72 datasets found
  1. a

    Congressional Districts 116th

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    Updated Apr 10, 2020
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    ArcGIS Living Atlas Team (2020). Congressional Districts 116th [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/arcgis-content::usa-particulate-matter-pm-2-5-between-1998-2016?layer=2
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    Dataset updated
    Apr 10, 2020
    Dataset authored and provided by
    ArcGIS Living Atlas Team
    Area covered
    Description

    This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries:50km hex bins generated using the Generate Tessellation toolStates and counties come from 2018 TIGER boundaries with coastlines clipped116th Congressional Districts come from this ArcGIS Living Atlas layerData processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and summarized within the state and county boundaries.The summation of these values were then divided by the total population of each state/county. This population value was determined by summarizing the population values from the hex bins within each geography.

  2. g

    Census of Population and Housing, 2000 [United States]: 110th Congressional...

    • search.gesis.org
    Updated May 6, 2021
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    United States Department of Commerce. Bureau of the Census (2021). Census of Population and Housing, 2000 [United States]: 110th Congressional District Summary File, Sample - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR21803
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    Dataset updated
    May 6, 2021
    Dataset provided by
    ICPSR - Interuniversity Consortium for Political and Social Research
    GESIS search
    Authors
    United States Department of Commerce. Bureau of the Census
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de447283https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de447283

    Area covered
    United States
    Description

    Abstract (en): This data collection contains information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, type of living quarters (household/group quarters), urban/rural status, household relationship, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry into the United States, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, occupation and industry, class of worker, income, and poverty status. Housing items include vacancy status, tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, household size, occupants per room, number of units in structure, year structure was built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent. With subject content identical to that provided in Summary File 3, the information is presented in 813 tables that are tabulated for every geographic unit represented in the data. There is one variable per table cell, plus additional variables with geographic information. The data cover more than a dozen geographic levels of observation (known as "summary levels" in the Census Bureau's nomenclature) based on the 110th Congressional Districts, e.g., the 110th Congressional Districts, themselves, Census tracts within the 110th Congressional Districts, and county subdivisions within the 110th Congressional Districts. There are 77 data files for each state, the District of Columbia, and Puerto Rico. The collection is supplied in 54 ZIP archives. There is a separate ZIP file for each state, the District of Columbia, and Puerto Rico, and for the convenience of those who need all of the data, a separate ZIP archive with all 4,004 data files. The codebook and other documentation are located in the last ZIP archive. All persons and housing units in the United States and Puerto Rico. Every person and housing unit in the United States was asked basic demographic and housing questions (for example, race, age, and relationship to householder). A sample of these people and housing units was asked more detailed questions. The sampling unit for Census 2000 was the housing unit, including all occupants. There were four different housing unit sampling rates, 1-in-8, 1-in-6, 1-in-4, and 1-in-2, designed to yield an overall average of about 1-in-6. mail questionnaireICPSR has not checked this data collection.

  3. Social by Race (by US Congress) 2017

    • opendata.atlantaregional.com
    Updated Jun 26, 2019
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    Georgia Association of Regional Commissions (2019). Social by Race (by US Congress) 2017 [Dataset]. https://opendata.atlantaregional.com/datasets/social-by-race-by-us-congress-2017/geoservice
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    Dataset updated
    Jun 26, 2019
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show school enrollment, education attainments, and household composition by race and by US Congressional Districts in Georgia.

    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 2013-2017). 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.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    Mean (average)

    t

    Aggregate (total)

    ch

    Change in absolute terms (value in t2 - value in t1)

    pch

    Percent change ((value in t2 - value in t1) / value in t1)

    chp

    Change in percent (percent in t2 - percent in t1)

    Suffixes:

    None

    Change over two periods

    _e

    Estimate from most recent ACS

    _m

    Margin of Error from most recent ACS

    _00

    Decennial 2000

    Attributes:

    Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here).

    Source: U.S. Census Bureau, Atlanta Regional Commission

    Date: 2013-2017

    For additional information, please visit the Census ACS website.

