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improve preface #258

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michaeldorman Oct 3, 2024
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Expand Up @@ -7,7 +7,7 @@ We aim to *minimize surprises*, with each section and chapter building on the pr
If you're just starting out with Python for working with geographic data, this book is an excellent place to start.

There are many resources on Python on 'GeoPython' but none that fill this need for an introductory resource that provides strong foundations for future work.
We want to avoid reinventing the wheel and provide something that fills an 'ecological niche' in the wider free and open source software for geospatial (FOSS4G) ecosystem.
We want to avoid reinventing the wheel and provide something that fills an 'ecological niche' in the wider free and open-source software for geospatial (FOSS4G) ecosystem.
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Was going to question this but, having looked it up, I think this is right and good, in open-source software it's an adjective and that seems right: https://learn.microsoft.com/en-us/style-guide/a-z-word-list-term-collections/o/open-source

Key features include:

1. Doing basic operations well
Expand All @@ -16,12 +16,12 @@ Key features include:
<!-- 4. Exercises at the end of each chapter with reproducible and open solutions -->
1. Provision of lucid example datasets and meaningful operations to illustrate the applied nature of geographic research

This book is complementary with, and adds value to, other projects in the ecosystem, as highlighted in the following comparison between *Geocomputation with Python* and related GeoPython books:
This book complements and adds value to other projects in the ecosystem, as highlighted in the following comparison between *Geocomputation with Python* and related GeoPython books:

- *Learning Geospatial Analysis with Python*[^book_1] and *Geoprocessing with Python*[^book_2] are books in this space that focus on processing spatial data using low-level Python interfaces for GDAL, such as the **gdal**, **gdalnumeric**, and **ogr** packages from **osgeo**. This approach requires writing more lines of code. We believe our approach is more "Pythonic" and future-proof, in light of development of packages such as **geopandas** and **rasterio**.
- *Learning Geospatial Analysis with Python*[^book_1] and *Geoprocessing with Python*[^book_2] are books in this space that focus on processing spatial data using low-level Python interfaces for GDAL, such as the **gdal**, **gdalnumeric**, and **ogr** packages from **osgeo**. This approach requires writing more lines of code. We believe our approach is more 'Pythonic' and future-proof, in light of development of packages such as **geopandas** and **rasterio**.
- *Introduction to Python for Geographic Data Analysis*[^book_3] (in progress) seeks to provide a general introduction to 'GIS in Python', with parts focusing on Python essentials, using Python with GIS, and case studies. Compared with this book, which is also open source, and is hosted at pythongis.org, *Geocomputation with Python* has a narrower scope (not covering spatial network analysis, for example) and more coverage of raster data processing and raster-vector interoperability.
- *Geographic Data Science with Python*[^book_4] is an ambitious project with chapters dedicated to advanced topics, with Chapter 4 on Spatial Weights getting into complex topics relatively early, for example.
- *Python for Geospatial Data Analysis*[^book_5] introduces a wide range of approaches to working with geospatial data using Python, including automation of proprietary and open-source GIS software, as well as standalone open source Python packages (which is what we focus on and explain comprehensively in our book). Geocompy is shorter, simpler and more introductory, and cover raster and vector data with equal importance.
- *Python for Geospatial Data Analysis*[^book_5] introduces a wide range of approaches to working with geospatial data using Python, including automation of proprietary and open-source GIS software, as well as standalone open-source Python packages (which is what we focus on and explain comprehensively in our book). Geocompy is shorter, simpler and more introductory, and cover raster and vector data with equal importance.

[^book_1]: [https://www.packtpub.com/product/learning-geospatial-analysis-with-python/9781783281138](https://www.packtpub.com/product/learning-geospatial-analysis-with-python/9781783281138)

Expand All @@ -35,7 +35,7 @@ This book is complementary with, and adds value to, other projects in the ecosys

Another unique feature of the book is that it is part of a wider community.
*Geocomputation with Python* is a sister project of *Geocomputation with R*[^geocompr][@lovelace_geocomputation_2019], a book on geographic data analysis, visualization, and modeling using the R programming language that has 60+ contributors and an active community, not least in the associated Discord group[^geocompr_discord].
Links with the vibrant 'R-spatial' community, and other communities such as GeoRust and JuliaGeo, lead to many opportunities for mutual benefit across open source ecosystems.
Links with the vibrant 'R-spatial' community, and other communities such as GeoRust and JuliaGeo, lead to many opportunities for mutual benefit across open-source ecosystems.

[^geocompr]: [https://r.geocompx.org/](https://r.geocompx.org/)

Expand Down Expand Up @@ -176,12 +176,12 @@ print(result)
# print(filtered_result)
```

## Acknowlegements
## Acknowledgements

We acknowledge Robin Lovelace, Jakub Nowosad, and Jannes Muenchow---authors of *Geocomputation with R* (Robin and Jakub also author the present book), a book on the same topic for a different programming language (R). The structure, topics, and most of the theoretical discussions were adapted from that earlier publication.

We thank the authors of the Python language, and the authors of the **numpy**, **pandas**, **shapely**, **geopandas**, and **rasterio** packages which are used extensively in the book, for building these wonderful tools.

We acknowledge of GitHub users Will Deakin, Sean Gillies, Josh Cole, and Jt Miclat (at the time of writing; full list on GitHub[^github_contributors]) for their contributions during the open-source development of the book.
We acknowledge GitHub users Will Deakin, Sean Gillies, Josh Cole, and Jt Miclat (at the time of writing; full list on GitHub[^github_contributors]) for their contributions during the open-source development of the book.

[^github_contributors]: [https://github.com/geocompx/geocompy/graphs/contributors](https://github.com/geocompx/geocompy/graphs/contributors)
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