This map shows per capita income (income per person) in the U.S. in 2020 in a multiscale map by country, state, county, ZIP Code, tract, and block group. ArcGIS Online subscription required. Per capita income is calculated by taking the sum of all incomes and dividing by the total population.The pop-up is configured to include the following information for each geography level:2020 Per capita incomeTotal population2025 projected per capita incomeThe data shown is from Esri's 2020 Updated Demographic estimates using Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2020/2025) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

This map shows the average household income in the U.S. in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group. Information for the average household income is an estimate of income for calendar year 2022. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases.The pop-up is configured to include the following information for each geography level:Average household incomeMedian household incomeCount of households by income groupAverage household income by householder age groupThe data shown is from Esri's 2022 Updated Demographic estimates using Census 2020 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's U.S. Updated Demographic (2022/2027) Data: Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2022/2027 Esri Updated DemographicsEssential demographic vocabularyThis item is for visualization purposes only and cannot be exported or used in analysis.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

Layer from Esri, which shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. (This map is embedded in the Roanoke County Demographics Website, and thus the county has been filtered to be the only geography shown.)

This map shows the median household income in the U.S. in 2017 in a multiscale map by country, state, county, ZIP Code, tract, and block group. Median household income is estimated for 2017 in current dollars, including an adjustment for inflation or cost-of-living increases.The pop-up is configured to include the following information for each geography level:Median household incomeMedian household income by age of householderCount of households by income level (Householder age 15 to 24)Count of households by income level (Householder age 25 to 34)Count of households by income level (Householder age 35 to 44)Count of households by income level (Householder age 45 to 54)Count of households by income level (Householder age 55 to 64)Count of households by income level (Householder age 65 to 74)Count of households by income level (Householder age 75 plus)The data shown is from Esri's 2017 Updated Demographic estimates using Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2017/2022) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Data Note: The median household income value divides the distribution of household income into two equal parts. Pareto interpolation is used if the median falls in an income interval other than the first or last. For the lowest interval, <$10,000, linear interpolation is used. If the median falls in the upper income interval of $500,000+, it is represented by the value of $500,001.

This map shows the median household income in the United States in 2012. Information for the 2012 Median Household Income is an estimate of income for calendar year 2012. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases. The median is the value that divides the distribution of household income into two equal parts. The median household income in the United States overall was $50,157 in 2012. This map shows Esri's 2012 estimates using Census 2010 geographies. The geography depicts States at greater than 50m scale, Counties at 7.5m to 50m scale, Census Tracts at 200k to 7.5m scale, and Census Block Groups at less than 200k scale. Scale Range: 1:591,657,528 down to 1:72,224 For more information on this map, including our terms of use, visit us online at http://goto.arcgisonline.com/maps/Demographics/USA_Median_Household_Income

According to exit polling in the 2020 Presidential Election in the United States, ** percent of surveyed voters making less than 50,000 U.S. dollars reported voting for former Vice President Joe Biden. In the race to become the next president of the United States, ** percent of voters with an income of 100,000 U.S. dollars or more reported voting for incumbent President Donald Trump.

This map compares the number of people living above the poverty line to the number of people living below. Why do this?There are people living below the poverty line everywhere. Nearly every area of the country has a balance of people living above the poverty line and people living below it. There is not an "ideal" balance, so this map makes good use of the national ratio of 6 persons living above the poverty line for every 1 person living below it. Please consider that there is constant movement of people above and below the poverty threshold, as they gain better employment or lose a job; as they encounter a new family situation, natural disaster, health issue, major accident or other crisis. There are areas that suffer chronic poverty year after year. This map does not indicate how long people in the area have been below the poverty line. "The poverty rate is one of several socioeconomic indicators used by policy makers to evaluate economic conditions. It measures the percentage of people whose income fell below the poverty threshold. Federal and state governments use such estimates to allocate funds to local communities. Local communities use these estimates to identify the number of individuals or families eligible for various programs." Source: U.S. Census BureauIn the U.S. overall, there are 6 people living above the poverty line for every 1 household living below. Green areas on the map have a higher than normal number of people living above compared to below poverty. Orange areas on the map have a higher than normal number of people living below the poverty line compared to those above in that same area.The map shows the ratio for counties and census tracts, using these layers, created directly from the U.S. Census Bureau's American Community Survey (ACS)For comparison, an older layer using 2013 ACS data is also provided.The layers are updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. Current Vintage: 2014-2018ACS Table(s): B17020Data downloaded from: Census Bureau's API for American Community Survey National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