  4. r

    ClaimLoc 2025 & MedianAge 2023

    • opendata.rcmrd.org
    Updated Jul 12, 2025
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    University of Wisconsin-Milwaukee (2025). ClaimLoc 2025 & MedianAge 2023 [Dataset]. https://opendata.rcmrd.org/maps/52cee01a881d42d099fcbfa8db561504
    Explore at:
    Dataset updated
    Jul 12, 2025
    Dataset authored and provided by
    University of Wisconsin-Milwaukee
    Area covered
    Description

    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)

  5. u

    Average Wildfire Hazard Potential in the US

    • colorado-river-portal.usgs.gov
    • california-smart-climate-housing-growth-usfca.hub.arcgis.com
    • +4more
    Updated Aug 18, 2020
    + more versions
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    ArcGIS Living Atlas Team (2020). Average Wildfire Hazard Potential in the US [Dataset]. https://colorado-river-portal.usgs.gov/maps/7b67417ceb5249cbb5fc904469d5d716
    Explore at:
    Dataset updated
    Aug 18, 2020
    Dataset authored and provided by
    ArcGIS Living Atlas Team
    Area covered
    Description

    This web map shows the wildfire hazard potential (WHP) for the conterminous United States aggregated from states to block groups and 50 km hex bins. The data is from the USDA Forest Service Fire Modeling Institute providing an index of WHP at a 270 meter resolution. Wildfire hazard potential provides information on the relative potential for wildfire that would be difficult for fire crews to contain. Areas with higher wildfire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior. A score of 5 is very high risk and a score between 0-1 is non-burnable area such as water or asphalt. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as communities, structures, or powerlines, it can approximate relative wildfire risk to those resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic planning and fuels management.Each layer has been enriched with 2020 Esri demographic attributes to better approximate wildfire hazard risk. A hosted imagery layer of this data is available in ArcGIS Living Atlas for additional analysis.Data notes:Zonal Statistics as Table were run against a local copy of the WHP data using US standard geographies as the feature zone input for the analysis. Geographies included are: State, County, Congressional District, ZIP Code, Tract, and Block Group. Statistical tables were joined to geographies. To learn more about zonal statistics, view the documentation here. 50 km hex bins were created using Generate Tessellation and then joined to zonal statistics as described above (step 1).Data was enriched with 2020 Esri Demographics. Attributes include population, households & housing units, growth rate, and calculated variables such as population change over time. To create the population-weighted attributes on the state, congressional district, and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average WHP were multiplied.The hex bins were converted into centroids and summarized within the state, congressional district, and county boundaries.The summation of these values were then divided by the total population of each respective geography.

  6. Population 2023 (all geographies, statewide)

    • opendata.atlantaregional.com
    Updated Feb 21, 2025
    + more versions
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    Georgia Association of Regional Commissions (2025). Population 2023 (all geographies, statewide) [Dataset]. https://opendata.atlantaregional.com/maps/c346c5bdd1e942e281b0344fa2c24feb
    Explore at:
    Dataset updated
    Feb 21, 2025
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    These data were developed by the Research & Analytics Department 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 2019-2023. 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:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (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)BeltLineStatistical (buffer)BeltLineStatisticalSub (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 Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State 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)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 2019-2023). 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: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

  7. Commuting (by US Congress) 2017

    • gisdata.fultoncountyga.gov
    • opendata.atlantaregional.com
    Updated Jun 22, 2019
    + more versions
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    Georgia Association of Regional Commissions (2019). Commuting (by US Congress) 2017 [Dataset]. https://gisdata.fultoncountyga.gov/datasets/GARC::commuting-by-us-congress-2017/data
    Explore at:
    Dataset updated
    Jun 22, 2019
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show the method of transportation that workers use to get to work and their mean travel time by US Congress in the Atlanta region.

