SWANA population mapBlack and Latino U.S. population sharesRacial makeup of U.S. states and territoriesEducation and income in U.S. countiesLatino population share by U.S. countyU.S. households without Internet accessU.S. households without Internet access by countyCensus Calculator: Are two estimates statistically different?Median age by sex in U.S. states and territoriesDorling cartogram of Iowa county populationsSpike map of Iowa county populations
The demographics and destiny of all 3,220 U.S. counties
data = {
const data = [];
names.forEach(d => {
const race_data = raw_race_data.find(t => t.geoid == d);
const age_data = raw_age_data.find(t => t.geoid == d);
const income_data = raw_income_data.find(t => t.geoid == d);
const education_data = raw_education_data.find(t => t.geoid == d);
const tenure_data = raw_tenure_data.find(t => t.geoid == d);
const cdc_data = raw_cdc_data.get(d) != undefined ? raw_cdc_data.get(d) : [undefined, undefined];
data.push({
"geoid": d,
"Non-white population (%)": ((+race_data.total-race_data.white_alone)/+race_data.total)*100,
"Median household income ($)": +income_data.median,
"Bachelor's degree or higher (%)": (+education_data.total_bachelors_or_higher/+education_data.total)*100,
"Renter-occupied units (%)": (+tenure_data.renter_occupied/+tenure_data.housing_units)*100,
"Median age": +age_data.median,
"Diabetes rate": cdc_data[0]
})
})
return Object.assign(data, {columns:[
"Non-white population (%)",
"Median household income ($)",
"Bachelor's degree or higher (%)",
"Renter-occupied units (%)",
"Median age",
"Diabetes rate"
]});
}
names = raw_income_data.map(d => d.geoid)
raw_cdc_data = Object.assign(new Map(await d3.csv("https://gist.githubusercontent.com/mbostock/74a5eafd839597f6c66a1c1dcb6f499f/raw/1742edce177a3b6d059715d2e04fa1315f23c600/cdc-diabetes-obesity.csv", ({county, diabetes, obesity}) => [county, [+diabetes, +obesity]])), {title: ["Diabetes", "Obesity"]})
raw_tenure_data = await d3.csv("https://raw.githubusercontent.com/datadesk/census-data-downloader/714bf0e5c03572360dd750fe93c0873218351973/data/processed/acs5_2017_tenure_counties.csv")
raw_education_data = await d3.csv("https://raw.githubusercontent.com/datadesk/census-data-downloader/714bf0e5c03572360dd750fe93c0873218351973/data/processed/acs5_2017_education_counties.csv")
raw_income_data = await d3.csv("https://raw.githubusercontent.com/datadesk/census-data-downloader/714bf0e5c03572360dd750fe93c0873218351973/data/processed/acs5_2017_medianhouseholdincome_counties.csv")
raw_age_data = await d3.csv("https://raw.githubusercontent.com/datadesk/census-data-downloader/714bf0e5c03572360dd750fe93c0873218351973/data/processed/acs5_2017_medianage_counties.csv")
raw_race_data = await d3.csv("https://raw.githubusercontent.com/datadesk/census-data-downloader/07bfd86ac885f01115488e4b5b6143e0b5c7d38c/data/processed/acs5_2017_race_counties.csv")
keys = data.columns
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