports the result to a new file srtm_32612_slope.tif. Note that the arguments of gdaldem are the metric name (slope), then the input file path, and finally the output file path.\n\nos.system('gdaldem slope output/srtm_32612.tif output/srtm_32612_slope.tif')\n\nHere we ran the gdaldem command through os.system, in order to remain in the Python environment, even though we are calling an external program. Alternatively, you can run the standalone command in the command line interface you are using, such as the Anaconda Prompt:\ngdaldem slope output/srtm_32612.tif output/srtm_32612_slope.tif\nReplacing the metric name, we can calculate other terrain properties. For example, here is how we can calculate an aspect raster srtm_32612_aspect.tif, also in degrees (between 0 and 360).\n\nos.system('gdaldem aspect output/srtm_32612.tif output/srtm_32612_aspect.tif')\n\nFigure 3.22 shows the results, using our more familiar plotting methods from rasterio. The code section is relatively long due to the workaround to create a color key (see Section 8.2.3) and removing ‘No Data’ flag values from the arrays so that the color key does not include them. Also note that we are using one of matplotlib’s the cyclic color scales ('twilight') when plotting aspect (Figure 3.22 (c)).\n# Input DEM\nsrc_srtm = rasterio.open('output/srtm_32612.tif')\nsrtm = src_srtm.read(1).astype(float)\nsrtm[srtm == src_srtm.nodata] = np.nan\nfig, ax = plt.subplots()\nrasterio.plot.show(src_srtm, cmap='Spectral_r', ax=ax)\nfig.colorbar(ax.imshow(srtm, cmap='Spectral_r'), ax=ax);\n# Slope\nsrc_srtm_slope = rasterio.open('output/srtm_32612_slope.tif')\nsrtm_slope = src_srtm_slope.read(1)\nsrtm_slope[srtm_slope == src_srtm_slope.nodata] = np.nan\nfig, ax = plt.subplots()\nrasterio.plot.show(src_srtm_slope, cmap='Spectral_r', ax=ax)\nfig.colorbar(ax.imshow(srtm_slope, cmap='Spectral_r'), ax=ax);\n# Aspect\nsrc_srtm_aspect = rasterio.open('output/srtm_32612_aspect.tif')\nsrtm_aspect = src_srtm_aspect.read(1)\nsrtm_aspect[srtm_aspect == src_srtm_aspect.nodata] = np.nan\nfig, ax = plt.subplots()\nrasterio.plot.show(src_srtm_aspect, cmap='twilight', ax=ax)\nfig.colorbar(ax.imshow(srtm_aspect, cmap='twilight'), ax=ax);\n\n\n\n\n\n\n\n\n\n\n\n(a) Input DEM\n\n\n\n\n\n\n\n\n\n\n\n(b) Slope (degrees)\n\n\n\n\n\n\n\n\n\n\n\n(c) Aspect (degrees)\n\n\n\n\n\n\n\nFigure 3.22: Slope and aspect calculation from a DEM\n\n\n\n\n\n3.3.5 Zonal operations\nJust like focal operations, zonal operations apply an aggregation function to multiple raster cells. However, a second raster, usually with categorical values, defines the zonal filters (or ‘zones’) in the case of zonal operations, as opposed to a predefined neighborhood window in the case of focal operation presented in the previous section. Consequently, raster cells defining the zonal filter do not necessarily have to be neighbors. Our grain.tif raster is a good example, as illustrated in Figure 1.24: different grain sizes are spread irregularly throughout the raster. Finally, the result of a zonal operation is a summary table grouped by zone, which is why this operation is also known as zonal statistics in the GIS world. This is in contrast to focal operations (Section 3.3.4) which return a raster object.\nTo demonstrate, let’s get back to the grain.tif and elev.tif rasters. To calculate zonal statistics, we use the arrays with raster values, which we already imported earlier. Our intention is to calculate the average (or any other summary function, for that matter) of elevation in each zone defined by grain values. To do that, first we first obtain the unique values defining the zones using np.unique.\n\nnp.unique(grain)\n\narray([0, 1, 2], dtype=uint8)\n\n\nNow, we can use dictionary comprehension (see note below) to split the elev array into separate one-dimensional arrays with values per grain group, with keys being the unique grain values.\n\nz = {i: elev[grain == i] for i in np.unique(grain)}\nz\n\n{np.uint8(0): array([ 2,  7,  9, 10, 13, 16, 17, 19, 20, 35], dtype=uint8),\n np.uint8(1): array([ 1,  3, 12, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32], dtype=uint8),\n np.uint8(2): array([ 4,  5,  6,  8, 11, 14, 15, 18, 28, 31, 33, 34, 36], dtype=uint8)}\n\n\n\n\n\n\n\n\nNote\n\n\n\nList comprehension and dictionary comprehension are concise ways to create a list or a dict, respectively, from an iterable object. Both are, conceptually, a concise syntax to replace for loops where we iterate over an object and return a same-length object with the results. Here are minimal examples of list and dictionary comprehension, respectively, to demonstrate the idea:\n\n[i**2 for i in [2,4,6]]—Returns [4,16,36]\n{i: i**2 for i in [2,4,6]}—Returns {2:4, 4:16, 6:36}\n\nList comprehension is more commonly encountered in practice. We use it in Section 4.2.6, Section 5.4.2, Section 5.5.1, and Section 5.6. Dictionary comprehension is only used in one place in the book (this section).