Gist b3d6991a9d7ab3de6f7f13e7ed38e097

Gist 1f575522ca61946b31d92bbd146a9dce

This visualization compares French legislative electoral districts between 2012 and 2017, showing how commune boundaries and canton assignments shifted across the two years. It displays two side-by-side maps, likely using Three.js with WebGL rendering to animate or morph between the two configurations. The data comes from CSV files containing commune-to-canton and commune-to-district correspondences for both years. The visualization relies on the Vue framework to manage the interface and handle state transitions, with the geographic comparisons rendered through WebGL for smooth interactive exploration.

71% match

Gist e6f4c2723682e0180ff0bbf59f417beb

This interactive map of French dams displays their locations as points across the country, with each point linking to a detailed information page. The visualization is built from a GeoJSON FeatureCollection of dam properties, including height, reservoir volume, river, and owner. The data was cleaned and enriched using shell scripting and the `jq` command-line tool, with the final map likely rendered using a library such as Leaflet or Mapbox. The code shows the use of `ogrinfo` and `ogr2ogr` for geospatial data processing and SQL queries to transform the attributes.

Gist 207d4ca5cb9d116c81752a3c17d6c24b

This map visualizes Burkina Faso’s administrative polygons, likely distinguishing regions by color or stroke. It uses D3.js to load and render the GeoJSON features with `d3.geoPath` and a geographic projection, displaying the country’s boundaries as vector shapes. The code references a two-polygon dataset and appears suited for choropleth or boundary mapping, with the rendering handled directly via SVG or Canvas.

Gist c8b3e33ae90180e985646e1ef4876dfe

This example demonstrates how to convert tabular JSON data containing longitude and latitude fields into a GeoJSON FeatureCollection using the command-line tool jq, making it ready for map-based visualization. The dataset covers digital mediation locations in the Auvergne-Rhône-Alpes region of France, sourced from data.gouv.fr. The transformation maps each original record into a GeoJSON Feature, preserving properties like name, address, services, and accessibility conditions, while constructing Point geometry from the coordinate pair. The code includes a preview image showing the resulting spatial distribution of these locations, though the visualization itself uses no specific JavaScript mapping library or framework beyond the jq conversion process.

France population

This choropleth map shows the population of French metropolitan and overseas departments, with each region filled with a color corresponding to its total population. A threshold color scale maps population counts to a blue sequential palette, and a legend with an axis explains the color-to-value mapping. The visualization uses D3 v3, TopoJSON, and a queue to load the geographic data from `france.json` and population figures from `population.csv`. Geographic paths are rendered as SVG via `d3.geo.path` and an Albers projection, and coloring is applied by matching department names between the two datasets.

KKcnarf

L’Équateur de Marseille

This interactive map visualizes global landmasses as particle clusters, with each country rendered as a distinct colored region. Built with D3 v4, the visualization combines SVG and canvas rendering to draw geographic outlines from TopoJSON data while animating particles that flow across the map. The visualization, titled "L'Équateur de Marseille," uses a custom d3.geo projection to center on Marseille, France, and animates points along the map's surface to create a dynamic, living effect. The combination of SVG for the base map and canvas for the animated particles results in a smooth, performance-optimized experience that highlights the spatial relationships between geographic features and the overlaid data points.

0007Carto

Gist f1a627d4af91444f3fcaea66c62c8319

A Leaflet map centered on France displays a circular buffer polygon around a point at coordinates (2.18, 46). The example uses proj4js and the reproject library to convert the point from WGS84 to EPSG:3857, then applies jsts.io.GeoJSONReader to read the geometry and create a 40,000-meter buffer via the jsts buffer method. After writing the buffered geometry back to GeoJSON, it reprojects to WGS84 and renders the result as a Leaflet GeoJSON layer on OpenStreetMap tiles, with a scale control and a marker at (0,0).