Modern Python Geospatial Guides
Build reliable geospatial workflows, from raw data to the browser.
Processing, analyzing, and visualizing location data using modern Python geospatial stacks. Every guide pairs architectural reasoning with runnable, copy-paste-ready code — and the CRS discipline that keeps spatial results correct.
The Python geospatial stack spans four connected domains. You ingest and clean data, lean on the core libraries to model it, run analysis and queries at whatever scale the data demands, and finally render the result as an interactive map. These guides follow that arc end to end — projected CRSs for metric work, cloud-native formats for scale, and vector tiles for delivery — so a workflow that starts with a messy Shapefile can finish as a map a stakeholder pans and clicks.
Four guides, one pipeline
Each guide includes architecture advice, practical code, debugging checklists, and diagrams.
Mastering Core Geospatial Python Libraries
GeoPandas, Shapely, PyProj, and Rasterio foundations for reproducible pipelines.
Read the guideGeospatial Data Ingestion & Processing Workflows
Move from raw files to validated, reprojected, cloud-native datasets ready for analysis.
Read the guideSpatial Analysis & Advanced Query Techniques
Indexing, overlays, nearest-neighbor search, clustering, PostGIS, DuckDB, and Dask at scale.
Read the guideWeb Mapping & Interactive Visualization
Folium, MapLibre GL JS, and PMTiles/MBTiles pipelines that put your data in the browser.
Read the guideBrowse every topic
Deep links straight to the guides, grouped by domain.