md`### Heading 3`
format = d => `${d}%`
contig = FileAttachment("ProjectedNRISHP.json").json()
counties = topojson.feature(contig, contig.objects.ProjectedNRISHP)
csv_dataprecipanom7580 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly19751980}) => [+STCOFIPS, +precipanomaly19751980])
csv_dataprecipanom8085 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly19801985}) => [+STCOFIPS, +precipanomaly19801985])
csv_dataprecipanom8590 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly19851990}) => [+STCOFIPS, +precipanomaly19851990])
csv_dataprecipanom9095 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly19901995}) => [+STCOFIPS, +precipanomaly19901995])
csv_dataprecipanom9500 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly19952000}) => [+STCOFIPS, +precipanomaly19952000])
csv_dataprecipanom0005 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly20002005}) => [+STCOFIPS, +precipanomaly20002005])
csv_dataprecipanom0510 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly20052010}) => [+STCOFIPS, +precipanomaly20052010])
csv_dataprecipanom1015 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly20102015}) => [+STCOFIPS, +precipanomaly20102015])
csv_dataprecipanom1520 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly20152020}) => [+STCOFIPS, +precipanomaly20152020])
csv_dataprecipanom2024 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, precipanomaly20202024}) => [+STCOFIPS, +precipanomaly20202024])
csv_datavalue7580 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue19751980}) => [+STCOFIPS, +tempvalue19751980])
csv_datavalue8085 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue19801985}) => [+STCOFIPS, +tempvalue19801985])
csv_datavalue8590 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue19851990}) => [+STCOFIPS, +tempvalue19851990])
csv_datavalue9095 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue19901995}) => [+STCOFIPS, +tempvalue19901995])
csv_datavalue9500 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue19952000}) => [+STCOFIPS, +tempvalue19952000])
csv_datavalue0005 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue20002005}) => [+STCOFIPS, +tempvalue20002005])
csv_datavalue0510 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue20052010}) => [+STCOFIPS, +tempvalue20052010])
csv_datavalue1015 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue20102015}) => [+STCOFIPS, +tempvalue20102015])
csv_datavalue1520 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue20152020}) => [+STCOFIPS, +tempvalue20152020])
csv_datavalue2024 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempvalue20202024}) => [+STCOFIPS, +tempvalue20202024])
csv_dataanom7580 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly19751980}) => [+STCOFIPS, +tempanomaly19751980])
csv_dataanom8085 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly19801985}) => [+STCOFIPS, +tempanomaly19801985])
csv_dataanom8590 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly19851990}) => [+STCOFIPS, +tempanomaly19851990])
csv_dataanom9095 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly19901995}) => [+STCOFIPS, +tempanomaly19901995])
csv_dataanom9500 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly19952000}) => [+STCOFIPS, +tempanomaly19952000])
csv_dataanom0005 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly20002005}) => [+STCOFIPS, +tempanomaly20002005])
csv_dataanom0510 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly20052010}) => [+STCOFIPS, +tempanomaly20052010])
csv_dataanom1015 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly20102015}) => [+STCOFIPS, +tempanomaly20102015])
