How do document databases handle geospatial data?

Document databases handle geospatial data by storing coordinates or shapes (like points, lines, or polygons) within documents and providing specialized indexes and query operations to work with this data efficiently. For example, MongoDB uses GeoJSON formats to represent locations as embedded objects in documents, such as { type: "Point", coordinates: [longitude, latitude] }. These databases typically create spatial indexes (like 2dsphere indexes in MongoDB) to optimize queries involving distance, intersections, or containment. This approach allows developers to store geospatial data flexibly alongside other document fields, making it easy to integrate location-based features into applications without requiring separate systems.

To query geospatial data, document databases offer operators tailored for spatial operations. For instance, MongoDB supports queries like $near (to find documents near a specific point), $geoWithin (to find locations inside a polygon), and $geoIntersects (to check if geometries overlap). These operators leverage the spatial indexes to perform calculations efficiently, even at scale. For example, a delivery app could use $near to find drivers within 5 kilometers of a pickup location. Some databases also support geospatial aggregations, like calculating distances between points in query results. These features abstract complex geometric computations, allowing developers to focus on application logic instead of low-level math.

Real-world use cases include location-based services like ride-sharing apps, store locators, or environmental monitoring systems. For example, a retail app might store store locations as GeoJSON points and use $geoWithin to show users which stores are within a selected area on a map. Document databases also handle dynamic geospatial data effectively—such as tracking moving vehicles or updating regional boundaries—because their schema flexibility allows for easy updates. While relational databases often require extensions (like PostGIS for PostgreSQL), document databases bake geospatial features directly into their query language and storage, simplifying implementation for developers building location-aware applications.