Kepler gl
Powerful open-source geospatial analysis tool.
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Use it free
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Self-host it
Run the open-source version on your own infrastructure.
What is Kepler gl?
Kepler.gl is an open-source geospatial data visualization tool designed to help users explore, analyze, and share spatial data through interactive maps. Its primary purpose is to simplify the process of creating customizable visualizations for datasets with geographic components, such as location-based trends or patterns. Developers, data scientists, and analysts use Kepler.gl to transform raw geospatial data into intuitive visual representations, enabling deeper insights without requiring advanced GIS expertise. The tool addresses challenges in handling large datasets by providing scalable rendering and intuitive interface controls, making it accessible for both technical and non-technical users.
How it works
Kepler.gl is a web-based platform that leverages WebGL for rendering geospatial data, allowing users to visualize datasets on maps with customizable layers and filters. Its purpose is to democratize geospatial analysis by providing a user-friendly interface for exploring spatial relationships, tracking trends, and sharing visualizations across teams. The tool supports real-time rendering of large datasets, enabling users to analyze millions of points or polygons with smooth interactivity. It integrates with popular data sources like CSV, GeoJSON, and Elasticsearch, and offers built-in tools for filtering, clustering, and color-coding data points based on attributes.
How to use it
- 1Access the Kepler.gl web interface via its GitHub-hosted demo or by deploying it locally. 2. Upload a dataset containing geographic coordinates or spatial geometry. 3. Configure visualization settings, such as point size, color scales, and layer interactions. 4. Use built-in tools to filter data, apply clustering algorithms, or overlay additional layers for comparative analysis. Practical tips include pre-processing data to ensure consistent coordinate systems, leveraging the 'Add Layer' feature for multi-dimensional data, and using the 'Export' function to share results with stakeholders.
What it can do
- geospatial analysis
Use cases
Assumptions and limitations
Assumptions
- source: https://kepler.gl/
- license: MIT — free to use
- privacy: Self-hosted — you control your data
Limitations
- Limited built-in statistical analysis tools compared to specialized GIS software
- Requires data to be in supported formats (e.g., CSV, GeoJSON) with proper coordinate systems
- Performance may degrade with extremely large datasets without optimization
- Dependent on internet connectivity for cloud-hosted instances
- Lacks advanced geospatial operations like buffer analysis or spatial joins
Understanding the result
Powerful open-source geospatial analysis tool.
Tool details
- Clearly flagged when a network request is needed.
- No account, no sign-up, and no tracking of your content.
- Powered by (MIT).
- Built with
- (https://kepler.gl/)
- License
- MIT
- Runs locally
- No — requires a network request
- Verification
- Not yet verified
- Input
- Query
- Output
- Text
Built with https://kepler.gl/. OpenToolVault provides the discovery and browser interface while crediting the original project maintainers.
- Built with
- License
- MIT
Open-source project
OpenToolVault is an independent directory. We are not affiliated with or endorsed by this project.
References
- / — GitHub Repository
Upstream project · GitHub
- MIT License
Upstream project
Frequently asked
How does Kepler.gl handle large geospatial datasets?
Kepler.gl uses WebGL for efficient rendering of large datasets, allowing smooth interaction with millions of points or polygons. It employs techniques like data sampling and clustering to maintain performance, though users may need to preprocess data for optimal results.
What data formats are supported by Kepler.gl?
The tool supports common geospatial formats including CSV (with latitude/longitude columns), GeoJSON, TopoJSON, and Elasticsearch indices. It also accepts data from PostGIS databases through specific integration setups.
How can I create a heatmap visualization with Kepler.gl?
To create a heatmap, upload a dataset with latitude, longitude, and a value column. In the 'Add Layer' menu, select 'Heatmap' and configure the color scale based on the value column. Adjust density settings and interactivity options to refine the visualization.
How does Kepler.gl compare to tools like Mapbox or Tableau?
Kepler.gl focuses on geospatial data analysis with built-in tools for clustering and spatial pattern recognition, while Mapbox emphasizes map customization and routing. Tableau offers broader data visualization capabilities but requires GIS plugins for advanced geospatial analysis. Kepler.gl is more lightweight for pure spatial exploration.
What should I do if my data isn't displaying correctly?
First, verify that your dataset contains valid latitude/longitude columns and that coordinates fall within the expected range (-90 to 90 for latitude, -180 to 180 for longitude). Check for missing values or formatting issues in the data. If using a custom coordinate system, ensure it's properly projected or converted to WGS84.