diff --git a/01-spatial-data.qmd b/01-spatial-data.qmd
index 3b5dc4d0..6f47bb9b 100644
--- a/01-spatial-data.qmd
+++ b/01-spatial-data.qmd
@@ -730,7 +730,7 @@ Instead, several packages provide alternative subsets of methods for working wit
 The two most notable approaches for working with rasters in Python are provided by **rasterio** and **rioxarray** packages.
 As we will see shortly, they differ in scope and underlying data models.
 Specifically, **rasterio** represents rasters as **numpy** arrays associated with a separate object holding the spatial metadata.
-The **rioxarray** package, a warpper of **rasterio**, however, represents rasters with **xarray** "extended" arrays, which are an extension of **numpy** array designed to hold axis labels and attributes in the same object, together with the array of raster values.
+The **rioxarray** package, a wrapper of **rasterio**, however, represents rasters with **xarray** "extended" arrays, which are an extension of **numpy** array designed to hold axis labels and attributes in the same object, together with the array of raster values.
 Similar approaches are provided by less well-known **xarray-spatial** and **geowombat** packages.
 Comparatively, **rasterio** is more well-established, but it is more low-level (which has both advantabes and distadvantages).