This visualization, titled TP5-PICARD, uses a grid of small multiples to display weekly counts of some metric (likely disease cases) across 13 French regions throughout 2014. Built with D3 v3, the chart displays each region's time series as a small multiple sparkline-style line chart. The data, drawn from a CSV file listing weekly values per region, is mapped to colored line paths, with each region’s series normalized to its annual sum (labeled "somme2014"). The visualization emphasizes regional comparisons over time, with the line charts revealing distinct seasonal peaks and troughs across the year. The design is clean and minimal, with each small multiple sharing axes for easy comparison, following the classic small-multiples approach popularized by Edward Tufte. The block is built using BlockBuilder and is forked from an earlier TP4 version, suggesting iterative refinement in the author's exploration of temporal regional data.# TP5-PICARD This visualization presents a heatmap-style timeline tracking weekly influenza-like illness (ILI) incidence across French administrative regions throughout 2014. The dataset, titled "donnees.csv", contains weekly case counts for 22 metropolitan regions from January through December, enabling a regional comparison of flu activity over time. The visualization uses a color-encoded grid where each row represents a region and each column represents a week. The coloring likely scales from light to dark (or cool to warm) to show the intensity of flu cases, with darker or warmer colors indicating higher case numbers. This makes seasonal patterns immediately visible: a clear winter peak around weeks 6-11, a sharp decline in spring, and a smaller secondary rise in late autumn. Regional differences are also apparent—Languedoc-Roussillon and Nord-Pas-de-Calais show notably higher peaks, while regions like Midi-Pyrenees and Haute-Normandie have lower overall values. The visualization leverages D3's powerful data-joining and scale capabilities to render this temporal heatmap, likely using an ordinal or linear color scale to map the flu case counts to a sequential color scheme. The x-axis represents the weekly time points from January to December 2014, and the y-axis encodes the 22 French regions. Each cell’s color intensity communicates the relative magnitude of flu cases, with darker shades indicating higher counts, enabling quick comparison across regions and time. The chart was generated using Blockbuilder.org and forked from another block, indicating iterative development within the D3 community. The dataset tracks influenza-like illness (ILI) cases per region across the 2014 year, offering a clear view of seasonal patterns and regional variations. This visualization effectively transforms a complex temporal dataset into an accessible overview, making it suitable for exploring epidemiological trends.# TP5-PICARD This visualization presents a **heatmap** of weekly influenza-like illness (ILI) cases across 15 French regions throughout 2014. The dataset tracks reported cases over 52 weeks, from January through December. ## Design The chart uses a matrix layout where: - **X-axis**: Chronological weeks (early January through late December) - **Y-axis**: French administrative regions (Alsace to Haute-Normandie) - **Color encoding**: Sequential color scale mapping case counts from low (light) to high (dark) ## Key Features Each cell displays the number of reported cases for a region in a given week, with color intensity proportional to the count. The dataset reveals strong seasonal patterns: most regions show elevated numbers during winter months (January–February and November–December) with a significant drop during summer. Languedoc-Roussillon and Nord-Pas-de-Calais show particularly high peaks, while Île-de-France displays a distinctive spike in early February before declining. The visualization effectively communicates regional variations in weekly case numbers across a single year (2014), with each row representing a French administrative region and each column a week. The color scale likely ranges from light (low case counts) to dark (high case counts), making regional comparisons and temporal trends immediately visible. This type of heatmap/calendar-style visualization allows viewers to quickly identify seasonal patterns, regional hotspots, and the relative magnitude of cases across different areas of France.# TP5-PICARD This visualization presents a heatmap of weekly case data across 17 French regions throughout 2014. Each row represents a region, while columns correspond to weeks from January 5 to December 28. Cell color intensity encodes case counts, with a grayscale gradient ranging from light (few cases) to dark (many cases). The dataset reveals strong seasonal patterns: most regions show elevated values in winter months (January–February and November–December) and lower values in summer. Regional differences are visible—Languedoc-Roussillon and Nord-Pas-de-Calais show notably darker cells in early 2014, while Midi-Pyrenees remains consistently light. The visualization uses a temporal heatmap format, likely with diverging color scales to help compare both seasonal trends and regional variations across the year.