csv_dataanom1520 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly20152020}) => [+STCOFIPS, +tempanomaly20152020])
csv_dataanom2024 = d3.csvParse(await FileAttachment("ultramaster@2.csv").text(),({STCOFIPS, tempanomaly20202024}) => [+STCOFIPS, +tempanomaly20202024])
precipdataanom7580 = Object.assign(new Map(csv_dataprecipanom7580), {title: "Precipitation Anomaly by County 1975-1980"})
precipdataanom8085 = Object.assign(new Map(csv_dataprecipanom8085), {title: "Precipitation Anomaly by County 1980-1985"})
precipdataanom8590 = Object.assign(new Map(csv_dataprecipanom8590), {title: "Precipitation Anomaly by County 1985-1990"})
precipdataanom9095 = Object.assign(new Map(csv_dataprecipanom9095), {title: "Precipitation Anomaly by County 1990-1995"})
precipdataanom9500 = Object.assign(new Map(csv_dataprecipanom9500), {title: "Precipitation Anomaly by County 1995-2000"})
precipdataanom0005 = Object.assign(new Map(csv_dataprecipanom0005), {title: "Precipitation Anomaly by County 2000-2005"})
precipdataanom0510 = Object.assign(new Map(csv_dataprecipanom0510), {title: "Precipitation Anomaly by County 2005-2010"})
precipdataanom1015 = Object.assign(new Map(csv_dataprecipanom1015), {title: "Precipitation Anomaly by County 2010-2015"})
precipdataanom1520 = Object.assign(new Map(csv_dataprecipanom1520), {title: "Precipitation Anomaly by County 2015-2020"})
precipdataanom2024 = Object.assign(new Map(csv_dataprecipanom2024), {title: "Precipitation Anomaly by County 2020-2024"})
datavalue7580 = Object.assign(new Map(csv_datavalue7580), {title: "Average Temperatures by County 1975-1980"})
datavalue8085 = Object.assign(new Map(csv_datavalue8085), {title: "Average Temperatures by County 1980-1985"})
datavalue8590 = Object.assign(new Map(csv_datavalue8590), {title: "Average Temperatures by County 1985-1990"})
datavalue9095 = Object.assign(new Map(csv_datavalue9095), {title: "Average Temperatures by County 1990-1995"})
datavalue9500 = Object.assign(new Map(csv_datavalue9500), {title: "Average Temperatures by County 1995-2000"})
datavalue0005 = Object.assign(new Map(csv_datavalue0005), {title: "Average Temperatures by County 2000-2005"})
datavalue0510 = Object.assign(new Map(csv_datavalue0510), {title: "Average Temperatures by County 2005-2010"})
datavalue1015 = Object.assign(new Map(csv_datavalue1015), {title: "Average Temperatures by County 2010-2015"})
datavalue1520 = Object.assign(new Map(csv_datavalue1520), {title: "Average Temperatures by County 2015-2020"})
datavalue2024 = Object.assign(new Map(csv_datavalue2024), {title: "Average Temperatures by County 2020-2024"})
dataanom7580 = Object.assign(new Map(csv_dataanom7580), {title: "Temperature Anomaly by County 1975-1980"})
dataanom8085 = Object.assign(new Map(csv_dataanom8085), {title: "Temperature Anomaly by County 1980-1985"})
dataanom8590 = Object.assign(new Map(csv_dataanom8590), {title: "Temperature Anomaly by County 1985-1990"})
dataanom9095 = Object.assign(new Map(csv_dataanom9095), {title: "Temperature Anomaly by County 1990-1995"})
dataanom9500 = Object.assign(new Map(csv_dataanom9500), {title: "Temperature Anomaly by County 1995-2000"})
dataanom0005 = Object.assign(new Map(csv_dataanom0005), {title: "Temperature Anomaly by County 2000-2005"})
dataanom0510 = Object.assign(new Map(csv_dataanom0510), {title: "Temperature Anomaly by County 2005-2010"})
dataanom1015 = Object.assign(new Map(csv_dataanom1015), {title: "Temperature Anomaly by County 2010-2015"})
dataanom1520 = Object.assign(new Map(csv_dataanom1520), {title: "Temperature Anomaly by County 2015-2020"})