\n\n\nAt this stage, we can expand the dictionary comprehension expression to calculate the mean elevation associated with each grain size class. Namely, instead of placing the elevation values (elev[grain==i]) into the dictionary values, we place their (rounded) mean (elev[grain==i].mean().round(1)).\n\nz = {i: elev[grain == i].mean().round(1) for i in np.unique(grain)}\nz\n\n{np.uint8(0): np.float64(14.8),\n np.uint8(1): np.float64(21.2),\n np.uint8(2): np.float64(18.7)}\n\n\nThis returns the statistics for each category, here the mean elevation for each grain size class. For example, the mean elevation in pixels characterized by grain size 0 is 14.8, and so on.\n\n\n3.3.6 Global operations and distances\nGlobal operations are a special case of zonal operations with the entire raster dataset representing a single zone. The most common global operations are descriptive statistics for the entire raster dataset such as the minimum or maximum—we already discussed those in Section 2.3.2.\nAside from that, global operations are also useful for the computation of distance and weight rasters. In the first case, one can calculate the distance from each cell to specific target cells or vector geometries. For example, one might want to compute the distance to the nearest coast (see Section 5.6). We might also want to consider topography, that means, we are not only interested in the pure distance but would like also to avoid the crossing of mountain ranges when going to the coast. To do so, we can weight the distance with elevation so that each additional altitudinal meter ‘prolongs’ the Euclidean distance (this is beyond the scope of the book). Visibility and viewshed computations also belong to the family of global operations (also beyond the scope of the book).\n\n\n3.3.7 Map algebra counterparts in vector processing\nMany map algebra operations have a counterpart in vector processing (Liu and Mason 2009). Computing a distance raster (global operation) while only considering a maximum distance (logical focal operation) is the equivalent of a vector buffer operation (Section 4.2.3). Reclassifying raster data (either local or zonal function depending on the input) is equivalent to dissolving vector data (Section 4.2.7). Overlaying two rasters (local operation), where one contains ‘No Data’ values representing a mask, is similar to vector clipping (Section Section 4.2.5). Quite similar to spatial clipping is intersecting two layers (Section 3.2.1, Section 3.2.6). The difference is that these two layers (vector or raster) simply share an overlapping area. However, be careful with the wording. Sometimes the same words have slightly different meanings for raster and vector data models. While aggregating polygon geometries means dissolving boundaries, for raster data geometries it means increasing cell sizes and thereby reducing spatial resolution. Zonal operations dissolve the cells of one raster in accordance with the zones (categories) of another raster dataset using an aggregating function.\n\n\n3.3.8 Merging rasters\nSuppose we would like to compute the NDVI (see Section 3.3.3), and additionally want to compute terrain attributes from elevation data for observations within a study area. Such computations rely on remotely sensed information. The corresponding source imagery is often divided into scenes covering a specific spatial extent (i.e., tiles), and frequently, a study area covers more than one scene. Then, we would need to merge (also known as mosaic) the scenes covering our study area. In case when all scenes are aligned (i.e., share the same origin and resolution), this can be thought of as simply gluing them into one big raster; otherwise, all scenes need to be resampled (see Section 4.3.3) to the same grid (e.g., the one defined by the first scene).\nFor example, let’s merge digital elevation data from two SRTM elevation tiles, for Austria ('aut.tif') and Switzerland ('ch.tif'). Merging can be done using function rasterio.merge.merge, which accepts a list of raster file connections, and returns the new ndarray and the corresponding transform object, representing the resulting mosaic.