Geocoding and Geo-Aggregation in Kibana

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Kibana is an open-source data visualization and exploration tool, part of the Elastic Stack. It allows users to analyze and visualize data stored in Elasticsearch in real-time. One of the powerful features of Kibana is its ability to work with geospatial data through geocoding and geo-aggregation capabilities.


Geocoding is the process of converting a description of a location, such as an address or place name, into geographic coordinates. In Kibana, geocoding allows a user to plot data points on a map using location information. This enables the user to gain valuable insights by visualizing data on a map.

To geocode data in Kibana, one needs a field that contains location information. This can be a specifically designated geolocation field or a field that contains location data within a document. By enabling geocoding in Kibana, one can transform such fields into geographic coordinates and use them for mapping purposes.

When geocoding, Kibana uses a combination of geocoding services, such as Elasticsearch's built-in GeoIP processor, online mapping services like Google Maps, or offline geocoding databases. It's worth noting that online services may have usage restrictions, while offline geocoding requires locally hosted databases.

Once data is geocoded in Kibana, users can create visualizations like heatmaps, coordinate maps, or region maps to represent the data points on a map. This helps in identifying spatial trends, patterns, or correlations that may not be apparent in traditional tabular data.


Geo-aggregation is the process of aggregating geospatial data based on certain criteria or parameters. In Kibana, geo-aggregation allows users to summarize, group, or filter data based on geographical properties. This helps in getting a better understanding of how data is distributed across different locations or regions.

Kibana provides various types of aggregations that can be applied to geospatial data, such as:

  • Grid Aggregation: Divides a map into a grid and calculates metrics for each cell, allowing the visualization of data density.
  • Geohash Aggregation: Converts geographic coordinates into a geohash grid, enabling the analysis of data at different precision levels.
  • Terms Aggregation: Groups data by specific terms or keywords, allowing the exploration of data based on attributes like city, street, or country.

By combining geo-aggregations with other Elasticsearch aggregations and Kibana visualizations, users can gain deeper insights and answer complex spatial questions from their data.

Use Cases

Geocoding and geo-aggregation in Kibana have a wide range of use cases across industries. Here are a few examples:

  1. Retail: Retail businesses can geocode their stores' addresses and use heatmaps to identify areas with high foot traffic or visualize sales performance across different regions.

  2. Logistics: Logistics companies can geocode delivery addresses to optimize routes and visualize delivery times or display real-time vehicle tracking on a map.

  3. Marketing: Geocoding customer addresses allows marketers to target specific regions and track the effectiveness of location-based campaigns by visualizing customer demographics on a map.

  4. Environmental Analysis: Climate scientists can use geo-aggregations to analyze weather data and identify patterns, such as rainfall distribution or temperature variations across different geographical areas.


Geocoding and geo-aggregation are powerful features of Kibana that enable users to visualize and analyze geospatial data in Elasticsearch. By harnessing these capabilities, businesses and organizations can gain valuable insights, make data-driven decisions, and uncover hidden patterns or trends related to location. With Kibana's intuitive interface and extensive mapping and aggregation options, exploring geospatial data has never been easier.

So, whether you're plotting customer locations, analyzing logistics routes, or studying environmental patterns, Kibana's geocoding and geo-aggregation capabilities can help you make sense of your data in a spatial context.

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