The world is getting smarter day by day and searches based on locations have become an integral part of our daily life. Be it searching for shopping centers, hospitals, restaurants, or any locations, we always look out for information such as distance and other information about the area. Elasticsearch is helpful in combining geo-location data with full-text search, structured search, and also in doing analytics.
In this chapter, we will cover the following topics:
- Introducing geo-spatial data
- Geo-location data types
- Working with geo-point data
- Geo aggregations
- Working with geo-shapes
Geo-spatial data is information of any object on the earth and is presented by numeric values called latitude-longitude (lat-lon) that are presented on geographical systems. Apart from lat-lon, a geo-spatial object also contains other information about that object such as name, size, and shape. Elasticsearch is very helpful when working with such kinds of data. It doesn't only provide powerful geo-location searches, but also has functionalities such as sorting with geo distance, creating geo clusters, scoring based on location, and working with arbitrary geo-shapes.
Elasticsearch has two data types to solely work on geo-spatial data; they are as follows: