New York traffic is worse than you thought. How to analyze and visualize geo-data with the Elastic Stack.
You may already use Elasticsearch (“You know, for search”). Perhaps you have used it as an open source solution for large scale log analytics. You might not know that Elasticsearch, together with its companion software Kibana, Logstash, and Beats, also serves as a full-featured Geographic Information System (GIS). The Elastic Stack can easily be used as a stand-alone solution and as a complement to your existing GIS-infrastructure.
In this presentation, we will build an end-user solution from the ground up to analyze traffic accidents in New York City.
This hands-on demo will cover:
- indexing geo-data into Elasticsearch using Logstash
- modeling do’s and don’ts of your geo-data for efficient storage and retrieval
- using the search APIs to combine geo-based queries with full text search
- using Kibana to visualize your spatial data from Elasticsearch
- integrating 3rd party data and map services into Kibana
- using the built-in tile mapping services
This presentation will be particularly useful for:
- Developers who need to integrate spatial search into their applications
- Elastic Stack users who are looking for an in-depth look into the geo-capabilities of the stack
- GIS analysts who look to add full-text search to their current workflows