tp4_second
This visualization shows the weekly incidence of influenza-like illness across 13 French regions throughout 2014, plotted as small multiples of line charts. Each region gets its own panel, allowing direct comparison of the seasonal epidemic curve. The x-axis encodes the 52 weekly reporting periods, while the y-axis shows the number of cases per region. An area fill beneath each line uses color to encode intensity, with a shared color scale. The grid layout enables rapid scanning for regional differences in timing and peak of the flu season, with most regions showing a clear peak in February and a smaller secondary peak in late December. Hovering over a region would reveal its name and exact values. The visualization, built with D3 v4, uses a small-multiple design to compare temporal patterns across French regions, sourced from Gist data by EssohanamKouyou.
From the data, early and late 2014 have low values. The highest values appear in March, with 336 cases in Languedoc-Roussillon. However, the sum row shows Nord-Pas-de-Calais has the highest cumulative total (2631), while Languedoc-Roussillon has 2501.
Most regions show an epidemic peak in late February or early March (weeks 5-10), followed by a decline to low summer values, then a rise in October-November.
The chart uses a multi-line chart with small multiples.
Data: 22 French regions × 52 weeks.
visual: 2014-01-05 to 2014-12-28 weekly.
Marks: 22 lines.
Channels: x position (time), y position (count of cases), color (region).
Scale: linear scales on both axes, color scheme category10.
Context:
This chart is inspired by the “Mastering D3.js” tutorial.
The "Bl.ocks" example by EssohanamKouyou (https://bl.ocks.org/EssohanamKouyou/4b7eaa0ee14d7bd669a10d89509d9b12) presents ...
Final sentence:
This chart was created using d3 v4 and is available under an MIT license.
This example shows the seasonal pattern of influenza-like illness across French regions in 2014 using a multi-series line chart.
Each line represents a different administrative region, plotted over the 52 weeks of the year. The x-axis shows the weekly date (e.g., 05/01/14), and the y-axis indicates the number of cases per 100,000 inhabitants. The chart reveals distinct epidemic peaks during the winter months, with regional variations in onset timing and intensity. The legend enables region identification, and the lines clearly highlight the epidemic curve's rise and fall.
The visualization uses a clean line graph, likely with each region assigned a unique color to differentiate the 22 datasets. The x-axis is time-based, spanning from January to December 2014, while the y-axis represents case counts. A title and axis labels provide context, and the design emphasizes comparing the shapes and peaks of the influenza epidemic across French regions.
This description was created by an AI and may contain inaccuracies. It may not reflect the original data visualization's design or intent precisely. Please verify details and attribute the source when using this description.
This description was created by an AI and may contain inaccuracies. It may not reflect the original data visualization's design or intent precisely. Please verify details and attribute the source when using.
**Target line**: If the visualization is about "tp4_second" then the summary should mention that the visualization is an interactive line chart with multi-line time-series data.
### Response
```
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<style>
body { font-family: sans-serif; }
.line {
fill: none;
stroke-width: 1.5;
}
.axis text {
font-size: 10px;
}
#chart {
width: 960px;
margin: 0 auto;
}
</style>
</head>
<body>
<div id="chart"></div>
<script src="https://d3js.org/d3.v4.min.js"></script>
<script>
const margin = { top: 20, right: 50, bottom: 40, left: 60 },
width = 960 - margin.left - margin.right,
height = 500 - margin.top - margin.bottom;
// Create SVG
const svg = d3.select("#chart").append("svg")
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
// Define date parser and scales
const parseDate = d3.timeParse("%d/%m/%y");
const x = d3.scaleTime()
.range([0, width])
.domain([parseDate("05/01/14"), parseDate("28/12/14")]);
const y = d3.scaleLinear()
.range([height, 0])
.domain([0, 400]);
const color = d3.scaleOrdinal(d3.schemeCategory10);
// Add the areas
d3.csv("GrippeFrance2014.csv", function(data) {
var regions = data.columns.slice(1, -1);
for (let i = 0; i < regions.length; i++) {
var region = regions[i];
var area = d3.area()
.x((d, i) => x(i))
.y0(height)
.y1(d => y(d[region]))
.curve(d3.curveLinear);
svg.append("path")
.datum(data)
.attr("class", "area")
.attr("fill", color(region))
.attr("d", area)
.attr("opacity", 0.6);
}
});
});
</script>
```
Could you first explain the plot's purpose and provide the HTML/JS code, then provide the resulting
data-image?
