Second Exercise on Observable: Classification and ColorsAssignment 1GeoG 5540 Final Project ProposalChoropleth Mapping, Assignment 3Urban population density and Air Quality in Florida in 2010
Map of the distribution of families of a certain size in the US in 1930
graduatedSymbols = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
//adding basemap of united states, by using topojason.feature() that read the features of the basemap's objects from the defined basepolygones.
//adding attributes for the basemap: filling color and projection
svg.append("path")
.datum(topojson.feature(basepolygons, basepolygons.objects.States_1930_WGS84))
.attr("fill", "#e7e2e2")
.attr("d", path_basemap);
//adding features of the basemap (states) and defining attributes (filling color, stroke color and shape, projection)
svg.append("path")
.datum(topojson.mesh(basepolygons, basepolygons.objects.States_1930_WGS84, (a, b) => a !== b))
.attr("fill", "none")
.attr("stroke", "gray")
.attr("stroke-linejoin", "round")
.attr("d", path_basemap);
//adding the point feature and joining visual variables (graduate circles) based on the square root of the values.
svg.append("g")
.selectAll("circle")
.data(points.features
.map(d => (d.value = Math.sqrt(d.properties[attributeName]), d))
.sort((a, b) => b.value - a.value))
.join("circle")
.attr("transform", d => `translate(${path_points.centroid(d)})`)
.attr("r", d => radius(d.value))
.attr("fill", d => colors(d.value))
.attr("fill-opacity", 1)
.attr("stroke", "#000")
.attr("stroke-width", 0.5)
//joining the name (id) of each feature with the values to show on the map
.text(d => `${d.properties[idName]} : ${format(d.properties[attributeName])}`);
return svg.node();
}
height = 610
width = 975
path_points = d3.geoPath().projection(projection)
path_basemap = d3.geoPath().projection(projection)
projection = d3.geoAlbers()
// formatting the numbers
format = d3.format(".2s")
radius = d3.scaleThreshold()
.domain(naturalbreaks)
.range(radiusArray)
sizeArrayForLegend = Array.from(radiusArray, d=>Math.PI*Math.pow(d, 2))
radiusArray = [4, 6.5, 8, 10.5, 14]
circleLabels = ["< 161.59", "241.57","322.35","444.69","616.95"]
colors = d3.scaleThreshold()
.domain(naturalbreaks)
.range(BuPu)
BuPu = ["#edf8fb", "#b3cde3", "#8c96c6", "#88419d", "#810f7c"]
naturalbreaks = simple.ckmeans(attribute, 5).map(v => v.pop())
d3.max(attribute)
attribute = Array.from(points.features, d=>Math.sqrt(d.properties[attributeName]))
attributeName = "FamilySize5"
idName = "STATENAM"
//import the point data (geojson)
points = FileAttachment("States_Points.geojson").json()
//import the polygon base map data
basepolygons = FileAttachment("States_1930_WGS84.json").json()
topojson = require("topojson-client@3")
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.