\n\nsrc_1 = rasterio.open('data/aut.tif')\nsrc_2 = rasterio.open('data/ch.tif')\nout_image, out_transform = rasterio.merge.merge([src_1, src_2])\n\n\n\n\n\n\n\nNote\n\n\n\nSome Python packages (such as rasterio) are split into several so-called sub-modules. The sub-modules are installed collectively when installing the main package. However, each sub-module needs to be loaded separately to be able to use its functions. For example, the rasterio.merge.merge function (see last code block) comes from the rasterio.merge sub-module of rasterio. Loading rasterio with import rasterio does not expose the rasterio.merge.merge function; instead, we have to load rasterio.merge with import rasterio.merge, and only then use rasterio.merge.merge.\nAlso check out the first code block in this chapter, where we load rasterio as well as three sub-modules: rasterio.plot, rasterio.merge, and rasterio.features.\n\n\nFigure 3.23 shows both inputs and the resulting mosaic.\nrasterio.plot.show(src_1);\nrasterio.plot.show(src_2);\nrasterio.plot.show(out_image, transform=out_transform);\n\n\n\n\n\n\n\n\n\n\n\n(a) aut.tif\n\n\n\n\n\n\n\n\n\n\n\n(b) ch.tif\n\n\n\n\n\n\n\n\n\n\n\n(c) Mosaic (aut.tif+ch.tif)\n\n\n\n\n\n\n\nFigure 3.23: Raster merging\n\n\n\nBy default in rasterio.merge.merge, areas of overlap retain the value of the first raster (method='first'). Other possible methods are:\n\n'last'—Value of the last raster\n'min'—Minimum value\n'max'—Maximum value\n\nWhen dealing with non-overlapping tiles, such as aut.tif and ch.tif (above), the method argument has no practical effect. However, it becomes relevant when we want to combine spectral imagery from scenes that were taken on different dates. The above four options for method do not cover the commonly required scenario when we would like to compute the mean value—for example to calculate a seasonal average NDVI image from a set of partially overlapping satellite images (such as Landsat). An alternative workflow to rasterio.merge.merge, for calculating a mosaic as well as averaging any overlaps, is to go through two steps:\n\nResampling all scenes into a common ‘global’ grid (Section 4.3.3), thereby producing a series of matching rasters (with the area surrounding each scene set as ‘No Data’)\nAveraging the rasters through raster algebra (Section 3.3.3), using np.mean(m,axis=0) or np.nanmean(m,axis=0) (depending whether we prefer to ignore ‘No Data’ or not), where m is the multi-band array, which would return a single-band array of averages\n\n\n\n\n\n\nBurrough, P. A., Rachael McDonnell, and Christopher D. Lloyd. 2015. Principles of Geographical Information Systems. Third. Oxford, New York: Oxford University Press.\n\n\nDieck, Tammo tom. 2008. Algebraic Topology. EMS Textbooks in Mathematics. Zürich: European Mathematical Society. https://www.maths.ed.ac.uk/~v1ranick/papers/diecktop.pdf.\n\n\nEgenhofer, Max, and John Herring. 1990. “A Mathematical Framework for the Definition of Topological Relations.” In Proc. The Fourth International Symposium on Spatial Data Handing, 803–13.\n\n\nHorn, B. K. P. 1981. “Hill Shading and the Reflectance Map.” Proceedings of the IEEE 69 (1): 14–47. https://doi.org/10.1109/PROC.1981.11918.\n\n\nLiu, Jian-Guo, and Philippa J. Mason. 2009. Essential Image Processing and GIS for Remote Sensing. Chichester, West Sussex, UK, Hoboken, NJ: Wiley-Blackwell.\n\n\nLovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2019. Geocomputation with R. CRC Press. http://robinlovelace.net/geocompr.\n\n\nPebesma, Edzer, and Roger Bivand. 2023. Spatial Data Science: With Applications in R. CRC Press. https://r-spatial.org/book/.\n\n\nQiu, Fang, Caiyun Zhang, and Yuhong Zhou. 2012. “The Development of an Areal Interpolation ArcGIS Extension and a Comparative Study.” GIScience & Remote Sensing 49 (5): 644–63. https://doi.org/gkb5fn.\n\n\nSpanier, Edwin Henry. 1995. Algebraic Topology. 1. corr. Springer ed. New York Berlin Barcelona Budapest: Springer.\n\n\nTobler, Waldo R. 1979. “Smooth Pycnophylactic Interpolation for Geographical Regions.” Journal of the American Statistical Association 74 (367): 519–30. https://doi.org/ghz78f.\n\n\nTomlin, C. Dana. 1990. Geographic Information Systems and Cartographic Modeling. Englewood Cliffs, N.J: Prentice Hall.\n\n\n———. 1994. “Map Algebra: One Perspective.” Landscape and Urban Planning 30 (1-2): 3–12. https://doi.org/dm2qm2.\n\n\nVirtanen, Pauli, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, et al. 2020. “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.” Nature Methods 17: 261–72. https://doi.org/10.1038/s41592-019-0686-2.\n\n\nZevenbergen, Lyle W., and Colin R. Thorne. 1987. “Quantitative Analysis of Land Surface Topography.” Earth Surface Processes and Landforms 12 (1): 47–56. https://doi.org/https://doi.org/10.1002/esp.3290120107.",
