{"version":"1.0","type":"rich","title":"CartoDB Point Clustering","description":"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.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-8675947?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}