"""

County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """

A web map displaying a series of Esri Living Atlas feature services added as items that pertain to poverty and Internet and Computer access for Broward County and its Census Tracts. The web map is used to analyze computer and internet access by the Black/African race category and poverty.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

The Black food insecurity rate in the United States was in 2020. Explore a map of black hunger statistics in the United States at the state and local level.

This map shows what country naturalized US citizens were born in using the Charts & Size and Predominance mapping styles. The area with the highest amount of foreign born naturalized US citizens is shown by color. Areas are: Africa, Asia, Europe, Latin America, Northern America, and Oceania.Data are available in 5-year estimates at the state, county, and tract level for the entire US.The data in this map contains the most recent American Community Survey (ACS) data from the U.S. Census Bureau. The Living Atlas layer in this map updates annually when the Census releases their new figures. To learn more, visit this FAQ, or visit the ACS website. Web Map originally owned by Summers Cleary

This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

This map shows median age in the US by country, state, county, tract, and congressional district for 2023. ArcGIS Online account required for use.The pop-up is configured to show median age, median age by sex, child age (under 18) population, senior age (over 65) population, the age dependency ratio, and population by 5 year age increments. Blending is used at the Tract level to highlight areas of human settlement. Congressional district is turned off by default and can be enabled in the Layers pane.Esri 2023 Age Dependency Ratio is the estimated ratio of the child population (Age 0-17) and senior population (Age 65+) to the working-age population (Age 18-64) in the geographic area. This ratio is then multiplied by 100. Higher ratios denote that a greater burden is carried by working-age people. Lower ratios mean more people are working who can support the dependent population. Read more. See Updated Demographics for more information on Esri Demographic variables.Esri Updated Demographics represent the suite of annually updated U.S. demographic data that provides current-year and five-year forecasts for more than two thousand demographic and socioeconomic characteristics, a subset of which is included in this layer. Included are a host of tables covering key characteristics of the population, households, housing, age, race, income, and much more. Esri's Updated Demographics data consists of point estimates, representing July 1 of the current and forecast years.Get started with U.S. Updated DemographicsHow to use and interpret U.S. Updated DemographicsEsri Updated Demographics DocumentationMethodologyEssential Esri Demographics vocabularyThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. This layer requires an ArcGIS Online subscription and does not consume credits. Please cite Esri when using this data. For information about purchasing additional Esri's Updated Demographics data, contact datasales@esri.com. Feedback: we would like to hear from you while this layer is in beta release. If you have any feedback regarding this item or Esri Demographics, please use this survey. Fields available:GEOIDNameState NameState Abbreviation2023 Total Population (Esri)2023 Household Population (Esri)2023 Group Quarters Population (Esri)2023 Population Density (Pop per Square Mile) (Esri)2023 Total Households (Esri)2023 Average Household Size (Esri)2023 Total Housing Units (Esri)2023 Owner Occupied Housing Units (Esri)2023 Renter Occupied Housing Units (Esri)2023 Vacant Housing Units (Esri)2020-2023 Population: Compound Annual Growth Rate (Esri)2020-2023 Households: Compound Annual Growth Rate (Esri)2023 Housing Affordability Index (Esri)2023 Percent of Income for Mortgage (Esri)2023 Wealth Index (Esri)2023 Socioeconomic Status Index (Esri)2023 Generation Alpha Population (Born 2017 or Later) (Esri)2023 Generation Z Population (Born 1999 to 2016) (Esri)2023 Millennial Population (Born 1981 to 1998) (Esri)2023 Generation X Population (Born 1965 to 1980) (Esri)2023 Baby Boomer Population (Born 1946 to 1964) (Esri)2023 Silent & Greatest Generations Population (Born 1945/Earlier) (Esri)2023 Population by Generation Base (Esri)2023 Child Population (Age <18) (Esri)2023 Working-Age Population (Age 18-64) (Esri)2023 Senior Population (Age 65+) (Esri)2023 Child Dependency Ratio (Esri)2023 Age Dependency Ratio (Esri)2023 Senior Dependency Ratio (Esri)2023 Total Population Age 0-4 (Esri)2023 Total Population Age 5-9 (Esri)2023 Total Population Age 10-14 (Esri)2023 Total Population Age 15-19 (Esri)2023 Total Population Age 20-24 (Esri)2023 Total Population Age 25-29 (Esri)2023 Total Population Age 30-34 (Esri)2023 Total Population Age 35-39 (Esri)2023 Total Population Age 40-44 (Esri)2023 Total Population Age 45-49 (Esri)2023 Total Population Age 50-54 (Esri)2023 Total Population Age 