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-    "text": "5.5 Spatial vectorization\nSpatial vectorization is the counterpart of rasterization (Section 5.4). It involves converting spatially continuous raster data into spatially discrete vector data such as points, lines, or polygons. There are three standard methods to convert a raster to a vector layer, which we cover next:\n\nRaster to polygons (Section 5.5.1)—converting raster cells to rectangular polygons, representing pixel areas\nRaster to points (Section 5.5.2)—converting raster cells to points, representing pixel centroids\nRaster to contours (Section 5.5.3)\n\nLet us demonstrate all three in the given order.\n\n5.5.1 Raster to polygons\nThe rasterio.features.shapes gives access to raster pixels as polygon geometries, along with the associated raster values. The returned object is a generator (see note in Section 3.3.1), yielding geometry,value pairs.\nFor example, the following expression returns a generator named shapes, referring to the pixel polygons.\n\nshapes = rasterio.features.shapes(rasterio.band(src_grain, 1) )\nshapes\n\n&lt;generator object shapes at 0x7fca98e13cd0&gt;\n\n\nWe can generate all shapes at once into a list named pol with list(shapes).\n\npol = list(shapes)\n\nEach element in pol is a tuple of length 2, containing the GeoJSON-like dict—representing the polygon geometry and the value of the pixel(s) which comprise the polygon. For example, here is the first element of pol.\n\npol[0]\n\n({'type': 'Polygon',\n  'coordinates': [[(-1.5, 1.5),\n    (-1.5, 1.0),\n    (-1.0, 1.0),\n    (-1.0, 1.5),\n    (-1.5, 1.5)]]},\n 1.0)\n\n\n\n\n\n\n\n\nNote\n\n\n\nNote that, when transforming a raster cell into a polygon, five-coordinate pairs need to be kept in memory to represent its geometry (explaining why rasters are often fast compared with vectors!).\n\n\nTo transform the list coming out of rasterio.features.shapes into the familiar GeoDataFrame, we need few more steps of data reshaping. First, we apply the shapely.geometry.shape function to go from a list of GeoJSON-like dicts to a list of shapely geometry objects. The list can then be converted to a GeoSeries (see Section 1.2.6).\n\ngeom = [shapely.geometry.shape(i[0]) for i in pol]\ngeom = gpd.GeoSeries(geom, crs=src_grain.crs)\ngeom\n\n0     POLYGON ((-1.5 1.5, -1.5 1, -1 ...\n1     POLYGON ((-1 1.5, -1 1, -0.5 1,...\n2     POLYGON ((-0.5 1.5, -0.5 1, 0 1...\n                     ...                \n11    POLYGON ((0 -0.5, 0 -1, -0.5 -1...\n12    POLYGON ((0.5 -1, 0.5 -1.5, 1 -...\n13    POLYGON ((1 -1, 1 -1.5, 1.5 -1....\nLength: 14, dtype: geometry\n\n\nThe values can also be extracted from the rasterio.features.shapes result and turned into a corresponding Series.\n\nvalues = [i[1] for i in pol]\nvalues = pd.Series(values)\nvalues\n\n0     1.0\n1     0.0\n2     1.0\n     ... \n11    2.0\n12    0.0\n13    2.0\nLength: 14, dtype: float64\n\n\nFinally, the two can be combined into a GeoDataFrame, hereby named result.\n\nresult = gpd.GeoDataFrame({'value': values, 'geometry': geom})\nresult\n\n\n\n\n\n\n\n\nvalue\ngeometry\n\n\n\n\n0\n1.0\nPOLYGON ((-1.5 1.5, -1.5 1, -1 ...\n\n\n1\n0.0\nPOLYGON ((-1 1.5, -1 1, -0.5 1,...\n\n\n2\n1.0\nPOLYGON ((-0.5 1.5, -0.5 1, 0 1...\n\n\n...\n...\n...\n\n\n11\n2.0\nPOLYGON ((0 -0.5, 0 -1, -0.5 -1...\n\n\n12\n0.0\nPOLYGON ((0.5 -1, 0.5 -1.5, 1 -...\n\n\n13\n2.0\nPOLYGON ((1 -1, 1 -1.5, 1.5 -1....\n\n\n\n\n14 rows × 2 columns\n\n\n\nThe polygon layer result is shown in Figure 5.8.\n\nresult.plot(column='value', edgecolor='black', legend=True);\n\n\n\n\n\n\n\nFigure 5.8: grain.tif converted to a polygon layer\n\n\n\n\n\nAs highlighted using edgecolor='black', neighboring pixels sharing the same raster value are dissolved into larger polygons. The rasterio.features.shapes function unfortunately does not offer a way to avoid this type of dissolving. One suggestion is to add unique values between 0 and 0.9999 to all pixels, convert to polygons, and then get back to the original values using np.floor.