Vis w/ great circles and leaflet q-cluster

Vis with great circle lines and leaflet q-cluster plugin

This example maps loan data from Tuscaloosa, Alabama, and Meridian, Mississippi, using great circle lines to connect points on a geographic map. Built with d3.v3 and CartoDB's Leaflet q-cluster plugin, it visualizes loan balances by plotting each record as a point, with arcs drawn between locations to highlight spatial relationships. The clustered markers use the q-cluster plugin to group nearby points, while the curved lines emphasize geographic connections. Data includes loan amounts, payoff dates, and loan types from the provided JSON.

Aandrewbt

89% match

CartoDB Point Clustering

This example displays point clustering on an interactive map centered on New Orleans, using CartoDB and Leaflet. It shows a sublayer of Twain dataset points filtered by city name, rendered as large blue markers. Clicking a marker queries overlapping points via CartoDB’s SQL API, revealing their count and source values in a side panel. The map uses Stamen toner tiles, while hover interactions change the cursor through CartoDB’s `featureOver` and `featureOut` events.

68% match

CartoDB Point Clustering

This example displays a choropleth map of U.S. ZIP code tabulation areas, colored by the ratio of mortgage disclosures to brokers, with polygon opacity scaled by broker count. It uses CartoDB.js with Leaflet to render the map, an SQL query joining broker data to census geographies, and CartoCSS for the graduated color and opacity styling. The map applies an aggregation stack blur filter to smooth the rendered polygons.

67% match

Leaflet - markercluster

This example demonstrates how Leaflet.markercluster groups hundreds of nearby map points into cluster icons, with each marker’s label bound as a popup. The map loads geographic coordinates from a `clusterPoints.json` file and renders them as clustered markers using the `L.markerClusterGroup` API and its configurable options like `maxClusterRadius` and `spiderfyOnMaxZoom`. Tile layers come from OpenStreetMap, and the Leaflet 0.7 library handles the base map interaction for zooming and panning.

11Cr18Ni9

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.

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.

Group Clustering

This example visualizes clustering patterns in a small dataset where each circle represents an ID with categorical attributes like Location, Group, Class, and Type. Users can click toolbar buttons to group the bubbles by any of these categories, causing the force simulation to reposition clusters around labeled axis points, or return to a single central cluster with “All.” The interactive visualization uses d3.forceSimulation with collide, charge, and positional forces, plus d3.drag for moving individual circles. Data loads from a local CSV file, and circles render as SVG elements with ordinal color scaling.

SSpaceActuary

Gist 7dcb0a7f9fae27f177013a8778db59bb

This interactive map clusters geographic points across multiple zoom levels, showing marker density from state and county summaries down to individual locations. It uses the Supercluster library for hierarchical point clustering and Tilebelt for tile calculations, with PostGIS spatial queries fetching points within tile bounding boxes. Clusters are cached per tile and zoom level, and D3’s max function determines cluster size scaling, while marker data comes from a geocoded node database.

Aakre54