Spatial Databases and Geospatial Data Sources

As geospatial datasets grew too large to manage as individual files, GIS practice increasingly turned to spatial databases: relational database management systems extended to store, index, and query geometry alongside conventional tabular data. The most widely used open-source example is PostGIS, an extension for the PostgreSQL database first released in 2001, which adds geometry and geography data types along with hundreds of spatial functions for operations such as distance calculation, intersection testing, and area measurement, all expressed within standard SQL queries. Efficient spatial querying at scale depends on specialized spatial indexing structures, most notably the R-tree, introduced by computer scientist Antonin Guttman in a 1984 paper at UC Berkeley, which organizes geometric objects into a hierarchy of nested bounding rectangles so that a database can quickly narrow a search to only the features near a given location rather than scanning every record. Commercial database vendors offer comparable functionality, including Oracle Spatial and Graph and Microsoft SQL Server's native geography and geometry data types.