PostGIS Geospatial Development for Location-Driven Applications

ArcGIS and QGIS Server are full desktop and server GIS suites with cartographic tooling, licenced data connectors and GUI analysis workflows — appropriate for GIS analysts producing maps. PostGIS is the right choice when your application needs to query, join and manipulate spatial data programmatically at scale, using SQL alongside relational business data, without per-seat licences. Most product teams building fleet management, store-locator, or spatial analytics features are better served by PostGIS than by a dedicated GIS server.

MongoDB's 2dsphere index handles point-proximity and basic polygon queries well for document-centric workloads. PostGIS is the better choice when you need complex spatial operations — multi-step routing, polygon intersection, spatial aggregation, raster analysis, topology — or when location data must join with relational tables (orders, users, inventory) in a single query. If your spatial queries are exclusively "find documents near a point," MongoDB is adequate; if you need a full spatial SQL engine, PostGIS wins on capability and standards compliance.

pgRouting loads the routing graph into memory per query by default, which becomes slow for national-scale networks. Production deployments pre-process topology with pgr_createTopology, build contraction hierarchies (pgr_contraction) to reduce the effective graph size for long-distance queries, partition the network by region to limit graph load, and cache frequent isochrone results in materialised views. With these optimisations, sub-second routing is achievable on networks with tens of millions of edges on mid-range hardware.

GiST indexes on geometry columns store bounding-box hierarchies (R-tree variant). They accelerate &&, ST_Intersects, ST_DWithin, and ST_Contains by filtering candidates to a small bounding-box set before the exact geometry test. They underperform when geometries have wildly different sizes (small points next to country polygons), when table statistics are stale, or when the query returns a large fraction of the table. Partitioning by geometry type and running ANALYZE after bulk loads restores planner accuracy.

We enforce a single canonical SRID (typically EPSG:4326 for storage, EPSG:3857 for display) at the schema level using PostGIS AddGeometryColumn constraints. All inbound data is reprojected at ingestion time via GDAL/OGR or ST_Transform, with SRID validation checks that reject rows with unknown or mismatched CRS. Source SRID and datum metadata are recorded in a spatial_metadata catalogue table so that data provenance is traceable for every geometry column.

PostGIS can serve real-time geofencing when queries are structured correctly: bounding-box pre-filter with && reduces candidates to a small set before the exact ST_Within or ST_DWithin check, and geofence polygons are indexed separately with GiST. For high-frequency telemetry (thousands of events per second), we pair PostGIS with a Redis GEORADIUS hot-path cache — PostGIS remains the authoritative geofence store and is queried for cache misses, boundary updates, and audit queries. TimescaleDB hypertables handle the time-series telemetry append load.

Martin and pg_tileserv are lightweight Rust and Go servers that connect directly to PostGIS and generate Mapbox Vector Tiles (MVT) on demand from geometry tables. We configure tile sources with ST_AsMVT, set zoom-level-dependent ST_Simplify thresholds so high-zoom tiles include full geometry detail while low-zoom tiles are simplified, expose the MVT endpoint behind a CDN (Cloudflare or CloudFront) for caching, and tune PostgreSQL parallel query settings so tile generation uses multiple CPU cores under load.