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Data scientists in diverse fields, from marketing to public health to civic hacking, need to work with demographic and socioeconomic data. Government census agencies offer richly detailed, high-quality datasets, but the number of variables and intricacies of administrative geographies (what is a Census tract anyway?) can make approaching this goldmine a daunting process. This course will introduce you to the Decennial Census and the annual American Community Survey, and show you where to find data on household income, commuting, race, family structure, and other topics that may interest you. You will use Python to request this data using the Census API for large and small geographies. You will manipulate the data using pandas, and create derived data such as a measure of segregation. You will also get a taste of the mapping capabilities of geopandas.
Decennial Census of Population and HousingFree
Start exploring Census data products with the Decennial Census. Use the Census API and the requests package to retrieve data, load into pandas data frames, and conduct exploratory visualization in seaborn. Learn about important Census geographies, including states, counties, and tracts.Census Subject Tables50 xpAggregate and Calculate Proportions100 xpCalculate Proportions100 xpIdentify Extreme Values100 xpUsing the Census API50 xpThe Basic API Request100 xpThe API Response and Pandas100 xpAPI to Visualization: Group Quarters100 xpCensus Geographies50 xpSpecific Places100 xpCongressional Districts by State100 xpZip Code Tabulation Areas100 xp
American Community Survey
Explore topics such as health insurance coverage and gentrification using the American Community Survey. Calculate Margins of Error and explore change over time. Create choropleth maps using geopandas.Annual Change50 xpHome Values in California100 xpHealth Insurance Coverage100 xpFinding ACS Tables by Subject50 xpMargins of Error50 xpPlotting Margins of Error over Time100 xpSignificance of Difference of Estimates100 xpSignificance of Difference of Proportions100 xpBasic Mapping with Geopandas50 xpChoropleth Map of Internet Access100 xpProportional Symbol Map of Households w/ Internet100 xpBivariate Map of Broadband Access100 xpNeighborhood Change50 xpIdentifying Gentrifiable Tracts100 xpIdentifying Gentrifying Tracts100 xpMapping Gentrification100 xp
Explore racial segregation in America. Calculate the Index of Dissimilarity, and important measure of segregation. Learn about and use Metropolitan Statistical Areas, and important geography for urban research. Study segregation changes over time in Chicago.Measuring Segregation: The Index of Dissimilarity50 xpCalculating D for One State100 xpCalculating D in a Loop100 xpCalculating D Using Grouping in Pandas100 xpMetropolitan Segregation50 xpJoining Tracts and Metropolitan Areas100 xpCreate Function to Calculate D100 xpCharacteristics of Segregated Metros100 xpSegregation Impacts: Unemployment50 xpCalculating Unemployment100 xpImpacts of Black-White Segregation by Sex100 xpWhite and Black Unemployment100 xpNeighborhood Segregation Over Time50 xpTract Demographics in a Segregated City100 xpSegregation Begets More Segregation100 xpPopulation Decline in Segregated Neighborhoods100 xp
Exploring Census Topics
In this chapter, you will apply what you have learned to four topical studies. Explore unemployment by race and ethnicity; commuting patterns and worker density; immigration and state-to-state population flows; and rent burden in San Francisco.Employment and the Labor Force50 xpUnemployment100 xpLabor Force Participation100 xpCommuting50 xpHeatmap of Travel Times By Commute Mode100 xpWorker Population100 xpMigration50 xpImmigration100 xpState-to-State Flows100 xpIs the Rent Too Damn High?50 xpRent Burden in San Francisco100 xpHigh Rent and Rent Burden100 xpCongratulations!50 xp
DatasetsHispanic Origin & Race by State, 2010Household Internet Access by State, 2017Brooklyn Tract Demographics, 2000Brooklyn Tract Geometries, 2000Brooklyn Tract Demographics, 2010Brooklyn Tract Geometries, 2010
PrerequisitesData Manipulation with pandas
Asst. Professor of Instruction, Temple University
Lee worked in tech and finance before becoming interested in urban inequality. He pursued studies in GIS and urban economic geography, completing his PhD at CUNY Graduate Center. His research interests include local public finance, residential location, segregation, and redistricting. He currently works as Assistant Director of the PSM in GIS at Temple University, where he teaches courses in spatial databases and geospatial programming.