    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 2013-2017). 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.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    Mean (average)

    t

    Aggregate (total)

    ch

    Change in absolute terms (value in t2 - value in t1)

    pch

    Percent change ((value in t2 - value in t1) / value in t1)

    chp

    Change in percent (percent in t2 - percent in t1)

    Suffixes:

    None

    Change over two periods

    _e

    Estimate from most recent ACS

    _m

    Margin of Error from most recent ACS

    _00

    Decennial 2000

    Attributes:

    SumLevel

    Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

    GEOID

    Census tract Federal Information Processing Series (FIPS) code

    NAME

    Name of geographic unit

    Planning_Region

    Planning region designation for ARC purposes

    Acres

    Total area within the tract (in acres)

    SqMi

    Total area within the tract (in square miles)

    County

    County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

    CountyName

    County Name

    Workers16P_e

    # Workers 16 years and over, 2017

    Workers16P_m

    # Workers 16 years and over, 2017 (MOE)

    Drove_e

    # Car, truck, or van - drove alone to work, 2017

    Drove_m

    # Car, truck, or van - drove alone to work, 2017 (MOE)

    pDrove_e

    % Car, truck, or van - drove alone to work, 2017

    pDrove_m

    % Car, truck, or van - drove alone to work, 2017 (MOE)

    Carpool_e

    # Car, truck, or van - carpooled to work, 2017

    Carpool_m

    # Car, truck, or van - carpooled to work, 2017 (MOE)

    pCarpool_e

    % Car, truck, or van - carpooled to work, 2017

    pCarpool_m

    % Car, truck, or van - carpooled to work, 2017 (MOE)

    PublicTrans_e

    # Public transportation (excluding taxicab) to work, 2017

    PublicTrans_m

    # Public transportation (excluding taxicab) to work, 2017 (MOE)

    pPublicTrans_e

    % Public transportation (excluding taxicab) to work, 2017

    pPublicTrans_m

    % Public transportation (excluding taxicab) to work, 2017 (MOE)

    Walked_e

    # Walked to work, 2017

    Walked_m

    # Walked to work, 2017 (MOE)

    pWalked_e

    % Walked to work, 2017

    pWalked_m

    % Walked to work, 2017 (MOE)

    OtherCommute_e

    # Other means to work, 2017

    OtherCommute_m

    # Other means to work, 2017 (MOE)

    pOtherCommute_e

    % Other means to work, 2017

    pOtherCommute_m

    % Other means to work, 2017 (MOE)

    WorkAtHome_e

    # Worked at home, 2017

    WorkAtHome_m

    # Worked at home, 2017 (MOE)

    pWorkAtHome_e

    % Worked at home, 2017

    pWorkAtHome_m

    % Worked at home, 2017 (MOE)

    aMeanTravelTimeWork_e

    Mean travel time to work (minutes), 2017

    aMeanTravelTimeWork_m

    Mean travel time to work (minutes), 2017 (MOE)

    last_edited_date

    Last date the feature was edited by ARC

    Source: U.S. Census Bureau, Atlanta Regional Commission

    Date: 2013-2017

    For additional information, please visit the Census ACS website.

  8. T

    Thailand Population: Thai Nationality: In House Registration (TNHR)

    • ceicdata.com
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    CEICdata.com, Thailand Population: Thai Nationality: In House Registration (TNHR) [Dataset]. https://www.ceicdata.com/en/thailand/population-by-region-and-registration/population-thai-nationality-in-house-registration-tnhr
    Explore at:
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2006 - Dec 1, 2017
    Area covered
    Thailand
    Variables measured
    Population
    Description

    Thailand Population: Thai Nationality: In House Registration (TNHR) data was reported at 64,627,515.000 Person in 2017. This records an increase from the previous number of 64,417,198.000 Person for 2016. Thailand Population: Thai Nationality: In House Registration (TNHR) data is updated yearly, averaging 61,390,974.500 Person from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 64,627,515.000 Person in 2017 and a record low of 56,515,051.000 Person in 1994. Thailand Population: Thai Nationality: In House Registration (TNHR) data remains active status in CEIC and is reported by Department of Provincial Administration. The data is categorized under Global Database’s Thailand – Table TH.G001: Population: By Region and Registration.

  9. Voting Age (by Georgia House) 2017

    • opendata.atlantaregional.com
    • gisdata.fultoncountyga.gov
    Updated Jun 22, 2019
    + more versions
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    Georgia Association of Regional Commissions (2019). Voting Age (by Georgia House) 2017 [Dataset]. https://opendata.atlantaregional.com/datasets/GARC::voting-age-by-georgia-house-2017/about
    Explore at:
    Dataset updated
    Jun 22, 2019
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show numbers and percentages for voting age population by Georgia House in the Atlanta region.