\n\n\n5.5.2 Raster to points\nTo transform a raster to points, we can use the rasterio.transform.xy function. As the name suggests, the function accepts row and column indices, and transforms them into x- and y-coordinates (using the raster’s transformation matrix). For example, the coordinates of the top-left pixel can be calculated passing the (row,col) indices of (0,0).\n\nsrc = rasterio.open('output/elev.tif')\nrasterio.transform.xy(src.transform, 0, 0)\n\n(np.float64(-1.25), np.float64(1.25))\n\n\n\n\n\n\n\n\nNote\n\n\n\nKeep in mind that the coordinates of the top-left pixel ((-1.25, 1.25)), as calculated in the above expression, refer to the pixel centroid. Therefore, they are not identical to the raster origin coordinates ((-1.5,1.5)), as specified in the transformation matrix, which are the coordinates of the top-left edge/corner of the raster (see Figure 5.9).\n\nsrc.transform\n\nAffine(0.5, 0.0, -1.5,\n       0.0, -0.5, 1.5)\n\n\n\n\nTo generalize the above expression to calculate the coordinates of all pixels, we first need to generate a grid of all possible row/column index combinations. This can be done using np.meshgrid, as follows.\n\nheight = src.shape[0]\nwidth = src.shape[1]\ncols, rows = np.meshgrid(np.arange(width), np.arange(height))\n\nWe now have two arrays, rows and cols, matching the shape of elev.tif and containing the corresponding row and column indices.\n\nrows\n\narray([[0, 0, 0, 0, 0, 0],\n       [1, 1, 1, 1, 1, 1],\n       [2, 2, 2, 2, 2, 2],\n       [3, 3, 3, 3, 3, 3],\n       [4, 4, 4, 4, 4, 4],\n       [5, 5, 5, 5, 5, 5]])\n\n\n\ncols\n\narray([[0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5]])\n\n\nThese can be passed to rasterio.transform.xy to transform the indices into point coordinates, accordingly stored in lists of arrays x and y.\n\nx, y = rasterio.transform.xy(src.transform, rows, cols)\n\n\nx\n\n[array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25])]\n\n\n\ny\n\n[array([1.25, 1.25, 1.25, 1.25, 1.25, 1.25]),\n array([0.75, 0.75, 0.75, 0.75, 0.75, 0.75]),\n array([0.25, 0.25, 0.25, 0.25, 0.25, 0.25]),\n array([-0.25, -0.25, -0.25, -0.25, -0.25, -0.25]),\n array([-0.75, -0.75, -0.75, -0.75, -0.75, -0.75]),\n array([-1.25, -1.25, -1.25, -1.25, -1.25, -1.25])]\n\n\nTypically we want to work with the points in the form of a GeoDataFrame which also holds the attribute(s) value(s) as point attributes. To get there, we can transform the coordinates as well as any attributes to 1-dimensional arrays, and then use methods we are already familiar with (Section 1.2.6) to combine them into a GeoDataFrame.\n\nx = np.array(x).flatten()\ny = np.array(y).flatten()\nz = src.read(1).flatten()\ngeom = gpd.points_from_xy(x, y, crs=src.crs)\npnt = gpd.GeoDataFrame(data={'value':z}, geometry=geom)\npnt\n\n\n\n\n\n\n\n\nvalue\ngeometry\n\n\n\n\n0\n1\nPOINT (-1.25 1.25)\n\n\n1\n2\nPOINT (-0.75 1.25)\n\n\n2\n3\nPOINT (-0.25 1.25)\n\n\n...\n...\n...\n\n\n33\n34\nPOINT (0.25 -1.25)\n\n\n34\n35\nPOINT (0.75 -1.25)\n\n\n35\n36\nPOINT (1.25 -1.25)\n\n\n\n\n36 rows × 2 columns\n\n\n\nThis ‘high-level’ workflow, like many other rasterio-based workflows covered in the book, is a commonly used one but lacking from the package itself. From the user’s perspective, it may be a good idea to wrap the workflow into a function (e.g., raster_to_points(src), returning a GeoDataFrame), to be re-used whenever we need it.\nFigure 5.9 shows the input raster and the resulting point layer.\n# Input raster\nfig, ax = plt.subplots()\npnt.plot(column='value', legend=True, ax=ax)\nrasterio.plot.show(src_elev, ax=ax);\n# Points\nfig, ax = plt.subplots()\npnt.plot(column='value', legend=True, edgecolor='black', ax=ax)\nrasterio.plot.show(src_elev, alpha=0, ax=ax);\n\n\n\n\n\n\n\n\n\n\n\n(a) Input raster\n\n\n\n\n\n\n\n\n\n\n\n(b) Points\n\n\n\n\n\n\n\nFigure 5.9: Raster and point representation of elev.tif\n\n\n\nNote that ‘No Data’ pixels can be filtered out from the conversion, if necessary (see Section 5.6).\n\n\n5.5.3 Raster to contours\nAnother common type of spatial vectorization is the creation of contour lines, representing lines of continuous height or temperatures (isotherms), for example. We will use a real-world digital elevation model (DEM) because the artificial raster elev.tif produces parallel lines (task for the reader: verify this and explain why this happens). Plotting contour lines is straightforward, using the contour=True option of rasterio.plot.show (Figure 5.10).