The 2020-2021 School Neighborhood Poverty Estimates are based on school locations from the 2020-2021 Common Core of Data (CCD) school file and income data from families with children ages 5 to 18 in the U.S. Census Bureau’s 2017-2021 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools.

Collections are available for the following years:

• School Neighborhood Poverty Estimates, 2020-2021
  
• School Neighborhood Poverty Estimates, 2019-2020
• School Neighborhood Poverty Estimates, 2018-2019
• School Neighborhood Poverty Estimates, 2017-2018
• School Neighborhood Poverty Estimates, 2016-2017
• School Neighborhood Poverty Estimates, 2015-2016

All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Database that provides access to population, housing, economic, and geographic data from several censuses and surveys about the United States, Puerto Rico and the Island Areas. Census data may be compiled into tables, maps and downloadable files, which can be viewed or printed. A large selection of pre-made tables and maps satisfies many information requests. By law, no one is permitted to reveal information from these censuses and surveys that could identify any person, household, or business. The following data are available: * American Community Survey * ACS Content Review * American Housing Survey * Annual Economic Surveys * Annual Surveys of Governments * Census of Governments * Decennial Census * Economic Census * Equal Employment Opportunity (EEO) Tabulation * Population Estimates Program * Puerto Rico Community Survey

The DSS Payment Demographic data set is made up of:

Selected DSS payment data by

• Geography: state/territory, electorate, postcode, LGA and SA2 (for 2015 onwards)

• Demographic: age, sex and Indigenous/non-Indigenous

• Duration on Payment (Working Age & Pensions)

• Duration on Income Support (Working Age, Carer payment & Disability Support Pension)

• Rate (Working Age & Pensions)

• Earnings (Working Age & Pensions)

• Age Pension assets data

• JobSeeker Payment and Youth Allowance (other) Principal Carers

• Activity Tested Recipients by Partial Capacity to Work (NSA,PPS & YAO)

• Exits within 3, 6 and 12 months (Newstart Allowance/JobSeeker Payment, Parenting Payment, Sickness Allowance & Youth Allowance)

• Disability Support Pension by medical condition

• Care Receiver by medical conditions

• Commonwealth Rent Assistance by Payment type and Income Unit type have been added from March 2017. For further information about Commonwealth Rent Assistance and Income Units see the Data Descriptions and Glossary included in the dataset.

From December 2022, the "DSS Expanded Benefit and Payment Recipient Demographics – quarterly data" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment and Special Benefit has changed to include recipients who are current but on zero rate of payment and those who are suspended from payment. The reporting population for ABSTUDY, Austudy, Parenting Payment and Youth Allowance has changed to include those who are suspended from payment. The expanded report will replace the standard report after June 2023.

Additional data for DSS Expanded Benefit and Payment Recipient Demographics – quarterly data includes:

• A new contents page to assist users locate the information within the spreadsheet

• Additional data for the ‘Suspended’ population in the ‘Payment by Rate’ tab to enable users to calculate the old reporting rules.

• Additional information on the Employment Earning by ‘Income Free Area’ tab.

From December 2022, Services Australia have implemented a change in the Centrelink payment system to recognise gender other than the sex assigned at birth or during infancy, or as a gender which is not exclusively male or female. To protect the privacy of individuals and comply with confidentialisation policy, persons identifying as ‘non-binary’ will initially be grouped with ‘females’ in the period immediately following implementation of this change. The Department will monitor the implications of this change and will publish the ‘non-binary’ gender category as soon as privacy and confidentialisation considerations allow.

