DV project

100%

✓ Published0🌍 Public

Last edited Jan 6, 2018

Created on Jan 6, 2018

This visualization displays road traffic accidents in France during 2016 using an interactive Leaflet map. Accident locations are plotted as markers that dynamically cluster based on zoom level, with cluster sizes indicated by color-coded circles. The map includes a semi-transparent mask layer highlighting the French border polygon. The code uses Leaflet with the Leaflet.markercluster plugin for clustering, D3.js (v4) to load and parse the CSV data, and integrates a Vue.js framework. Accident coordinates are validated against the country boundary before being added to the cluster group.

AI-generated description

Built with blockbuilder.org

forked from <a href='http://bl.ocks.org/lsoudade/'>lsoudade</a>'s block: <a href='http://bl.ocks.org/lsoudade/dc85c22351cb3392b353f687cd9ccc50'>D3 project</a>

forked from <a href='http://bl.ocks.org/ElisaDnr/'>ElisaDnr</a>'s block: <a href='http://bl.ocks.org/ElisaDnr/bc8dfc919e61f7768aeae7fbf1b8adb7'>D3 project</a>

forked from <a href='http://bl.ocks.org/anonymous/'>anonymous</a>'s block: <a href='http://bl.ocks.org/anonymous/6a67211c1fb22919aa8a8b9dd6e233b8'>D3 project</a>

mit Licensed

Similar vizzes

Loading thumbnail…

DV project

This visualization displays road accident locations in France for 2016 on an interactive Leaflet map. The code plots markers from a CSV dataset containing latitude and longitude coordinates, using the PruneCluster library to cluster nearby points for performance. D3.js handles CSV parsing, while a Leaflet mask layer outlines the country boundary. The map supports zooming and panning with OpenStreetMap tiles, allowing users to explore accident density across regions and switch between clustered and individual marker views. The animation refers to the responsive map interactions as the view changes.

EElisaDnr

96% match

Loading thumbnail…

DV project

This interactive map visualizes traffic accident data from a 2016 French CSV file. It uses Leaflet to display OpenStreetMap tiles as the base layer, with accident locations plotted as markers. The map employs Leaflet's MarkerCluster plugin to dynamically group nearby accident points, visually representing density with colored clusters (green, yellow, and red) based on the number of incidents in an area. The visualization is built with D3.js (v4) for data loading and parsing, while the map interface and clustering functionality are handled by Leaflet and its markercluster extension.

EElisaDnr

96% match

Loading thumbnail…

DV project

An interactive map displays traffic accident locations across France, using color-coded markers to represent the density of incidents in clustered groups. The visualization leverages the Leaflet mapping library alongside D3.js to load and parse a CSV file containing accident characteristics, including coordinates. A custom Leaflet extension creates dynamic clusters that change size and color based on the number of accidents in a given area. The map also includes a polygon overlay of France to visually confine the data points to the country's borders.

EElisaDnr

95% match

Loading thumbnail…

DV project

This visualization maps road accident data from a 2016 French CSV file, displaying markers on a Leaflet map of France. D3.js loads the data and filters coordinates to show only incidents within the country’s borders. The map uses Leaflet’s marker cluster plugin to group nearby accidents into colored, zoom-dependent clusters. A custom polygon mask overlays the map to visually highlight the French territory, while the underlying tile layer provides geographic context. The implementation combines D3’s CSV parsing, Leaflet’s mapping and clustering APIs, and GeoJSON for the country outline.

663anp3ca

94% match

Loading thumbnail…

D3_Project_BrancheElisa

A French road-accident map visualizes the geographic distribution of 2016 incidents. Using OpenStreetMap tiles as the base layer, the application plots each accident as a marker, and the Leaflet.markercluster library groups nearby markers into color-coded clusters that animate their size and position as the user zooms. The D3.v4 `d3.csv` function parses the local `caracteristiques_2016.csv` data file, extracting latitude and longitude coordinates to place individual accident points on the interactive map.

EElisaDnr

87% match

Loading thumbnail…

Untitled

A bar chart displays traffic accident victim counts for four vehicle types in France in 1958. The chart uses D3 v4 with an SVG rendering. It loads data from a local CSV file via `d3.csv` and maps the categorical vehicle names to an `xScale` (a `d3.scaleBand`) and the numeric quantities to a `yScale` (a `d3.scaleLinear`). Axes are added with `d3.axisBottom` and `d3.axisLeft`, while the bars are appended as SVG `rect` elements with a fixed steelblue fill.

DDavidZheng217

74% match

Loading thumbnail…

DV4

This interactive choropleth map displays weekly influenza case counts across French regions for 2014. A slider lets viewers step through 52 weeks, updating the color of each region via a quantized scale based on the current week’s data. The visualization uses D3 v4 with `d3.geoConicConformal` for the map projection and `d3.geoPath` to render GeoJSON regions, while a tooltip shows exact values on hover. Data comes from a CSV file with per-region weekly counts, which is merged with regional boundaries from a separate JSON file.

AAminaSGitHub

71% match

Loading thumbnail…

Line Chart

This line chart displays the number of road accidents per hour of the day, based on data from a CSV file of accident records. The x-axis encodes time in 15-minute increments over a 24-hour period, while the y-axis shows the accident count. The visualization uses d3.csv to parse the data, d3.timeParse to convert hour strings, and d3.scaleTime, d3.scaleLinear, and d3.line to render the SVG path with a steelblue stroke. The counts are aggregated by sorting and grouping hour values.

EElisaDnr

71% match