Gist ae4b814c65267e5d9299fbb279a83176

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Gist ae4b814c65267e5d9299fbb279a83176

✓ Published0🌍 Public

Last edited May 24, 2017

Created on May 24, 2017

This visualization combines a geographical forecast with spatial clustering and outlier detection, showing how raw location data is grouped and analyzed on a map. It uses a custom D3 force simulation to arrange clusters, while the geographic backdrop is rendered through `d3.geoPath` with map projections. The clustering logic relies on KMeans and spatial clustering algorithms from an external library, and outlier detection flags anomalous points via canvas `getImageData` pixel analysis. The code integrates Three.js for enhanced 3D terrain rendering, with transitions driven by `transition.attrTween` to animate cluster movements over time. The data source appears to be simulated forecast points, processed through the clustering pipeline.

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