Geospatial search in Elasticsearch refers to the ability to search and analyze geospatial data, which is data that has a location or geographic component. Elasticsearch provides powerful geospatial search capabilities that enable users to perform complex queries and aggregations on geospatial data, such as points, lines, polygons, and multi-polygons.
Here are some of the key features of geospatial search in Elasticsearch:
1. GeoJSON support: Elasticsearch supports GeoJSON, which is a popular format for representing geospatial data in JSON format.
2. Geo-shape data types: Elasticsearch provides several geo-shape data types, such as point, line, polygon, and multi-polygon, that can be used to represent geospatial data.
3. Geo-distance queries: Elasticsearch provides geo-distance queries that enable you to search for documents based on their proximity to a specific location or within a specific distance.
4. Geo-bounding box queries: Elasticsearch provides geo-bounding box queries that enable you to search for documents that fall within a specific rectangular area.
5. Geo-aggregations: Elasticsearch provides geo-aggregations that enable you to perform aggregations on geospatial data, such as grouping documents by geographic region or calculating the average distance between points.
6. Integration with mapping tools: Elasticsearch provides integration with mapping tools such as Kibana and Leaflet, which enables you to visualize and interact with geospatial data in real-time.
Overall, geospatial search in Elasticsearch enables users to perform powerful and flexible queries and aggregations on geospatial data, and gain insights and value from location-based information. By leveraging the power of geospatial search, organizations can optimize their operations and improve their business outcomes by making data-driven decisions based on location-based insights.