Gist 7dcb0a7f9fae27f177013a8778db59bb

Gist ae4b814c65267e5d9299fbb279a83176

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.

HHugoberry

72% match

Gist fdfc88707da3d0b9b203

This example shows the clustering and connection of points on a map based on their geographic proximity and shared attributes. It uses a force-directed layout via `d3.forceSimulation` to position nodes while links are drawn as curved paths, with a base projection provided by `d3.geoPath` and `d3.geoMercator`. The data is loaded from an external CSV file, and the visualization updates dynamically as the simulation runs, highlighting node neighborhoods and their interconnections.

MMSCAU

72% match

CartoDB Point Clustering

This example demonstrates point clustering on a map using CartoDB, showing how thousands of tornado records can be aggregated into hierarchical groups of different sizes. The visualization uses an SQL query with ST_SnapToGrid to cluster points at multiple scales, dynamically adjusting the cluster radius based on zoom level and a slider control. CartoCSS styles differentiate the cluster sizes, and clicking a point reveals the individual records it contains. The map is embedded with Google Maps and uses CartoDB.js, with an interactive side panel for cluster-size adjustments.

72% match

CartoDB Point Clustering

This example demonstrates point clustering on a map using CartoDB.js, Google Maps, and a custom SQL template. It visualizes tornado data, grouping nearby points into hexagonal clusters of varying sizes based on a user-adjustable "cluster size" slider. The clustering algorithm runs through a SQL query that creates three levels of hexagon grids (large, medium, small) to aggregate points, with each cluster's size and opacity reflecting the number of points it contains. Clicking a cluster reveals details about the original points it represents. The side panel includes a range input to dynamically adjust the cluster radius, which updates the visualization via CartoDB's SQL and CartoCSS templating.

Clustering stylings for CartoDB

This CartoDB map visualizes clustered point data by varying marker width and opacity based on count attributes and zoom levels. It uses CartoCSS styling to define rules that scale marker size with cluster counts, while zoom-dependent conditions control which hierarchy levels appear. The code applies opacity and line styling only when the map reaches specific zoom thresholds, hiding higher-level clusters at closer views. The example demonstrates how CartoDB’s styling language can create interactive, zoom-aware cluster representations without JavaScript libraries.

D3 Marker Clustering w/Unified Mercator & Tile

This example demonstrates a D3 marker clustering visualization that combines a Mercator projection with slippy-map-style tile loading. The map uses OSM tiles rendered from a unified Mercator projection, while markers are drawn on an SVG canvas. A custom force simulation, based on D3's force module, handles clustering by applying a collision force with an absorption rule: when two markers overlap, the larger one absorbs the smaller, growing in radius and count. The underlying tiles and markers stay synchronized through a unified projection and zoom transform, providing a smooth pan-and-zoom experience with dynamic marker merging.

Aandrew-reid

CartoDB Point Clustering

This example demonstrates point clustering on a map using CartoDB, visualizing tornado data with adaptive hexagon grids. The SQL template computes three levels of hexagonal clusters (bigs, mids, smalls) based on point density, while CartoCSS styles the markers with different sizes and labels according to cluster level. As users zoom in, the marker sizes and cluster classifications update dynamically. Clicking a point reveals the number of tornadoes in that cluster, providing an interactive summary of spatial distribution.

Clustering example

This example demonstrates point clustering on a world map using OpenLayers 3. Geographic point data from a local GeoJSON file is loaded into a vector source, then grouped by an `ol.source.Cluster` with a 40-pixel distance threshold. Clusters are rendered as blue circles with white text indicating the number of points in each cluster, while the underlying MapQuest satellite tiles provide geographic context. The style is generated dynamically based on cluster size and cached for performance.

TThomasG77