PostGIS
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| PostGIS | |
|---|---|
| Name | PostGIS |
| Developer | PostgreSQL Global Development Group |
| Released | 2001 |
| Programming language | C, SQL |
| Operating system | Linux, Unix-like, Windows |
| Platform | x86, x86-64, ARM |
| Language | English |
| Genre | Spatial database extender |
| License | GNU General Public License |
PostGIS is an open-source spatial database extender that adds geospatial capabilities to the PostgreSQL object-relational database system. It provides spatial types, indexing, and functions used across enterprise locations, municipal mapping, environmental analysis, and web mapping stacks. Widely adopted by government agencies, scientific institutions, and commercial vendors, it integrates with GIS software, web services, and geospatial tooling.
Overview
PostGIS extends PostgreSQL with spatial types and functions compatible with standards from the Open Geospatial Consortium and interoperable with tools such as QGIS, ArcGIS, GeoServer, MapServer, and GDAL. It enables storage and querying of vector geometries and raster data alongside attribute tables used by systems like ESRI, UN-GGIM, and Ordnance Survey. Implementations commonly appear in stacks including Linux, Windows Server, Amazon Web Services, Google Cloud Platform, and Microsoft Azure deployments.
Features
PostGIS offers spatial indexing with R-tree-like structures implemented via GiST and SP-GiST index types, topology models employed by projects like OpenStreetMap, and support for standards such as Simple Features and Well-Known Text. Feature sets include geometry and geography types used by National Aeronautics and Space Administration, European Space Agency, and USGS for remote sensing and cartography. Advanced functions cover distance, area, buffer, intersection, overlay, and reprojection compatible with PROJ and EPSG databases used by Ordnance Survey and IGN.
Architecture and Implementation
PostGIS is implemented as a PostgreSQL extension using C APIs and SQL functions, relying on libraries such as GEOS for geometry operations, PROJ for coordinate transformations, and GDAL for raster and format translation. Integration points include PostgreSQL features like MVCC, WAL, and extension management used in projects such as Debian, Ubuntu, Red Hat Enterprise Linux, and CentOS. Packaging and distribution practices tie into Homebrew, APT, RPM, and container ecosystems like Docker and orchestration via Kubernetes.
Spatial Data Types and Functions
Supported types include two-dimensional and three-dimensional Point, LineString, Polygon, MultiPoint, MultiLineString, and MultiPolygon conforming to OGC Simple Features; geographic types support calculations on ellipsoids used by agencies like NOAA and NGA. Function categories include spatial relationship predicates inspired by DE-9IM and operations implemented in JTS Topology Suite derivatives, used in projects such as GeoTools and Mapnik. PostGIS provides raster types used by Landsat, Sentinel, and MODIS processing pipelines and supports topology management akin to systems created at Ordnance Survey and Australian Bureau of Statistics.
Use Cases and Applications
PostGIS underpins applications ranging from urban planning in municipalities like City of New York and London Borough of Camden to transportation modeling used by Uber, Lyft, and municipal transit agencies. Environmental modeling leverages PostGIS in initiatives by NASA, NOAA, US Fish and Wildlife Service, and WWF for habitat mapping and climate analysis. Utilities and telecom operators such as AT&T and Verizon use PostGIS for asset management; e-commerce and logistics platforms including Amazon and DHL use it for geocoding and routing. Web mapping services combine PostGIS with Leaflet, OpenLayers, Mapbox, and Carto for interactive visualization.
Performance and Scalability
Performance relies on indexing strategies like GiST and SP-GiST, partitioning approaches used in PostgreSQL and cloud-native scaling patterns employed by Amazon RDS, Google Cloud SQL, and Azure Database for PostgreSQL. Large-scale deployments integrate replication features such as Streaming Replication and logical replication used by Heroku and TimescaleDB for time-series integration. Optimization techniques reference query planners and cost models from PostgreSQL core work, parallel query execution researched by PGCon contributors, and tuning approaches described in materials from Percona, EDB, and Crunchy Data.
History and Development
Development began in the early 2000s with contributions from individuals associated with projects like Refraction Research and organizations aligned with PostgreSQL advocacy. The project evolved alongside standards from the Open Geospatial Consortium and libraries such as GEOS and PROJ. Major milestones include integration into packaging ecosystems maintained by Debian GIS, adoption by mapping platforms like OpenStreetMap and inclusion in commercial distributions offered by Esri partners and vendors such as Boundless Spatial (formerly OpenGeo). The community sustains development through events like FOSS4G, conferences attended by contributors from institutions including University of California, Massachusetts Institute of Technology, Imperial College London, and organizations like OSGeo.