md`# Interpretation of Patterns
I was hoping for significant differences between the median ages of men and women for each county, that doesn't seem to be the case here, but the map tells a story nonetheless. There are some clear patterns from which we can make several inferences. Firstly, the nothernmost quarter of counties have a higher median age of men and women than most other counties. These counties are mostly rural with lower populations and lack major cities. Besides the center of the state, which roughly has a medium-high median age, most other counties tend to have low median ages of both men and women. There is little to no variance between the median ages of men and women for every county except 8. Of these 8 counties, 5 of them contain older females on average compared to the 3 which contain older males. The 5 counties with higher median ages of females than males, have a larger difference between the ages of men and women than the counties with higher median ages of males than females. Thus we can conclude that the median age for females is just slightly higher than that of males when looking at Wisconsin counties. However it should be noted that counties with a smaller sample size have increased room for error.`
chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append(legend)
.attr("transform", "translate(670,250)");
svg.append("g")
.selectAll("path")
.data(topojson.feature(polygons, polygons.objects.newWI).features)
.join("path")
.attr("fill", d => color(data.get(d.properties[idAttribute])))
.attr("d", path)
.append("title")
.text(d => `${d.properties[idAttribute]}, ${format(data.get(d.properties[idAttribute]))}`);
return svg.node();
}
// http://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/
schemes = [
{
name: "RdBu",
colors: [
"#e8e8e8", "#e4acac", "#c85a5a",
"#b0d5df", "#ad9ea5", "#985356",
"#64acbe", "#627f8c", "#574249"
]
},
{
name: "BuPu",
colors: [
"#e8e8e8", "#ace4e4", "#5ac8c8",
"#dfb0d6", "#a5add3", "#5698b9",
"#be64ac", "#8c62aa", "#3b4994"
]
},
{
name: "GnBu",
colors: [
"#e8e8e8", "#b5c0da", "#6c83b5",
"#b8d6be", "#90b2b3", "#567994",
"#73ae80", "#5a9178", "#2a5a5b"
]
},
{
name: "PuOr",
colors: [
"#e8e8e8", "#e4d9ac", "#c8b35a",
"#cbb8d7", "#c8ada0", "#af8e53",
"#9972af", "#976b82", "#804d36"
]
}
]
labels = ["low", "", "high"]
n = Math.floor(Math.sqrt(colors.length))
x = d3.scaleQuantile(Array.from(data.values(), d => d[0]), d3.range(n))
y = d3.scaleQuantile(Array.from(data.values(), d => d[1]), d3.range(n))
path = d3.geoPath().projection(projection)
//Rotate the map sets the longitude of origin for our UTM projection.
projection = d3.geoTransverseMercator().rotate([87,0]).fitExtent([[10, 10], [width, height]], polygon_features);
polygon_features = topojson.feature(polygons, polygons.objects.newWI)
data = Object.assign(new Map(d3.csvParse(await FileAttachment("Wisconsin.csv").text(), ({FIPS, MED_AGE_F, MED_AGE_M, POP2010}) => [FIPS, [+MED_AGE_M, +MED_AGE_F]])), {title: ["Median Female Age", "Median Male Age"]})
idAttribute = "FIPS"
polygons = FileAttachment("WisExport.json").json()
height = 1200
width = 775
legend = () => {
const k = 24;
const arrow = DOM.uid();
return svg`<g font-family=sans-serif font-size=10>
<g transform="translate(-${k * n / 2},-${k * n / 2}) rotate(-45 ${k * n / 2},${k * n / 2})">
<marker id="${arrow.id}" markerHeight=10 markerWidth=10 refX=6 refY=3 orient=auto>
<path d="M0,0L9,3L0,6Z" />
</marker>
${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg`<rect width=${k} height=${k} x=${i * k} y=${(n - 1 - j) * k} fill=${colors[j * n + i]}>
<title>${data.title[0]}${labels[j] && ` (${labels[j]})`}
${data.title[1]}${labels[i] && ` (${labels[i]})`}</title>
</rect>`)}
<line marker-end="${arrow}" x1=0 x2=${n * k} y1=${n * k} y2=${n * k} stroke=black stroke-width=1.5 />
<line marker-end="${arrow}" y2=0 y1=${n * k} stroke=black stroke-width=1.5 />
<text font-weight="bold" dy="0.71em" transform="rotate(90) translate(${n / 2 * k},6)" text-anchor="middle">${data.title[0]}</text>
<text font-weight="bold" dy="0.71em" transform="translate(${n / 2 * k},${n * k + 6})" text-anchor="middle">${data.title[1]}</text>
</g>
</g>`;
}
color = {
return value => {
if (!value) return "#ccc";
let [a, b] = value;
return colors[y(b) + x(a) * n];
};
}
formatNum = d3.format(".1f")
format = (value) => {
if (!value) return "N/A";
let [a, b] = value;
return `${a} ${data.title[0]}${labels[x(a)] && ` (${labels[x(a)]})`}
${formatNum(b)} ${data.title[1]}${labels[y(b)] && ` (${labels[y(b)]})`}`;
}
topojson = require("topojson-client@3")
d3 = require("d3@5")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