    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 2013-2017). 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.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    Mean (average)

    t

    Aggregate (total)

    ch

    Change in absolute terms (value in t2 - value in t1)

    pch

    Percent change ((value in t2 - value in t1) / value in t1)

    chp

    Change in percent (percent in t2 - percent in t1)

    Suffixes:

    None

    Change over two periods

    _e

    Estimate from most recent ACS

    _m

    Margin of Error from most recent ACS

    _00

    Decennial 2000

    Attributes:

    SumLevel

    Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

    GEOID

    Census tract Federal Information Processing Series (FIPS) code

    NAME

    Name of geographic unit

    Planning_Region

    Planning region designation for ARC purposes

    Acres

    Total area within the tract (in acres)

    SqMi

    Total area within the tract (in square miles)

    County

    County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

    CountyName

    County Name

    VotingAgeCitizen_e

    # Citizen, 18 and over population, 2017

    VotingAgeCitizen_m

    # Citizen, 18 and over population, 2017 (MOE)

    VotingAgeCitizenMale_e

    # Male citizen, 18 and over population, 2017

    VotingAgeCitizenMale_m

    # Male citizen, 18 and over population, 2017 (MOE)

    pVotingAgeCitizenMale_e

    % Male citizen, 18 and over population, 2017

    pVotingAgeCitizenMale_m

    % Male citizen, 18 and over population, 2017 (MOE)

    VotingAgeCitizenFemale_e

    # Female citizen, 18 and over population, 2017

    VotingAgeCitizenFemale_m

    # Female citizen, 18 and over population, 2017 (MOE)

    pVotingAgeCitizenFemale_e

    % Female citizen, 18 and over population, 2017

    pVotingAgeCitizenFemale_m

    % Female citizen, 18 and over population, 2017 (MOE)

    last_edited_date

    Last date the feature was edited by ARC

    Source: U.S. Census Bureau, Atlanta Regional Commission

    Date: 2013-2017

    For additional information, please visit the Census ACS website.

  10. l

    Percentage of Hispanic

    • data.lacounty.gov
    • hub.arcgis.com
    Updated Dec 22, 2023
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    County of Los Angeles (2023). Percentage of Hispanic [Dataset]. https://data.lacounty.gov/datasets/percentage-of-hispanic
    Explore at:
    Dataset updated
    Dec 22, 2023
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

  11. Population 2022 (all geographies, statewide)

    • gisdata.fultoncountyga.gov
    • opendata.atlantaregional.com
    Updated Mar 2, 2024
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    Georgia Association of Regional Commissions (2024). Population 2022 (all geographies, statewide) [Dataset]. https://gisdata.fultoncountyga.gov/maps/602b48678ffc48e889161507c1bb674a
    Explore at:
    Dataset updated
    Mar 2, 2024
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    These data were 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 2018-2022 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:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (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)BeltLineStatistical (buffer)BeltLineStatisticalSub (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 Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State 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)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 2018-2022). 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: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

  12. Opportunity Youth (by US Congress) 2017

    • opendata.atlantaregional.com
    Updated Jun 26, 2019
    + more versions
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    Georgia Association of Regional Commissions (2019). Opportunity Youth (by US Congress) 2017 [Dataset]. https://opendata.atlantaregional.com/datasets/GARC::opportunity-youth-by-us-congress-2017/about
    Explore at:
    Dataset updated
    Jun 26, 2019
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show the number and percentages of opportunity to youth by US Congress in the Atlanta region.