\n\nfig, ax = plt.subplots()\nrasterio.plot.show(src_dem, ax=ax)\nrasterio.plot.show(\n    src_dem, \n    ax=ax, \n    contour=True, \n    levels=np.arange(0,1200,50), \n    colors='black'\n);\n\n\n\n\n\n\n\nFigure 5.10: Displaying raster contours\n\n\n\n\n\nUnfortunately, rasterio does not provide any way of extracting the contour lines in the form of a vector layer, for uses other than plotting.\nThere are two possible workarounds:\n\nUsing gdal_contour on the command line (see below), or through its Python interface osgeo\nWriting a custom function to export contour coordinates generated by, e.g., matplotlib or skimage\n\nWe demonstrate the first approach, using gdal_contour. Although we deviate from the Python-focused approach towards more direct interaction with GDAL, the benefit of gdal_contour is the proven algorithm, customized to spatial data, and with many relevant options. Both the gdal_contour program (along with other GDAL programs) and its osgeo Python wrapper, should already be installed on your system since GDAL is a dependency of rasterio. Using the command line pathway, generating 50 \\(m\\) contours of the dem.tif file can be done as follows.\n\nos.system('gdal_contour -a elev data/dem.tif output/dem_contour.gpkg -i 50.0')\n\nLike all GDAL programs (also see gdaldem example in Section 3.3.4), gdal_contour works with files. Here, the input is the data/dem.tif file and the result is exported to the output/dem_contour.gpkg file.\nTo illustrate the result, let’s read the resulting dem_contour.gpkg layer back into the Python environment. Note that the layer contains an attribute named 'elev' (as specified using -a elev) with the contour elevation values.\n\ncontours1 = gpd.read_file('output/dem_contour.gpkg')\ncontours1\n\n\n\n\n\n\n\n\nID\nelev\ngeometry\n\n\n\n\n0\n0\n750.0\nLINESTRING (795382.355 8935384....\n\n\n1\n1\n800.0\nLINESTRING (795237.703 8935384....\n\n\n2\n2\n650.0\nLINESTRING (798098.379 8935384....\n\n\n...\n...\n...\n...\n\n\n29\n29\n450.0\nLINESTRING (795324.083 8931774....\n\n\n30\n30\n450.0\nLINESTRING (795488.616 8931774....\n\n\n31\n31\n450.0\nLINESTRING (795717.42 8931774.8...\n\n\n\n\n32 rows × 3 columns\n\n\n\nFigure 5.11 shows the input raster and the resulting contour layer.\n\nfig, ax = plt.subplots()\nrasterio.plot.show(src_dem, ax=ax)\ncontours1.plot(ax=ax, edgecolor='black');\n\n\n\n\n\n\n\nFigure 5.11: Contours of the dem.tif raster, calculated using the gdal_contour program",
+    "text": "5.5 Spatial vectorization\nSpatial vectorization is the counterpart of rasterization (Section 5.4). It involves converting spatially continuous raster data into spatially discrete vector data such as points, lines, or polygons. There are three standard methods to convert a raster to a vector layer, which we cover next:\n\nRaster to polygons (Section 5.5.1)—converting raster cells to rectangular polygons, representing pixel areas\nRaster to points (Section 5.5.2)—converting raster cells to points, representing pixel centroids\nRaster to contours (Section 5.5.3)\n\nLet us demonstrate all three in the given order.\n\n5.5.1 Raster to polygons\nThe rasterio.features.shapes gives access to raster pixels as polygon geometries, along with the associated raster values. The returned object is a generator (see note in Section 3.3.1), yielding geometry,value pairs.\nFor example, the following expression returns a generator named shapes, referring to the pixel polygons.\n\nshapes = rasterio.features.shapes(rasterio.band(src_grain, 1) )\nshapes\n\n&lt;generator object shapes at 0x7f240b533cd0&gt;\n\n\nWe can generate all shapes at once into a list named pol with list(shapes).\n\npol = list(shapes)\n\nEach element in pol is a tuple of length 2, containing the GeoJSON-like dict—representing the polygon geometry and the value of the pixel(s) which comprise the polygon. For example, here is the first element of pol.\n\npol[0]\n\n({'type': 'Polygon',\n  'coordinates': [[(-1.5, 1.5),\n    (-1.5, 1.0),\n    (-1.0, 1.0),\n    (-1.0, 1.5),\n    (-1.5, 1.5)]]},\n 1.0)\n\n\n\n\n\n\n\n\nNote\n\n\n\nNote that, when transforming a raster cell into a polygon, five-coordinate pairs need to be kept in memory to represent its geometry (explaining why rasters are often fast compared with vectors!).\n\n\nTo transform the list coming out of rasterio.features.shapes into the familiar GeoDataFrame, we need few more steps of data reshaping. First, we apply the shapely.geometry.shape function to go from a list of GeoJSON-like dicts to a list of shapely geometry objects. The list can then be converted to a GeoSeries (see Section 1.2.6).