TP 4: Donnees spatiales suite

100%

TP 4: Donnees spatiales suite

✓ Published0🌍 Public

Last edited Dec 1, 2016

Created on Nov 24, 2016

A choropleth map of metropolitan France visualizes regional influenza case counts for November 2014, with each department-colored polygon representing the summed weekly values from the GrippeFrance2014.csv dataset. The visualization uses d3.geo.conicConformal projection and d3.geo.path to render the GeoJSON map, while a d3.scale.quantize color scale encodes case totals from light to dark green. Hovering over a region triggers a tooltip displaying the region name and its aggregated value. Regions without matching data appear gray.

AI-generated description

Built with blockbuilder.org

mit Licensed

Similar vizzes

Loading thumbnail…

TP 4: Donnees spatiales suite

This choropleth map of France visualizes regional influenza case counts for a five-week period in late 2014, using a color scale ranging from light purple to dark green. The map is built with d3.v3, using the `d3.geo.conicConformal` projection and `d3.geo.path` to render GeoJSON boundaries from `carteFrance.json`. Regional values from `GrippeFrance2014.csv` are summed and joined to the geographic features, with missing data shown in gray.

RRomyRatolojanahary

97% match

Loading thumbnail…

TP 4: Donnees spatiales suite

RRomyRatolojanahary

97% match

Loading thumbnail…

TP 4: Donnees spatiales suite

RRomyRatolojanahary

97% match

Loading thumbnail…

TP 4: Donnees spatiales suite

This choropleth map visualizes the geographic distribution of influenza cases across French regions for five weeks in late 2014. The colors, ranging from light purple to dark green, represent the cumulative number of cases per region for that period. Hovering over a region reveals its name and total case count. The map uses `d3.geo.conicConformal()` for projection and `d3.geo.path()` to render geographic boundaries from `carteFrance.json`. Data from `GrippeFrance2014.csv` is merged with the GeoJSON features, and a quantized color scale maps the summed values to the fill colors.

RRomyRatolojanahary

96% match

Loading thumbnail…

TP 4: Donnees spatiales

A choropleth map of France shows the total number of influenza cases reported in 2014 for each region, using a five-color quantized scale ranging from light purple to dark green. The visualization merges data from a CSV file containing weekly case counts with a GeoJSON file of French regional boundaries, assigning the annual sum to each region. Built with D3 v3, the map uses a conic conformal projection and the `d3.geo.path` API to render the regions, with the color domain fixed from 0 to 2000 cases. Regions lacking data appear gray.

RRomyRatolojanahary

94% match

Loading thumbnail…

TP 5: Donnees spatiales

A choropleth map of French regions displays weekly influenza case counts for 2014, with fill colors encoding case numbers and a slider to adjust the displayed week. The visualization loads a GeoJSON file of regional boundaries and a CSV of time-series data, merging them to assign values to each region. Built with D3 v3, it uses `d3.geo.conicConformal` for projection, `d3.geo.path` for rendering, and `d3.scale.quantize` for color mapping. A tooltip shows region names and values on mouse hover.

RRomyRatolojanahary

94% match

Loading thumbnail…

TP 5: Donnees spatiales

A choropleth map of France displays regional influenza case counts over 52 weeks in 2014, using a quantized color scale. The visualization renders the GeoJSON map with `d3.geo.path` and a conic conformal projection. A slider and week label let the user browse the time series, while a tooltip shows the region name and its weekly value. Data comes from a CSV file loaded with `d3.csv` and `queue`.

RRomyRatolojanahary

93% match

Loading thumbnail…

TP 5: Donnees spatiales

A choropleth map of French regions displays weekly influenza case counts for 2014. A slider updates the map to show data for each week, with region colors derived from a quantize scale. The visualization uses D3 v3’s `d3.geo.conicConformal` projection and `d3.geo.path` to render GeoJSON regions, while `d3.csv` loads the time-series data and `d3.queue` manages asynchronous file loading.

RRomyRatolojanahary

92% match