55-59 (Esri)2023 Total Population Age 60-64 (Esri)2023 Total Population Age 65-69 (Esri)2023 Total Population Age 70-74 (Esri)2023 Total Population Age 75-79 (Esri)2023 Total Population Age 80-84 (Esri)2023 Total Population Age 85+ (Esri)2023 Median Age (Esri)2023 Male Population (Esri)2023 Median Male Age (Esri)2023 Female Population (Esri)2023 Median Female Age (Esri)2023 Total Population by Five-Year Age Base (Esri)2023 Total Daytime Population (Esri)2023 Daytime Population: Workers (Esri)2023 Daytime Population: Residents (Esri)2023 Daytime Population Density (Pop per Square Mile) (Esri)2023 Civilian Population Age 16+ in Labor Force (Esri)2023 Employed Civilian Population Age 16+ (Esri)2023 Unemployed Population Age 16+ (Esri)2023 Unemployment Rate (Esri)2023 Civilian Population 16-24 in Labor Force (Esri)2023 Employed Civilian Population Age 16-24 (Esri)2023 Unemployed Population Age 16-24 (Esri)2023 Unemployment Rate: Population Age 16-24 (Esri)2023 Civilian Population 25-54 in Labor Force (Esri)2023 Employed Civilian Population Age 25-54 (Esri)2023 Unemployed Population Age 25-54 (Esri)2023 Unemployment Rate: Population Age 25-54 (Esri)2023 Civilian Population 55-64 in Labor Force (Esri)2023 Employed Civilian Population Age 55-64 (Esri)2023 Unemployed Population Age 55-64 (Esri)2023 Unemployment Rate: Population Age 55-64 (Esri)2023 Civilian Population 65+ in Labor Force (Esri)2023 Employed Civilian Population Age 65+ (Esri)2023 Unemployed Population Age 65+ (Esri)2023 Unemployment Rate: Population Age 65+ (Esri)2023 Child Economic Dependency Ratio (Esri)2023 Working-Age Economic Dependency Ratio (Esri)2023 Senior Economic Dependency Ratio (Esri)2023 Economic Dependency Ratio (Esri)2023 Hispanic Population (Esri)2023 White Non-Hispanic Population (Esri)2023 Black/African American Non-Hispanic Population (Esri)2023 American Indian/Alaska Native Non-Hispanic Population (Esri)2023 Asian Non-Hispanic Population (Esri)2023 Pacific Islander Non-Hispanic Population (Esri)2023 Other Race Non-Hispanic Population (Esri)2023 Multiple Races Non-Hispanic Population (Esri)2023 Diversity Index (Esri)2023 Population by Race Base (Esri)2023 Population Age 25+: Less than 9th Grade (Esri)2023 Population Age 25+: 9-12th Grade/No Diploma (Esri)2023 Population Age 25+: High School Diploma (Esri)2023 Population Age 25+: GED/Alternative Credential (Esri)2023 Population Age 25+: Some College/No Degree (Esri)2023 Population Age 25+: Associate's Degree (Esri)2023 Population Age 25+: Bachelor's Degree (Esri)2023 Population Age 25+: Graduate/Professional Degree (Esri)2023 Educational Attainment Base (Pop 25+)(Esri)2023 Household Income less than $15,000 (Esri)2023 Household Income $15,000-$24,999 (Esri)2023 Household Income $25,000-$34,999 (Esri)2023 Household Income $35,000-$49,999 (Esri)2023 Household Income $50,000-$74,999 (Esri)2023 Household Income $75,000-$99,999 (Esri)2023 Household Income $100,000-$149,999 (Esri)2023 Household Income $150,000-$199,999 (Esri)2023 Household Income $200,000 or greater (Esri)2023 Median Household Income (Esri)2023 Average Household Income (Esri)2023 Per Capita Income (Esri)2023 Households by Income Base (Esri)2023 Gini Index (Esri)2023 P90-P10 Ratio of Income Inequality (Esri)2023 P90-P50 Ratio of Income Inequality (Esri)2023 P50-P10 Ratio of Income Inequality (Esri)2023 80-20 Share Ratio of Income Inequality (Esri)2023 90-40 Share Ratio of Income Inequality (Esri)2023 Households in Low Income Tier (Esri)2023 Households in Middle Income Tier (Esri)2023 Households in Upper Income Tier (Esri)2023 Disposable Income less than $15,000 (Esri)2023 Disposable Income $15,000-$24,999 (Esri)2023 Disposable Income $25,000-$34,999 (Esri)2023 Disposable Income $35,000-$49,999 (Esri)2023 Disposable Income $50,000-$74,999 (Esri)2023 Disposable Income $75,000-$99,999 (Esri)2023 Disposable Income $100,000-$149,999 (Esri)2023 Disposable Income $150,000-$199,999 (Esri)2023 Disposable Income $200,000 or greater (Esri)2023 Median Disposable Income (Esri)2023 Home Value less than $50,000 (Esri)2023 Home Value $50,000-$99,999 (Esri)2023 Home Value $100,000-$149,999 (Esri)2023 Home Value $150,000-$199,999 (Esri)2023 Home Value $200,000-$249,999 (Esri)2023 Home Value $250,000-$299,999 (Esri)2023 Home Value $300,000-$399,999 (Esri)2023 Home Value $400,000-$499,999 (Esri)2023 Home Value $500,000-$749,999 (Esri)2023 Home Value $750,000-$999,999 (Esri)2023 Home Value $1,000,000-$1,499,999 (Esri)2023 Home Value $1,500,000-$1,999,999 (Esri)2023 Home Value $2,000,000 or greater (Esri)2023 Median Home Value (Esri)2023 Average Home Value (Esri)2028 Total Population (Esri)2028 Household Population (Esri)2028 Population Density (Pop per Square Mile) (Esri)2028 Total Households (Esri)2028 Average Household Size (Esri)2023-2028 Population: Compound Annual Growth Rate (Esri)2023-2028 Households: Compound Annual Growth Rate (Esri)2023-2028 Per Capita Income: Compound Annual Growth Rate (Esri)2023-2028 Median Household Income: Compound Annual Growth Rate (Esri)2028 Diversity Index (Esri)2028 Median Household Income (Esri)2028 Average Household Income (Esri)2028 Per Capita Income (Esri)

