Elasticsearch provides several types of geospatial queries that can be used to search and analyze geospatial data. Here are some of the most common types of geospatial queries in Elasticsearch:
1. Geo-distance query: The geo-distance query enables you to search for documents based on their proximity to a specific location or within a specific distance. You can specify the distance in kilometers, miles, or other units.
2. Geo-bounding box query: The geo-bounding box query enables you to search for documents that fall within a specific rectangular area defined by two points, typically the upper-left and lower-right corners of the box.
3. Geo-polygon query: The geo-polygon query enables you to search for documents that fall within a specific polygon defined by a set of points. The polygon can be defined using a GeoJSON object or a set of coordinates.
4. Geo-shape query: The geo-shape query enables you to search for documents that fall within a specific shape, such as a point, a line, a polygon, or a multi-polygon. The shape can be defined using a GeoJSON object or a set of coordinates.
5. Geo-distance aggregation: The geo-distance aggregation enables you to group documents by their proximity to a specific location or within a specific distance, and perform aggregations on the results.
6. Geo-hash grid aggregation: The geo-hash grid aggregation enables you to group documents into a grid of cells based on their location, and perform aggregations on the results. This can be useful for visualizing geospatial data at different zoom levels.
Overall, Elasticsearch provides a wide range of geospatial queries that enable users to search and analyze geospatial data in flexible and powerful ways. By choosing the appropriate geospatial query for the task at hand, organizations can leverage the power of geospatial search to gain insights and value from location-based information.