Local Government Area has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2022 boundaries from June 2023.

Commonwealth Electorate Division has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

SA2 has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

From December 2021, the following are included in the report:

• selected payments by work capacity, by various demographic breakdowns

• rental type and homeownership

• Family Tax Benefit recipients and children by payment type

• Commonwealth Rent Assistance by proportion eligible for the maximum rate

• an age breakdown for Age Pension recipients

For further information, please see the Glossary.

From June 2021, data on the Paid Parental Leave Scheme is included yearly in June releases. This includes both Parental Leave Pay and Dad and Partner Pay, across multiple breakdowns. Please see Glossary for further information.

From March 2017 the DSS demographic dataset will include top 25 countries of birth. For further information see the glossary.

From March 2016 machine readable files containing the three geographic breakdowns have also been published for use in National Map, links to these datasets are below:

• Statistical Area 2 - SA2

• Commonwealth Electoral Division - CED

• Local Government Area - LGA

Pre June 2014 Quarter Data contains:

Selected DSS payment data by

• Geography: state/territory; electorate; postcode and LGA

• Demographic: age, sex and Indigenous/non-Indigenous

Note: JobSeeker Payment replaced Newstart Allowance and other working age payments from 20 March 2020, for further details see: https://www.dss.gov.au/benefits-payments/jobseeker-payment

For data on DSS payment demographics as at June 2013 or earlier, the department has published data which was produced annually. Data is provided by payment type containing timeseries’, state, gender, age range, and various other demographics. Links to these publications are below:

• Statistical Paper series

Concession card data in the March and June 2020 quarters have been re-stated to address an over-count in reported cardholder numbers.

28/06/2024 – The March 2024 and December 2023 reports were republished with updated data in the ‘Carer Receivers by Med Condition’ section, updates are exclusive to the ‘Care Receivers of Carer Payment recipients’ table, under ‘Intellectual / Learning’ and ‘Circulatory System’ conditions only.

This map layer portrays 1983 to 1987 estimates for total personal income, per capita personal income, annual number of full-time and part- time jobs, average wage per job in dollars, population, and per capita number of jobs, for counties in the United States. Total personal income is all the income that is received by, or on behalf of, the residents of a particular area. It is calculated as the sum of wage and salary disbursements, other labor income, proprietors' income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and transfer payments to persons, minus personal contributions for social insurance. Per capita personal income is calculated as the total personal income of the residents of a county divided by the resident population of the county. The Census Bureau's annual midyear population estimates were used in the computation. The average annual number of full-time and part-time jobs includes all jobs for which wages and salaries are paid, except jury and witness service and paid employment of prisoners. The jobs are counted at equal weight, and employees, sole proprietors, and active partners are all included. Unpaid family workers and volunteers are not included. Average wage per job is the wage and salary disbursements divided by the number of wage and salary jobs in the county. Wage and salary disbursements consist of the monetary remuneration of employees, including the compensation of corporate officers; commissions, tips, and bonuses; and receipts in kind, or pay-in-kind, such as the meals furnished to the employees of restaurants. It reflects the amount of payments disbursed, but not necessarily earned during the year. Per capita number of jobs is calculated as the average annual number of full-time and part-time jobs in a county divided by the resident population of the county. The Census Bureau's annual midyear population estimates were used in the computation. All dollar estimates are in current dollars, not adjusted for inflation. The information in this map layer comes from the Regional Economic Information System (REIS) that is distributed by the Bureau of Economic Analysis, http://www.bea.gov/. This is an updated version of the November 2004 map layer.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

A dataset listing North Carolina counties by population for 2024.

A dataset listing New York counties by population for 2024.

A dataset listing California counties by population for 2024.

Percent changes to demographic metrics in Home Owners’ Loan Corporation (HOLC) categories. Education and racial data start in 1940 and income data start in 1960.