This layer contains a Vermont-only subset of census tract level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, BLOCK, BLKGRP, and TBLKGRP.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual tract level, since this data has been protected using differential privacy.*VCGI exported a Vermont-only subset of the nation-wide layer to produce this layer--with fields limited to this popular subset: OBJECTID: OBJECTID GEOID: Geographic Record Identifier NAME: Area Name-Legal/Statistical Area Description (LSAD) Term-Part Indicator County_Name: County Name State_Name: State Name P0010001: Total Population P0010003: Population of one race: White alone P0010004: Population of one race: Black or African American alone P0010005: Population of one race: American Indian and Alaska Native alone P0010006: Population of one race: Asian alone P0010007: Population of one race: Native Hawaiian and Other Pacific Islander alone P0010008: Population of one race: Some Other Race alone P0020002: Hispanic or Latino Population P0020003: Non-Hispanic or Latino Population P0030001: Total population 18 years and over H0010001: Total housing units H0010002: Total occupied housing units H0010003: Total vacant housing units P0050001: Total group quarters population PCT_P0030001: Percent of Population 18 Years and Over PCT_P0020002: Percent Hispanic or Latino PCT_P0020005: Percent White alone, not Hispanic or Latino PCT_P0020006: Percent Black or African American alone, not Hispanic or Latino PCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or Latino PCT_P0020008: Percent Asian alone, not Hispanic or Latino PCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or Latino PCT_P0020010: Percent Some Other Race alone, not Hispanic or Latino PCT_P0020011: Percent Population of two or more races, not Hispanic or Latino PCT_H0010002: Percent of Housing Units that are Occupied PCT_H0010003: Percent of Housing Units that are Vacant SUMLEV: Summary Level REGION: Region DIVISION: Division COUNTY: County (FIPS) COUNTYNS: County (NS) TRACT: Census Tract AREALAND: Area (Land) AREAWATR: Area (Water) INTPTLON: Internal Point (Longitude) INTPTLAT: Internal Point (Latitude) BASENAME: Area Base Name POP100: Total Population Count HU100: Total Housing Count *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual tracts will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map shows Census tracts throughout the US based on if they are considered disadvantaged or partially disadvantaged according to Justice40 Initiative criteria. This is overlaid with the most recent American Community Survey (ACS) figures from the U.S. Census Bureau to communicate the predominant race that lives within these disadvantaged or partially disadvantaged tracts. Predominance helps us understand the group of population which has the largest count within an area. Colors are more transparent if the predominant race has a similar count to another race/ethnicity group. The colors on the map help us better understand the predominant race or ethnicity:Hispanic or LatinoWhite Alone, not HispanicBlack or African American Alone, not HispanicAsian Alone, not HispanicAmerican Indian and Alaska Native Alone, not HispanicTwo or more races, not HispanicNative Hawaiian and Other Pacific Islander, not HispanicSome other race, not HispanicSearch for any region, city, or neighborhood throughout the US, DC, and Puerto Rico to learn more about the population in the disadvantaged tracts. Click on any tract to learn more. Zoom to your area, filter to your county or state, and save this web map focused on your area to share the pattern with others. You can also use this web map within an ArcGIS app such as a dashboard, instant app, or story. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.Note: Justice40 tracts use 2010-based boundaries, while the most recent ACS figures are offered on 2020-based boundaries. When you click on an area, there will be multiple pop-ups returned due to the differences in these boundaries. From Justice40 data source:"Census tract geographical boundaries are determined by the U.S. Census Bureau once every ten years. This tool utilizes the census tract boundaries from 2010 because they match the datasets used in the tool. The U.S. Census Bureau will update these tract boundaries in 2020.Under the current formula, a census tract will be identified as disadvantaged in one or more categories of criteria:IF the tract is above the threshold for one or more environmental or climate indicators AND the tract is above the threshold for the socioeconomic indicatorsCommunities are identified as disadvantaged by the current version of the tool for the purposes of the Justice40 Initiative if they are located in census tracts that are at or above the combined thresholds in one or more of eight categories of criteria.The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening toolPurpose"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI).American Community Survey (ACS):Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themesEach tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively. In our utilization of these sources, we likely integrated data from both the ACS and the SVI to analyze and understand various socio-economic and demographic indicators at the state, county, and possibly tract levels. This integrated data would have been valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United StatesNote: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2014-2018 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2015-2019 ACSEP_PCIEP_PCIPer capita income estimate, 2015-2019 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2015-2019 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2015-2019 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2015-2019 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2015-2019 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World