\n\ngeom = [shapely.geometry.shape(i[0]) for i in pol]\ngeom = gpd.GeoSeries(geom, crs=src_grain.crs)\ngeom\n\n0     POLYGON ((-1.5 1.5, -1.5 1, -1 ...\n1     POLYGON ((-1 1.5, -1 1, -0.5 1,...\n2     POLYGON ((-0.5 1.5, -0.5 1, 0 1...\n                     ...                \n11    POLYGON ((0 -0.5, 0 -1, -0.5 -1...\n12    POLYGON ((0.5 -1, 0.5 -1.5, 1 -...\n13    POLYGON ((1 -1, 1 -1.5, 1.5 -1....\nLength: 14, dtype: geometry\n\n\nThe values can also be extracted from the rasterio.features.shapes result and turned into a corresponding Series.\n\nvalues = [i[1] for i in pol]\nvalues = pd.Series(values)\nvalues\n\n0     1.0\n1     0.0\n2     1.0\n     ... \n11    2.0\n12    0.0\n13    2.0\nLength: 14, dtype: float64\n\n\nFinally, the two can be combined into a GeoDataFrame, hereby named result.\n\nresult = gpd.GeoDataFrame({'value': values, 'geometry': geom})\nresult\n\n\n\n\n\n\n\n\nvalue\ngeometry\n\n\n\n\n0\n1.0\nPOLYGON ((-1.5 1.5, -1.5 1, -1 ...\n\n\n1\n0.0\nPOLYGON ((-1 1.5, -1 1, -0.5 1,...\n\n\n2\n1.0\nPOLYGON ((-0.5 1.5, -0.5 1, 0 1...\n\n\n...\n...\n...\n\n\n11\n2.0\nPOLYGON ((0 -0.5, 0 -1, -0.5 -1...\n\n\n12\n0.0\nPOLYGON ((0.5 -1, 0.5 -1.5, 1 -...\n\n\n13\n2.0\nPOLYGON ((1 -1, 1 -1.5, 1.5 -1....\n\n\n\n\n14 rows × 2 columns\n\n\n\nThe polygon layer result is shown in Figure 5.8.\n\nresult.plot(column='value', edgecolor='black', legend=True);\n\n\n\n\n\n\n\nFigure 5.8: grain.tif converted to a polygon layer\n\n\n\n\n\nAs highlighted using edgecolor='black', neighboring pixels sharing the same raster value are dissolved into larger polygons. The rasterio.features.shapes function unfortunately does not offer a way to avoid this type of dissolving. One suggestion is to add unique values between 0 and 0.9999 to all pixels, convert to polygons, and then get back to the original values using np.floor.\n\n\n5.5.2 Raster to points\nTo transform a raster to points, we can use the rasterio.transform.xy function. As the name suggests, the function accepts row and column indices, and transforms them into x- and y-coordinates (using the raster’s transformation matrix). For example, the coordinates of the top-left pixel can be calculated passing the (row,col) indices of (0,0).\n\nsrc = rasterio.open('output/elev.tif')\nrasterio.transform.xy(src.transform, 0, 0)\n\n(np.float64(-1.25), np.float64(1.25))\n\n\n\n\n\n\n\n\nNote\n\n\n\nKeep in mind that the coordinates of the top-left pixel ((-1.25, 1.25)), as calculated in the above expression, refer to the pixel centroid. Therefore, they are not identical to the raster origin coordinates ((-1.5,1.5)), as specified in the transformation matrix, which are the coordinates of the top-left edge/corner of the raster (see Figure 5.9).\n\nsrc.transform\n\nAffine(0.5, 0.0, -1.5,\n       0.0, -0.5, 1.5)\n\n\n\n\nTo generalize the above expression to calculate the coordinates of all pixels, we first need to generate a grid of all possible row/column index combinations. This can be done using np.meshgrid, as follows.\n\nheight = src.shape[0]\nwidth = src.shape[1]\ncols, rows = np.meshgrid(np.arange(width), np.arange(height))\n\nWe now have two arrays, rows and cols, matching the shape of elev.tif and containing the corresponding row and column indices.\n\nrows\n\narray([[0, 0, 0, 0, 0, 0],\n       [1, 1, 1, 1, 1, 1],\n       [2, 2, 2, 2, 2, 2],\n       [3, 3, 3, 3, 3, 3],\n       [4, 4, 4, 4, 4, 4],\n       [5, 5, 5, 5, 5, 5]])\n\n\n\ncols\n\narray([[0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5],\n       [0, 1, 2, 3, 4, 5]])\n\n\nThese can be passed to rasterio.transform.xy to transform the indices into point coordinates, accordingly stored in lists of arrays x and y.\n\nx, y = rasterio.transform.xy(src.transform, rows, cols)\n\n\nx\n\n[array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25]),\n array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25])]\n\n\n\ny\n\n[array([1.25, 1.25, 1.25, 1.25, 1.25, 1.25]),\n array([0.75, 0.75, 0.75, 0.75, 0.75, 0.75]),\n array([0.25, 0.25, 0.25, 0.25, 0.25, 0.25]),\n array([-0.25, -0.25, -0.25, -0.25, -0.25, -0.25]),\n array([-0.75, -0.75, -0.75, -0.75, -0.75, -0.75]),\n array([-1.25, -1.25, -1.25, -1.25, -1.25, -1.25])]\n\n\nTypically we want to work with the points in the form of a GeoDataFrame which also holds the attribute(s) value(s) as point attributes. To get there, we can transform the coordinates as well as any attributes to 1-dimensional arrays, and then use methods we are already familiar with (Section 1.2.6) to combine them into a GeoDataFrame.