Relational databases handle geographic data through specialized data types and extensions designed to store and query spatial information. Most modern relational database systems, like PostgreSQL (with PostGIS), MySQL (with spatial extensions), and SQL Server (with geography/geometry types), include built-in support for geographic data. These systems use standardized data formats such as Well-Known Text (WKT) or Well-Known Binary (WKB) to represent points, lines, polygons, and other geometric shapes. For example, a database might store a city’s location as a POINT type with latitude and longitude coordinates or define a regional boundary as a POLYGON with vertices. This approach allows geographic data to fit naturally into table structures, with columns dedicated to spatial features.
To work with geographic data, relational databases provide spatial functions and operators for queries. These include distance calculations (ST_Distance), containment checks (ST_Contains), and intersection tests (ST_Intersects). For instance, a developer could write a query to find all coffee shops within 1 kilometer of a user’s location by converting their coordinates into a POINT, using a spatial function to filter results. Databases also support coordinate reference systems (CRS) like WGS84 (used in GPS) to ensure accurate spatial calculations. For example, PostgreSQL’s PostGIS extension allows specifying a SRID (Spatial Reference System Identifier) like 4326 for WGS84 when storing data, enabling consistent interpretation of coordinates across applications.
Performance is optimized using spatial indexes like R-trees or GiST (Generalized Search Tree) indexes, which accelerate queries involving geographic relationships. Without such indexes, operations like “find all polygons intersecting a given area” would require full table scans, which are inefficient for large datasets. For example, creating a spatial index on a GEOMETRY column in PostGIS allows the database to quickly filter records based on bounding box comparisons. While relational databases aren’t as specialized as dedicated geospatial systems, their integration of spatial extensions makes them practical for applications requiring location-based queries, such as mapping tools or logistics platforms, while leveraging existing SQL skills and infrastructure.