dataanom2024 = Object.assign(new Map(csv_dataanom2024), {title: "Temperature Anomaly by County 2020-2024"})
precipanom7580 = Array.from(csv_datavalue7580.values(), d => d[1])
precipanom8085 = Array.from(csv_datavalue8085.values(), d => d[1])
precipanom8590 = Array.from(csv_datavalue8590.values(), d => d[1])
precipanom9095 = Array.from(csv_datavalue9095.values(), d => d[1])
precipanom9500 = Array.from(csv_datavalue9500.values(), d => d[1])
precipanom0005 = Array.from(csv_datavalue0005.values(), d => d[1])
precipanom0510 = Array.from(csv_datavalue0510.values(), d => d[1])
precipanom1015 = Array.from(csv_datavalue1015.values(), d => d[1])
precipanom1520 = Array.from(csv_datavalue1520.values(), d => d[1])
precipanom2024 = Array.from(csv_datavalue2024.values(), d => d[1])
value7580 = Array.from(csv_datavalue7580.values(), d => d[1])
value8085 = Array.from(csv_datavalue8085.values(), d => d[1])
value8590 = Array.from(csv_datavalue8590.values(), d => d[1])
value9095 = Array.from(csv_datavalue9095.values(), d => d[1])
value9500 = Array.from(csv_datavalue9500.values(), d => d[1])
value0005 = Array.from(csv_datavalue0005.values(), d => d[1])
value0510 = Array.from(csv_datavalue0510.values(), d => d[1])
value1015 = Array.from(csv_datavalue1015.values(), d => d[1])
value1520 = Array.from(csv_datavalue1520.values(), d => d[1])
value2024 = Array.from(csv_datavalue2024.values(), d => d[1])
anom7580 = Array.from(csv_dataanom7580.values(), d => d[1])
anom8085 = Array.from(csv_dataanom8085.values(), d => d[1])
anom8590 = Array.from(csv_dataanom8590.values(), d => d[1])
anom9095 = Array.from(csv_dataanom9095.values(), d => d[1])
anom9500 = Array.from(csv_dataanom9500.values(), d => d[1])
anom0005 = Array.from(csv_dataanom0005.values(), d => d[1])
anom0510 = Array.from(csv_dataanom0510.values(), d => d[1])
anom1015 = Array.from(csv_dataanom1015.values(), d => d[1])
anom1520 = Array.from(csv_dataanom1520.values(), d => d[1])
anom2024 = Array.from(csv_dataanom2024.values(), d => d[1])
precipanomcolor = d3.scaleThreshold()
.domain([-50, -25, -15, -10, -5, 5, 10, 20, 30, 40, 50])
.range(["#543005",
"#8c510a",
"#bf812d",
"#dfc27d",
"#f6e8c3",
"#f5f5f5",
"#ccece6",
"#99d8c9",
"#66c2a4",
"#2ca25f",
"#006d2c", "#003c30"])
//more information on sequential scales: https://observablehq.com/@d3/sequential-scales
// color = d3.scaleSequentialQuantile([...data.values()], d3.interpolateBlues)
// color = d3.scaleQuantile()
// .domain(med_age)
// .range()
valuecolor = d3.scaleThreshold()
.domain(30, 35, 40, 45, 50, 55, 60, 65, 70)
.range(["#fff7ec", "#fee8c8", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#b30000", "#7f0000"])
anomcolor = d3.scaleThreshold()
.domain([-1, -0.5, 0, 0.5, 1, 2.5])
.range(["#fef0d9", "#fdd49e", "#fdbb84", "#fc8d59", "#e34a33", "#b30000"])
width = 975
height = 610
margin = 50
//Rotate the map sets the longitude of origin for our UTM Zone 15N projection.
//projection = d3.geoTransverseMercator().rotate([94,0]).fitExtent([[margin, margin], [width, height]], counties);
//d3 reference for projections: https://d3js.org/d3-geo
//use the following url for specific projection settings: https://github.com/veltman/d3-stateplane
//Use this code to set up the map projection (if different than geographic projection)
projection = d3.geoAlbersUsa().fitExtent([[margin, margin], [width - margin, height - margin]], counties)
//projection = d3.geoMercator().fitExtent([[margin, margin], [width - margin, height - margin]], counties)
//Using a path generator to project geometry onto the map
path = d3.geoPath().projection(projection);
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.
