height = 525
width = 850
path_points = d3.geoPath().projection(projection)
path_basemap = d3.geoPath().projection(projection)
projection = d3.geoAlbers()
format = d3.format("1")
//proportional symbols
//radius = d3.scaleSqrt([0, d3.max(attribute)], [0, 30])
radius = d3.scaleThreshold()
.domain(naturalbreaks)
.range(radiusArray)
sizeArrayForLegend = Array.from(radiusArray, d=>Math.PI*Math.pow(d, 2))
radiusArray = [8, 10, 14, 19, 26, 30]
circleLabels = Array.from(naturalbreaks.slice(1), d => format(Math.pow(d, 2)))
colors = d3.scaleThreshold()
.domain(naturalbreaks)
.range(Greens)
Greens = ['#edf8fb','#ccece6','#99d8c9','#66c2a4','#2ca25f','#006d2c']
naturalbreaks = simple.ckmeans(attribute, 6).map(v => v.pop())
d3.max(attribute)
attribute = Array.from(points.features, d=>Math.sqrt(d.properties[Percent_Fire_Deparments_RespondingtoWildfires]))
filteredPoints = {
return {
...points,
features: points.features.filter(d => d.properties.states_sim !== "District of Columbia")
};
}
csv_data = d3.csvParse(await FileAttachment("StateF2Pexport-1@1.csv").text(),({Firedepart, Firedepa_2}) => [Firedepart, Firedepa_2])
Percent_Fire_Deparments_RespondingtoWildfires = "Firedepa_2"
idName = "Firedepart"
//import the point data (geojson)
points = FileAttachment("StateF2Pexport.json").json()
//import the polygon base map data
basepolygons = FileAttachment("states_simple.json").json()
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.
