“At its core, geospatial analysis is a search problem,” Castorani explains.

When you treat location like any other search parameter, such as a timestamp or a keyword, you unlock the ability to analyze millions of data points in mere milliseconds. You gain the ability to search the physical world in real time.

Elastic treats location as a first-class data type alongside text, numbers, dates, IP fields, security events, and logs. Coordinates, polygons, bounding boxes, and spatial relationships can all be queried with the same performance and flexibility as any other data. Data flows in through Logstash at high velocity and volume, stored and indexed for instant retrieval. That means analysts can ask questions like “what vessels crossed this corridor in the last six hours?” or “which assets are within kilometers of this incident?” and get answers immediately.

Elastic augments and enhances traditional GIS tools scaling back end data, providing first phase triage of massive datasets and exposing findings through open APIs for visualization and deeper analysis. Spatial data doesn’t have to be siloed in a specialized datastore. It can live alongside an organization’s other operational data, enabling richer context and more complete answers.

Breaking down these silos also lowers the barrier for who can manage intelligence. “With natural language, you don’t have to learn how the system works or write complex queries — you can just ask questions and get answers,” says Peter Steenberge, GenAI Search Specialist at Elastic.

In time-sensitive environments, people who need answers aren’t necessarily the ones who know how to run complex spatial queries. With Elastic, less technical users can simply ask questions in natural language and instantly access insights across geospatial and telemetry data without requiring deep technical expertise or training.

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Articles by Parker Hornstein

Senior Solutions Architect, Elastic

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