Geostack Spatial Analytics Library

Challenge

Every real estate analysis project required rebuilding the same geospatial infrastructure: database setup, Census data downloads, shapefile processing, and drive-time calculations. This duplication wasted time and created inconsistencies across projects.

Solution

Built a pip-installable Python library that centralizes all geospatial analytics capabilities, enabling other projects to focus on analysis rather than infrastructure.

• PostGIS database management with automated schema migrations and spatial indexing
• Census Bureau and ACS data API client with local caching for offline analysis
• TIGER/Line shapefile downloader with automatic database loading for tract boundaries and road networks
• Drive-time isochrone generation using OSRM routing engine for market area delineation
• CLI tools for common tasks: downloading shapefiles, checking database status, loading spatial data

Impact

• Eliminated 80% of setup time for new spatial analysis projects through shared infrastructure
• Consistent spatial reference data across all real estate analyses (same tract boundaries, same demographics)
• Other projects install via pip install -e and immediately access PostGIS capabilities
• Single source of truth for Census data, preventing version drift across analyses