    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 2013-2017). 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.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    Mean (average)

    t

    Aggregate (total)

    ch

    Change in absolute terms (value in t2 - value in t1)

    pch

    Percent change ((value in t2 - value in t1) / value in t1)

    chp

    Change in percent (percent in t2 - percent in t1)

    Suffixes:

    None

    Change over two periods

    _e

    Estimate from most recent ACS

    _m

    Margin of Error from most recent ACS

    _00

    Decennial 2000

    Attributes:

    SumLevel

    Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

    GEOID

    Census tract Federal Information Processing Series (FIPS) code

    NAME

    Name of geographic unit

    Planning_Region

    Planning region designation for ARC purposes

    Acres

    Total area within the tract (in acres)

    SqMi

    Total area within the tract (in square miles)

    County

    County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

    CountyName

    County Name

    PopAges1619_e

    # Population, ages 16-19, 2017

    PopAges1619_m

    # Population, ages 16-19, 2017 (MOE)

    DisconYouth_e

    # Disconnected youth: ages 16-19 not in school or in labor force, 2017

    DisconYouth_m

    # Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

    pDisconYouth_e

    % Disconnected youth: ages 16-19 not in school or in labor force, 2017

    pDisconYouth_m

    % Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

    OwnChildInFam_e

    # Own children in families, 2017

    OwnChildInFam_m

    # Own children in families, 2017 (MOE)

    NoParentLabForce_e

    # Own children in families with no parent in the labor force, 2017

    NoParentLabForce_m

    # Own children in families with no parent in the labor force, 2017 (MOE)

    pNoParentLabForce_e

    % Own children in families with no parent in the labor force, 2017

    pNoParentLabForce_m

    % Own children in families with no parent in the labor force, 2017 (MOE)

    last_edited_date

    Last date the feature was edited by ARC

    Source: U.S. Census Bureau, Atlanta Regional Commission

    Date: 2013-2017

    For additional information, please visit the Census ACS website.

  13. Housing Characteristics 2021 (all geographies, statewide)

    • gisdata.fultoncountyga.gov
    Updated Mar 10, 2023
    + more versions
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    Georgia Association of Regional Commissions (2023). Housing Characteristics 2021 (all geographies, statewide) [Dataset]. https://gisdata.fultoncountyga.gov/maps/3a1485583e8b496bb03505b32b56cfe5
    Explore at:
    Dataset updated
    Mar 10, 2023
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    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

  14. ACS2021 Demographic Population ARC21

    • gisdata.fultoncountyga.gov
    Updated Mar 9, 2023
    + more versions
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    Georgia Association of Regional Commissions (2023). ACS2021 Demographic Population ARC21 [Dataset]. https://gisdata.fultoncountyga.gov/maps/GARC::acs2021-demographic-population-arc21
    Explore at:
    Dataset updated
    Mar 9, 2023
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    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

  15. ACS 2020 Marital Status

    • gisdata.fultoncountyga.gov
    • opendata.atlantaregional.com
    Updated Apr 22, 2022
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    Georgia Association of Regional Commissions (2022). ACS 2020 Marital Status [Dataset]. https://gisdata.fultoncountyga.gov/maps/b67f6f895ead41e4bd509afb2ff4ff21
    Explore at:
    Dataset updated
    Apr 22, 2022
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    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 2016-2020 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.

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    Mean (average)

    t

    Aggregate (total)

    ch

    Change in absolute terms (value in t2 - value in t1)

    pch

    Percent change ((value in t2 - value in t1) / value in t1)

    chp

    Change in percent (percent in t2 - percent in t1)

    s

    Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

    Suffixes:

    _e20

    Estimate from 2016-20 ACS

    _m20

    Margin of Error from 2016-20 ACS

    _e10

    2006-10 ACS, re-estimated to 2020 geography

    _m10

    Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

    _e10_20

    Change, 2010-20 (holding constant at 2020 geography)

    Geographies

    AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

    ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

    Census Tracts (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 (subarea of City of Atlanta)

    City of Atlanta Neighborhood Statistical Areas (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)

    State of Georgia (statewide)

    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 2016-2020). 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 Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

    Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

  16. Housing Value 2022 (all geographies, statewide)

    • gisdata.fultoncountyga.gov
    • opendata.atlantaregional.com
    • +2more
    Updated Mar 1, 2024
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    Georgia Association of Regional Commissions (2024). Housing Value 2022 (all geographies, statewide) [Dataset]. https://gisdata.fultoncountyga.gov/maps/57a9a53be8074818be578ddbc03c0e3f
    Explore at:
    Dataset updated
    Mar 1, 2024
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    These data were 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 2018-2022 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:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (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)BeltLineStatistical (buffer)BeltLineStatisticalSub (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 Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State 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)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 2018-2022). 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: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

  17. ACS 2020 By Race Demographic

    • gisdata.fultoncountyga.gov
    • opendata.atlantaregional.com
    Updated Apr 22, 2022
    + more versions
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    Georgia Association of Regional Commissions (2022). ACS 2020 By Race Demographic [Dataset]. https://gisdata.fultoncountyga.gov/maps/e8d4fd0d545c446b8e28847b5684fd5a
    Explore at:
    Dataset updated
    Apr 22, 2022
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    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 2016-2020 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.

