preface.qmd

## Preface {.unnumbered}

**Geocomputation with Python** (*geocompy*) is motivated by the need for an introductory, yet rigorous and up-to-date, resource geographic data with the most popular programming language in the world.
A unique selling point of the book is its cohesive and joined-up coverage of *both vector and raster* geographic data models and consistent learning curve.
We aim to *minimize surprises*, with each section and chapter building on the previous.
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.
Key features include:

1. Doing basic operations well
2. Integration of vector and raster datasets and operations
3. Clear explanation of each line of code in the book to minimize surprises
4. Excercises at the end of each chapter with reproducible and open solutions
5. 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:

- [Learning Geospatial Analysis with Python](https://www.packtpub.com/product/learning-geospatial-analysis-with-python/9781783281138) and [Geoprocessing with Python](https://www.manning.com/books/geoprocessing-with-python) 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](https://gdal.org/api/python_bindings.html) from **osgeo**. 
This approach requires writing more lines of code. 
We believe our approach is more ["Pythonic"](https://rasterio.readthedocs.io/en/latest/intro.html#philosophy) and future-proof, in light of development of packages such as **geopandas** and **rasterio**.
- [Introduction to Python for Geographic Data Analysis](https://pythongis.org/) (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](https://pythongis.org/part3/chapter-11/index.html), for example) and more coverage of raster data processing and raster-vector interoperability.
- [Geographic Data Science with Python](https://geographicdata.science/book/intro.html) is an ambitious project with chapters dedicated to advanced topics, with Chapter 4 on [Spatial Weights](https://geographicdata.science/book/notebooks/04_spatial_weights.html) getting into complex topics relatively early, for example.
Geocompy is shorter, simpler and more introductory, and cover raster and vector data with equal importance (1 to 4).

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](https://r.geocompx.org/), 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](https://discord.gg/PMztXYgNxp).
Links with the vibrant 'R-spatial' community, and other communities such as [GeoRust](https://georust.org/) and [JuliaGeo](https://juliageo.org/), will lead to many opportunities for mutual benefit across open source ecosystems.