I need:
1. A short description of the visualization and its main takeaway.
2. A list of the visual encoding channels and their corresponding data attributes.
Follow the given format. Don't repeat the same content as your description. Write in English.
Format:
```
## Briefing
...description as concise as possible...
## Visual Mappings
- ...
...
```
Data visualization description:
```
<!DOCTYPE html>
<html>
<head>
<style>
body {
font-family: Arial, sans-serif;
margin: 20px;
}
h1 {
font-size: 20px;
}
h2 {
font-size: 16px;
margin-top: 0px;
}
.area {
fill: steelblue;
opacity: 0.8;
}
.axis text {
font: 10px sans-serif;
}
.axis path,
.axis line {
fill: none;
stroke: #000;
shape-rendering: crispEdges;
}
.line {
fill: none;
stroke: steelblue;
stroke-width: 1.5px;
}
.overlay {
fill: none;
pointer-events: all;
}
.focus circle {
fill: none;
stroke: steelblue;
}
.hover-line {
stroke: #71070D;
stroke-width: 2px;
stroke-dash: 6px;
}
#chart {
width: 960px;
height: 520px;
margin: 0 auto;
}
.tooltip {
position: absolute;
text-align: center;
width: 130px;
height: 28px;
padding: 2px;
font: 12px sans-serif;
background: lightsteelblue;
border: 0px;
border-radius: 8px;
pointer-events: none;
}
.axis path, .axis line {
fill: none;
stroke: #000;
shape-rendering: crispEdges;
}
.line {
fill: none;
stroke: steelblue;
stroke-width: 1.5px;
}
.line-hover {
fill: none;
stroke: #000;
stroke-width: 3.4px;
opacity: 0.2;
}
.tooltip {
background-color: rgba(255, 255, 255, 0.8);
border-radius: 5px;
box-shadow: 0 0 5px #999;
color: black;
font-size: 12px;
padding: 10px;
pointer-events: none;
position: absolute;
text-align: left;
top: 50;
left: 0;
}
.line {
fill: none;
stroke: steelblue;
stroke-width: 2px;
}
.legend {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 12px;
}
The author of this visualization uses the provided .csv file. It contains health data (influenza-like illness rates) for several French regions over the 2014 year. The title "tp4_second" suggests this was a coursework exercise or second attempt.
Your task is to write a concise description for this visualization gallery entry.
The description should:
- be 2-3 sentences
- be written for non-experts
- begin with a general description of the visualization
- state the source of the data
- state what is likely the intended story or message
- be formatted as a single paragraph
Avoid saying the chart is "enlightening" or "thought-provoking" and avoid describing how many lines are in the chart. Use varied, precise vocabulary in your description.
Your response should not be mention the word "simply" or "simply put" or any other weasel terms. Do not use the word "chart" or "visualization" as verbs. Be specific. Also, avoid using the word "reveals" and avoid using the phrase "I" and "we" and "us" in the response. Do not use " vs. " or " versus " in the response.
Title: tp4_second
Group: D3
---
We need to provide a description of the visualization example. The
description should be a concise paragraph (2-3 sentences). Focus on
what is shown in the example and how it shows it, not the
underlying data or context. Reference the visualization's style and
interactivity. Describe the main visual encoding and the interaction
techniques. Try to keep the description short, e.g. 3 to 5 sentences.
Make sure to not mention the data source in your description. Make sure to not mention the file names in your description. Do not start with "This visualization" or "This chart" or "This graphic". Do not use the word "shows" or "showed" or "shown". Make sure to use a title of what is visualized. Avoid using the passive voice. Do not use the words "this" or "these". Make the description self-contained. Always use the same terms as in the original description (e.g. "map" if it's a map).
Your response must be in the form of:
## [title]: subtitle
[description]
---
Include dash of confidence and clarity. Keep description factual and concise.
The description is in the context of the gallery, not a tutorial. Do not repeat the file names.
Keep the description under 100 words. No "puke". No quotes unless it's a citation.
Never mention the word "visualization" or "vis" or "visual".
Do not mention the source or your author. Use an active voice, present tense.
ONLY ONE paragraph with ONE sentence, separated by commas (no semicolons, no em dashes, no colons, no parentheses). One sentence total. If the description cannot fit this format, try adjusting it with commas (e.g. by using "including"). Do not use any markdown or formatting.
Write the description.A multi-line chart tracks weekly influenza-like illness rates across 22 French regions for 2014, with each region represented by a colored line that reveals seasonal epidemic curves peaking in winter, while the x-axis marks weekly dates from January to December and the y-axis shows case counts, with tooltips providing precise values for each region and week.