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    Mean (average)

    t

    Aggregate (total)

    ch

    Change in absolute terms (value in t2 - value in t1)

    pch

    Percent change ((value in t2 - value in t1) / value in t1)

    chp

    Change in percent (percent in t2 - percent in t1)

    s

    Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

    Suffixes:

    _e20

    Estimate from 2016-20 ACS

    _m20

    Margin of Error from 2016-20 ACS

    _e10

    2006-10 ACS, re-estimated to 2020 geography

    _m10

    Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

    _e10_20

    Change, 2010-20 (holding constant at 2020 geography)

    Geographies

    AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

    ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

    Census Tracts (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 (subarea of City of Atlanta)

    City of Atlanta Neighborhood Statistical Areas (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)

    State of Georgia (statewide)

    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 2016-2020). 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 Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

    Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

  18. Population by Sex & Age 2023 (all geographies, statewide)

    • opendata.atlantaregional.com
    • arc-garc.opendata.arcgis.com
    Updated Feb 21, 2025
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    Georgia Association of Regional Commissions (2025). Population by Sex & Age 2023 (all geographies, statewide) [Dataset]. https://opendata.atlantaregional.com/maps/4d51045c9943418b9e35841ab66103aa
    Explore at:
    Dataset updated
    Feb 21, 2025
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    These data were developed by the Research & Analytics Department 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 2019-2023. 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:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (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)BeltLineStatistical (buffer)BeltLineStatisticalSub (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 Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State 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)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 2019-2023). 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: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

  19. Demographic change 2010 - 2023 (all geographies, statewide)

    • gisdata.fultoncountyga.gov
    • hub.arcgis.com
    Updated Feb 22, 2025
    + more versions
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    Georgia Association of Regional Commissions (2025). Demographic change 2010 - 2023 (all geographies, statewide) [Dataset]. https://gisdata.fultoncountyga.gov/maps/f70f4d7defb94a20987e59061b012bbe
    Explore at:
    Dataset updated
    Feb 22, 2025
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    These data were developed by the Research & Analytics Department 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 2019-2023. 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:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (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)BeltLineStatistical (buffer)BeltLineStatisticalSub (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 Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State 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)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 2019-2023). 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: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

  20. Voting Age 2023 (all geographies, statewide)

    • gisdata.fultoncountyga.gov
    • opendata.atlantaregional.com
    • +2more
    Updated Feb 21, 2025
    Share
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    Georgia Association of Regional Commissions (2025). Voting Age 2023 (all geographies, statewide) [Dataset]. https://gisdata.fultoncountyga.gov/maps/072aa08f554b4d8497098eaa540a3fe3
    Explore at:
    Dataset updated
    Feb 21, 2025
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    These data were developed by the Research & Analytics Department 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 2019-2023. 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:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (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)BeltLineStatistical (buffer)BeltLineStatisticalSub (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 Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State 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)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 2019-2023). 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: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

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ArcGIS Living Atlas Team (2020). Congressional Districts 116th [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/arcgis-content::usa-particulate-matter-pm-2-5-between-1998-2016?layer=2

Congressional Districts 116th

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Dataset updated
Apr 10, 2020
Dataset authored and provided by
ArcGIS Living Atlas Team
Area covered
Description

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries:50km hex bins generated using the Generate Tessellation toolStates and counties come from 2018 TIGER boundaries with coastlines clipped116th Congressional Districts come from this ArcGIS Living Atlas layerData processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and summarized within the state and county boundaries.The summation of these values were then divided by the total population of each state/county. This population value was determined by summarizing the population values from the hex bins within each geography.

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