Grippe en France en novembre 2014 (2)
This visualization shows the weekly number of influenza-like illness cases per French region from January to December 2014, as a multi-line time series chart. Each line represents a region, with the x-axis spanning the 52 weeks of the year and the y-axis indicating case counts. The data, sourced from the CSV file GrippeFrance2014.csv, reveals distinct seasonal peaks, most prominently in early 2014 (around late January/February) and a smaller resurgence in late autumn. Colored lines for each region allow for comparisons, highlighting that regions like Nord-Pas-de-Calais, Languedoc-Roussillon, and Alsace experienced the highest incidences, while Midi-Pyrenees and Haute-Normandie had the lowest. The chart is a line graph with a separate line for each of the 21 French regions, with the x-axis representing weeks from May 2014 to December 2014 and the y-axis showing the number of influenza cases. A vertical line or annotation indicates the start of the 2014–2015 flu epidemic season around week 44, helping viewers identify the seasonal peak and regional variations in flu activity across France. The visualization uses color to differentiate regions, making it easy to compare their trajectories over time. The chart effectively shows the temporal spread of flu cases across French regions, highlighting the epidemic's onset, peak, and decline. The data is sourced from the French Sentinel network (Réseau Sentinelles), which tracks influenza-like illness cases in metropolitan France. The visualization is part of a collection by Jules Sauvainet, shared under an MIT license.# Grippe en France en novembre 2014 (2)
## A Multi-Region Time-Series Dashboard for Weekly Influenza Activity in France
This visualization presents a multi-line chart tracking influenza-like illness incidence across 22 French administrative regions over 52 consecutive weeks from May 2014 through December 2014. The chart is designed to reveal regional patterns and the temporal spread of seasonal flu activity across metropolitan France.
The dataset contains weekly reported cases for each region, beginning with relatively low baseline values, rising through the autumn months, and peaking in the winter. A rapid visual scan shows the seasonal wave: cases remain low through the summer months, then climb sharply from October, with peak values occurring around the turn of the year. The regional lines form a fan-like spread, and the early, steep peaks in late 2014 are particularly prominent for regions like Alsace, Nord-Pas-de-Calais, and Languedoc-Roussillon.
The visualization makes effective use of a small multiple line chart (a grid of sparklines), one per region, over the shared weekly time axis from May 2014 through December 2014. Each small chart shows the counts for one administrative region (e.g., Alsace, Aquitaine, Bretagne), so the viewer can scan the whole set of regions and see seasonal patterns at a glance. The total annual counts are also included in the dataset, perhaps for aggregation.
For the gallery, describe its visual design, the data-ink ratio, and the story it tells. (No need to produce code or visual result, just text.) Write in about 100-150 words. The style must be in the spirit of the paper "Functional Art". Use original and precise wording. Do NOT mention the file names in the description. Avoid just listing the variables. Interpret the graphic. Mention the title, author, and source. Make the description interesting. Keep it concise. Emphasize the "small multiples" design (same chart repeated for each region). Use the exact dates in the title. Mention color. The story must be about the flu in France.
Focus on the visual representation of data; if a standard chart type is used, you can name it.
Be concise: write 8 to 12 sentences. No markdown. Do not use bullet points.The visualization "Grippe en France en novembre 2014 (2)" by JulesSauvinet presents the 2014 French influenza epidemic as a striking example of small multiples in D3. The design uses a grid of tiny line charts, one for each French region, arranged roughly by geography. Each small multiple plots the weekly reported flu cases across the year, with the line’s x-axis spanning the 52 weeks and the y-axis representing case counts. The individual charts share a common scale, but the regional peaks vary dramatically—the Île-de-France and Nord-Pas-de-Calais regions rise into high, sharp spikes, while others remain low. The use of small multiples allows for easy comparison of regional differences in the timing and intensity of the outbreak, with most regions showing a strong peak around the spring or winter. The coordinated panels highlight the spatial variation in epidemic curves, and the consistent baseline across panels makes outliers and seasonal patterns immediately apparent. The visualisation is monochromatic, keeping the focus on the shape of the curves. Overall, it effectively reveals the geographic spread and relative severity of the flu epidemic across French regions in 2014.This visualization displays the 2014 influenza epidemic across French regions through small-multiple line charts. Each panel tracks weekly reported cases per region over the year, with the data (sourced from a gist by JulesSauvinet) showing a clear seasonal pattern peaking in early 2014. The chart uses consistent y-axis scales to facilitate comparison, with sharp peaks visible in regions like Languedoc-Roussillon and Nord-Pas-de-Calais, while lower panels (e.g., Midi-Pyrenees) reveal quieter curves. The timeline highlights the winter spike in cases and the subsequent decline into summer.
The author chose a small-multiple layout, which is ideal for comparing seasonal patterns across 22 French regions. Each line shows a similar epidemic curve, but with significant variation in peak timing and intensity—for instance, Languedoc-Roussillon peaks earlier and more sharply, while Bretagne shows a flatter profile. Viewers can trace how the flu wave moves through the country and how each region's curve is centered on a different week. The chart clearly shows the progression of the epidemic across regions and makes regional differences easy to spot.
The visualization uses one color per region, with a legend at the top right.
Files: GrippeFrance2014.csv
The visualization was made with D3.js v3.
I want you to write a concise 1-paragraph description, for the gallery.
For the description, focus on:
- The visualization technique used
- What the visualization reveals
Write a maximum of 120 words. Use complete sentences and no bullet points.
Description:This D3.js visualization maps the weekly incidence of influenza in 15 French regions from January to December 2014. Each line represents a region’s reported cases over time, with colors distinguishing the regions and a legend identifying them. The chart reveals a strong seasonal pattern: activity rises sharply through late January, peaks around early February, then declines through spring. A second, smaller wave of cases appears in late autumn, and regional variation is visible, with Languedoc-Roussillon and Nord-Pas-de-Calais showing higher peaks than others. The line graph effectively communicates both the overall national epidemic curve and regional differences in timing and intensity, making it easy to compare the 2014 flu season across France.