Geocomputation with Python, Dorman M., Graser A., Nowosad J., 2025

Geocomputation with Python, Dorman M., Graser A., Nowosad J., 2025.
    
   Geocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python’s capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. 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. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.




The Simple Features standard.
Geometries are the basic building blocks of vector layers. Although the Simple Features standard defines about 20 types of geometries, we will focus on the seven most commonly used types: POINT, LINESTRING, POLYGON, MULTIPOINT, MULTILINESTRING, MULTIPOLYGON and GE0METRYC0LLECTI0N. A useful list of possible geometry types can be found in R’s sf package documentation.

Simple feature geometries can be represented by well-known binary (WKB) and well-known text (WKT) encodings. WKB representations are usually hexadecimal strings easily readable for computers, and this is why GIS software and spatial databases use WKB to transfer and store geometry objects. WKT, on the other hand, is a human-readable text markup description of Simple Features. Both formats are exchangeable, and if we present one, we will naturally choose the WKT representation.

Contents.
Preface
Prerequisites.
Code and sample data.
Software.
Acknowledgments.
Authors.
1. Geographic data in Python.
1.1. Introduction.
1.2. Vector data.
1.2.1. Vector data classes.
1.2.2. Vector layers.
1.2.3. Geometry columns.
1.2.4. The Simple Features standard.
1.2.5. Geometries.
1.2.6. Vector layer from scratch.
1.2.7. Derived numeric properties.
1.3. Raster data.
1.3.1. Using rasterio.
1.3.2. Raster from scratch.
1.4. Coordinate Reference Systems.
1.4.1. Geographic coordinate systems.
1.4.2. Projected coordinate reference systems.
1.4.3. CRS in Python.
1.5. Units.
2. Attribute data operations.
Prerequisites.
2.1. Introduction.
2.2. Vector attribute manipulation.
2.2.1. Vector attribute subsetting.
2.2.2. Vector attribute aggregation.
2.2.3. Vector attribute joining.
2.2.4. Creating attributes and removing spatial information.
2.3. Manipulating raster objects.
2.3.1. Raster subsetting.
2.3.2. Summarizing raster objects.
3. Spatial data operations.
Prerequisites.
3.1. Introduction.
3.2. Spatial operations on vector data.
3.2.1. Spatial subsetting.
3.2.2. Topological relations.
3.2.3. Spatial joining.
3.2.4. Non-overlapping joins.
3.2.5. Spatial aggregation.
3.2.6. Joining incongruent layers.
3.2.7. Distance relations.
3.3. Spatial operations on raster data.
3.3.1. Spatial subsetting.
3.3.2. Map algebra.
3.3.3. Local operations.
3.3.4. Focal operations.
3.3.5. Zonal operations.
3.3.6. Global operations and distances.
3.3.7. Map algebra counterparts in vector processing.
3.3.8. Merging rasters.
4. Geometry operations.
Prerequisites.
4.1. Introduction.
4.2. Geometric operations on vector data.
4.2.1. Simplification.
4.2.2. Centroids.
4.2.3. Buffers.
4.2.4. Affine transformations.
4.2.5. Pairwise geometry-generating operations.
4.2.6. Subsetting vs. clipping.
4.2.7. Geometry unions.
4.2.8. Type transformations.
4.3. Geometric operations on raster data.
4.3.1. Extent and origin.
4.3.2. Aggregation and disaggregation.
4.3.3. Resampling.
5. Raster-vector interactions.
Prerequisites.
5.1. Introduction.
5.2. Raster masking and cropping.
5.3. Raster extraction.
5.3.1. Extraction to points.
5.3.2. Extraction to lines.
5.3.3. Extraction to polygons.
5.4. Rasterization.
5.4.1. Rasterizing points.
5.4.2. Rasterizing lines and polygons.
5.5. Spatial vectorization.
5.5.1. Raster to polygons.
5.5.2. Raster to points.
5.5.3. Raster to contours.
5.6. Distance to nearest geometry.
6. Reprojecting geographic data.
Prerequisites.
6.1. Introduction.
6.2. Coordinate Reference Systems.
6.3. Querying and setting coordinate systems.
6.4. Geometry operations on projected and unprojected data.
6.5. When to reproject?.
6.6. Which CRS to use?.
6.7. Reprojecting vector geometries.
6.8. Reprojecting raster geometries.
6.9. Custom map projections.
7. Geographic data I/O.
Prerequisites.
7.1. Introduction.
7.2. Retrieving open data.
7.3. Geographic data packages.
7.4. File formats.
7.5. Data input (I).
7.5.1. Vector data.
7.5.2. Raster data.
7.6. Data output (0).
7.6.1. Vector data.
7.6.2. Raster data.
8. Making maps with Python.
Prerequisites.
8.1. Introduction.
8.2. Static maps.
8.2.1. Minimal examples.
8.2.2. Styling.
8.2.3. Symbology.
8.2.4. Labels.
8.2.5. Layers.
8.2.6. Basemaps.
8.2.7. Faceted maps.
8.2.8. Exporting.
8.3. Interactive maps.
8.3.1. Minimal example.
8.3.2. Styling.
8.3.3. Layers.
8.3.4. Symbology.
8.3.5. Basemaps.
8.3.6. Exporting.
References
Index



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