Relational databases handle geographic data by using a variety of data types and functions tailored for spatial information. Most modern relational database management systems (RDBMS) support geographic data through extensions or built-in features that allow for the storage, retrieval, and manipulation of geographic information. For instance, PostgreSQL has a widely used extension called PostGIS, which adds support for geographic objects, enabling developers to work with coordinates, geometries, and even perform complex spatial queries.
These databases use specific data types to store geographic information, such as points, lines, and polygons. A point could represent a specific location, such as a city or an address, while a polygon could define an area like a country or a park. Developers can create tables that include these spatial data types alongside regular text or number fields, allowing for a seamless integration of geographic data with other application data. For example, a table of parks might include fields for the park's name, size, and a geometry column defining its boundary.
To work with this data, relational databases provide a set of spatial functions and operators that let developers perform various analyses and queries. For instance, they can determine the distance between two geographical points, check whether a point falls within a specific area, or find all parks within a certain radius of a location. These capabilities enhance applications with location-based features, such as geo-fencing or route optimization, making relational databases a powerful tool for handling geographic data in real-world scenarios.