Visualize large point datasets at scale
By: Ryan Baumann
With the new Mapbox GL heatmaps, you can add powerful map data visualizations to your applications with minimal code. Integrating our maps natively keeps your customer data private and allows you to control the entire experience for users. Visualize large point datasets, enable users to identify trends with heatmaps based on any data property, and integrate our heatmaps with other developer tools.
Create heatmaps with millions of points
Create heatmaps from massive telemetry, web traffic, and social media data. Real-time adjustments to style properties like color work with any data source — from live twitter streams, to database queries via an API endpoint, to any vector tile source. Try it yourself with this map of automotive telemetry data across London.
See trends in any data property
With our heatmaps, you can do spatial data aggregations to quickly understand average, minimum, and maximum trends, such as this example of home insurance values and property aggregation. Users can interpret data at the state level down to individual homes. Explore the full map.
Stay within your workflow
Data scientists face many of the same challenges as BI developers, often exporting data into a third-party data visualization tool. Mapbox integrates with tools like Jupyter Notebooks to visualize analysis results all in one place. Whether you prefer Python, Scala, or R for model training and data munging, our tools help you understand and share results quickly.
Have questions? Reach out to sales about building heatmaps into your BI platform or dashboard. Get moving quickly with our GL JS heatmaps tutorial.
Heatmaps for Business Intelligence was originally published in Points of interest on Medium, where people are continuing the conversation by highlighting and responding to this story.