\n\nx = np.array(x).flatten()\ny = np.array(y).flatten()\nz = src.read(1).flatten()\ngeom = gpd.points_from_xy(x, y, crs=src.crs)\npnt = gpd.GeoDataFrame(data={'value':z}, geometry=geom)\npnt\n\n\n\n\n\n\n\n\nvalue\ngeometry\n\n\n\n\n0\n1\nPOINT (-1.25 1.25)\n\n\n1\n2\nPOINT (-0.75 1.25)\n\n\n2\n3\nPOINT (-0.25 1.25)\n\n\n...\n...\n...\n\n\n33\n34\nPOINT (0.25 -1.25)\n\n\n34\n35\nPOINT (0.75 -1.25)\n\n\n35\n36\nPOINT (1.25 -1.25)\n\n\n\n\n36 rows × 2 columns\n\n\n\nThis ‘high-level’ workflow, like many other rasterio-based workflows covered in the book, is a commonly used one but lacking from the package itself. From the user’s perspective, it may be a good idea to wrap the workflow into a function (e.g., raster_to_points(src), returning a GeoDataFrame), to be re-used whenever we need it.\nFigure 5.9 shows the input raster and the resulting point layer.\n# Input raster\nfig, ax = plt.subplots()\npnt.plot(column='value', legend=True, ax=ax)\nrasterio.plot.show(src_elev, ax=ax);\n# Points\nfig, ax = plt.subplots()\npnt.plot(column='value', legend=True, edgecolor='black', ax=ax)\nrasterio.plot.show(src_elev, alpha=0, ax=ax);\n\n\n\n\n\n\n\n\n\n\n\n(a) Input raster\n\n\n\n\n\n\n\n\n\n\n\n(b) Points\n\n\n\n\n\n\n\nFigure 5.9: Raster and point representation of elev.tif\n\n\n\nNote that ‘No Data’ pixels can be filtered out from the conversion, if necessary (see Section 5.6).\n\n\n5.5.3 Raster to contours\nAnother common type of spatial vectorization is the creation of contour lines, representing lines of continuous height or temperatures (isotherms), for example. We will use a real-world digital elevation model (DEM) because the artificial raster elev.tif produces parallel lines (task for the reader: verify this and explain why this happens). Plotting contour lines is straightforward, using the contour=True option of rasterio.plot.show (Figure 5.10).\n\nfig, ax = plt.subplots()\nrasterio.plot.show(src_dem, ax=ax)\nrasterio.plot.show(\n    src_dem, \n    ax=ax, \n    contour=True, \n    levels=np.arange(0,1200,50), \n    colors='black'\n);\n\n\n\n\n\n\n\nFigure 5.10: Displaying raster contours\n\n\n\n\n\nUnfortunately, rasterio does not provide any way of extracting the contour lines in the form of a vector layer, for uses other than plotting.\nThere are two possible workarounds:\n\nUsing gdal_contour on the command line (see below), or through its Python interface osgeo\nWriting a custom function to export contour coordinates generated by, e.g., matplotlib or skimage\n\nWe demonstrate the first approach, using gdal_contour. Although we deviate from the Python-focused approach towards more direct interaction with GDAL, the benefit of gdal_contour is the proven algorithm, customized to spatial data, and with many relevant options. Both the gdal_contour program (along with other GDAL programs) and its osgeo Python wrapper, should already be installed on your system since GDAL is a dependency of rasterio. Using the command line pathway, generating 50 \\(m\\) contours of the dem.tif file can be done as follows.\n\nos.system('gdal_contour -a elev data/dem.tif output/dem_contour.gpkg -i 50.0')\n\nLike all GDAL programs (also see gdaldem example in Section 3.3.4), gdal_contour works with files. Here, the input is the data/dem.tif file and the result is exported to the output/dem_contour.gpkg file.\nTo illustrate the result, let’s read the resulting dem_contour.gpkg layer back into the Python environment. Note that the layer contains an attribute named 'elev' (as specified using -a elev) with the contour elevation values.\n\ncontours1 = gpd.read_file('output/dem_contour.gpkg')\ncontours1\n\n\n\n\n\n\n\n\nID\nelev\ngeometry\n\n\n\n\n0\n0\n750.0\nLINESTRING (795382.355 8935384....\n\n\n1\n1\n800.0\nLINESTRING (795237.703 8935384....\n\n\n2\n2\n650.0\nLINESTRING (798098.379 8935384....\n\n\n...\n...\n...\n...\n\n\n29\n29\n450.0\nLINESTRING (795324.083 8931774....\n\n\n30\n30\n450.0\nLINESTRING (795488.616 8931774....\n\n\n31\n31\n450.0\nLINESTRING (795717.42 8931774.8...\n\n\n\n\n32 rows × 3 columns\n\n\n\nFigure 5.11 shows the input raster and the resulting contour layer.\n\nfig, ax = plt.subplots()\nrasterio.plot.show(src_dem, ax=ax)\ncontours1.plot(ax=ax, edgecolor='black');\n\n\n\n\n\n\n\nFigure 5.11: Contours of the dem.tif raster, calculated using the gdal_contour program",
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       "<span class='chapter-number'>5</span>  <span class='chapter-title'>Raster-vector interactions</span>"
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