Gist 646a839ec3b9e7a2ffa1c7bd7b3c2f34

Skycoin Nodes - 2018/06/10

This interactive map visualizes the global distribution of Skycoin nodes on June 1, 2018, using Mapbox GL to display geographic locations as blue circular markers. Hovering over a marker reveals a popup with details such as node name, description, and coordinates. The map uses a dark basemap and centers on a world view, with node data loaded from a GeoJSON file. The visualization provides an at-a-glance understanding of Skycoin node density and location, while the hover interaction enables quick access to individual node information.

78% match

Gist 6ce6b2a6ecda4579b64f

This visualization uses a Vue.js interface to display a list of city and state locations, likely from a dataset of place names. It shows a static, text-based table of these locations, with no visible map or chart. The code relies on standard DOM rendering through Vue’s template system, without additional data-viz libraries like D3 or Three.js. The rendering approach is a simple list, and the data source appears to be a CSV of location strings. No interactive or dynamic behavior is evident beyond the initial display.

CCaged

72% match

Gist 484101

This visualization shows an interactive network of interconnected nodes, with each node representing a person and links showing connections between them. It reveals the structure of social ties, likely from a GitHub collaboration graph, by grouping related clusters. The rendering uses a canvas-based `d3.forceSimulation` with custom collision and link forces to position nodes, while a custom `d3.geoPath`-style projection maps node positions onto a projected sphere. The code also employs Canvas `getImageData` to detect node boundaries for hover interactions, and uses `transition.attrTween` for smooth link animations. The underlying data is loaded from a Gist, and the visualization runs entirely client-side without external data files.

DDylanFM

70% match

Gist 2a4314be6d43ecda163979127d71caf5

This visualization maps a set of geographic points distributed across downtown Denver, Colorado, using a GeoJSON point layer. It shows the raw spatial clustering of locations, likely representing places or events, without additional thematic encoding. The rendering relies on a mapping library to project and draw the points, while the point data itself is loaded from a GeoJSON feature collection. The code demonstrates a simple, direct approach to displaying raw coordinate data, with no aggregation or styling beyond the base point markers.

Gist 229124535f96c8b9363f191ad4b10942

This map visualizes the geographic distribution of Target store locations across the United States, with each point representing a store. The visualization is built with Vue and uses CSV data containing store names and addresses. Hovering over a point reveals the store name and address, while the map's zoom and pan controls allow users to explore regional clusters. The data is plotted as points on a geographic map, highlighting the density of Target stores in metropolitan areas such as the Northeast corridor, Seattle, and Alabama. The example demonstrates how to bind external CSV data to a Vue component for interactive mapping.

Gist 177f6c8fe1ef0050c1d0d9e490f6f6d5

This example visualizes the centroids of French communes as a particle system, with each point positioned by latitude and longitude from a CSV dataset. Using WebGL rendering within a Vue framework, the visualization employs a force simulation to spatialize the points, while geographic context is provided through a canvas-based path drawing approach. The underlying data includes commune names, INSEE codes, population, and coordinates, allowing the particles to reflect real administrative geography. The rendering leverages d3.forceSimulation for dynamic positioning and canvas getImageData for analyzing pixel density, creating a dense, interactive map-like scatter of population centers.

PPhilippe Rivière

Gist 10676346

This map displays labeled points from a GeoJSON file, plotting locations like "Internet Archive," "China," and "New York" at their geographic coordinates. The visualization likely renders a large-scale point cloud, with overlapping labels suggesting a messy or aggregated dataset. Using a mapping library, likely D3.js with `d3.geoPath` for drawing map features and standard SVG or Canvas techniques for placing text labels, the code creates a simple geographical scatter plot. The rendering emphasizes the spatial distribution of these named places across the globe.

CCliffordAnderson

Gist f0ea84b0d4d50604c405f5b74db1b498

This example visualizes the relationships between a set of JavaScript frameworks and their descriptions using a force-directed graph built with Vue.js and D3’s `d3.forceSimulation`. The graph reads from an inline `category.csv` dataset, mapping each of the three categories—Node.js, Vue.js, and Angular.js—to its respective description. Nodes are rendered as circles, with labels displayed alongside, while the simulation drives their positions through charge and link forces. The visualization demonstrates a compact, interactive network layout, though it does not include drag behavior or hover interactions beyond the static arrangement. The code relies on Vue’s reactivity to update the DOM as the simulation runs.