100+ datasets found
  1. 2023 American Community Survey: S0101 | Age and Sex (ACS 1-Year Estimates...

    • data.census.gov
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    ACS, 2023 American Community Survey: S0101 | Age and Sex (ACS 1-Year Estimates Subject Tables) [Dataset]. https://data.census.gov/table/ACSST1Y2023.S0101
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2023
    Description

    Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The age dependency ratio is derived by dividing the combined under-18 and 65-and-over populations by the 18-to-64 population and multiplying by 100..The old-age dependency ratio is derived by dividing the population 65 and over by the 18-to-64 population and multiplying by 100..The child dependency ratio is derived by dividing the population under 18 by the 18-to-64 population and multiplying by 100..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

  2. P

    Census 2020 Table P1 12011 Place

    • data.pompanobeachfl.gov
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +1more
    Updated Feb 28, 2023
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    External Datasets (2023). Census 2020 Table P1 12011 Place [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p1-12011-place
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    arcgis geoservices rest api, html, geojson, csv, zip, kmlAvailable download formats
    Dataset updated
    Feb 28, 2023
    Dataset provided by
    cjennings_BCGIS
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table P1 – Race at the place level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  3. 2019 American Community Survey: S0801 | COMMUTING CHARACTERISTICS BY SEX...

    • data.census.gov
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    ACS, 2019 American Community Survey: S0801 | COMMUTING CHARACTERISTICS BY SEX (ACS 5-Year Estimates Subject Tables) [Dataset]. https://data.census.gov/cedsci/table?text=commute&t=Commuting&tid=ACSST5Y2019.S0801&hidePreview=false
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2019
    Description

    Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 12 selected states are Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Wisconsin..Workers include members of the Armed Forces and civilians who were at work last week..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..2019 ACS data products include updates to several categories of the existing means of transportation question. For more information, see: Change to Means of Transportation..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

  4. a

    USA 2020 Census Population Characteristics - Place Geographies

    • city-of-vancouver-strategic-plan-dashboard-cityofvancouver.hub.arcgis.com
    Updated May 25, 2023
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    Vancouver Online Maps (2023). USA 2020 Census Population Characteristics - Place Geographies [Dataset]. https://city-of-vancouver-strategic-plan-dashboard-cityofvancouver.hub.arcgis.com/datasets/8fe7368fd2024ed183572566a8fe96c3
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    Dataset updated
    May 25, 2023
    Dataset authored and provided by
    Vancouver Online Maps
    Area covered
    Description

    This CSV file shows total population counts by sex, age, and race groupsdata from the2020 CensusDemographic andHousing Characteristics. Thisisshown by Nation, Consolidated City, Census Designated Place, Incorporated Placeboundaries. Eachgeographylayercontainsa common set of Census countsbased on available attributes from the U.S. Census Bureau. There are alsoadditionalcalculated attributes related to this topic, which can be mapped or used within analysis.  Vintageof boundaries and attributes:2020Demographic andHousing CharacteristicsTable(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this file.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDatethe Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layerThe United States Census BureauDemographic andHousing Characteristics:2020 Census Results2020 Census Data QualityGeography &2020 CensusTechnical DocumentationData Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & UpdatesData Processing Notes:These 2020 Census boundaries come from the US Census TIGER geodatabases.These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. ForCensustractsand block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square metersor larger (mid tolarge sizedwater bodies) are erased from the tractand block groupboundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased tomore accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters). The layercontainsall US states, Washington D.C., and Puerto Rico.Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can beidentifiedby the "_calc_" stub in the field name).Field alias names were created based on the Table Shells file available from the Data Table Guide for theDemographic Profile and Demographic andHousing Characteristics.Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected usingdifferential privacy techniquesby the U.S. Census Bureau.

  5. d

    UNI-CEN Standardized Census Data Table - Census Tract (CT) - 1976 - Long...

    • search.dataone.org
    Updated Dec 28, 2023
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    UNI-CEN Project (2023). UNI-CEN Standardized Census Data Table - Census Tract (CT) - 1976 - Long Format (DTA) (Version 2023-03) [Dataset]. http://doi.org/10.5683/SP3/RWMP14
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    UNI-CEN Project
    Time period covered
    Jan 1, 1976
    Description

    UNI-CEN Standardized Census Data Tables contain Census data that have been reformatted into a common table format with standardized variable names and codes. The data are provided in two tabular formats for different use cases. "Long" tables are suitable for use in statistical environments, while "wide" tables are commonly used in GIS environments. The long tables are provided in Stata Binary (dta) format, which is readable by all statistics software. The wide tables are provided in comma-separated values (csv) and dBase 3 (dbf) formats with codebooks. The wide tables are easily joined to the UNI-CEN Digital Boundary Files. For the csv files, a .csvt file is provided to ensure that column data formats are correctly formatted when importing into QGIS. A schema.ini file does the same when importing into ArcGIS environments. As the DBF file format supports a maximum of 250 columns, tables with a larger number of variables are divided into multiple DBF files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

  6. American Community Survey: 1-Year Estimates: Subject Tables 1-Year

    • catalog.data.gov
    Updated Jul 19, 2023
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    U.S. Census Bureau (2023). American Community Survey: 1-Year Estimates: Subject Tables 1-Year [Dataset]. https://catalog.data.gov/dataset/american-community-survey-1-year-estimates-subject-tables-1-year
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    Dataset updated
    Jul 19, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Description

    The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Much of the ACS data provided on the Census Bureau's Web site are available separately by age group, race, Hispanic origin, and sex. Summary files, Subject tables, Data profiles, and Comparison profiles are available for the nation, all 50 states, the District of Columbia, Puerto Rico, every congressional district, every metropolitan area, and all counties and places with populations of 65,000 or more. Subject tables provide an overview of the estimates available in a particular topic. The data are presented as population counts and percentages. There are over 16,000 variables in this dataset.

  7. P

    Census 2020 Table P4 12011 Tracts

    • data.pompanobeachfl.gov
    • broward-county-demographics-bcgis.hub.arcgis.com
    Updated Feb 28, 2023
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    External Datasets (2023). Census 2020 Table P4 12011 Tracts [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p4-12011-tracts
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    csv, kml, zip, geojson, html, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Feb 28, 2023
    Dataset provided by
    cjennings_BCGIS
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table P4 – Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over at the census tract level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  8. P

    Census 2020 Table P5 12011 Tracts

    • data.pompanobeachfl.gov
    • broward-county-demographics-bcgis.hub.arcgis.com
    Updated Feb 28, 2023
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    External Datasets (2023). Census 2020 Table P5 12011 Tracts [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p5-12011-tracts
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    geojson, arcgis geoservices rest api, kml, zip, csv, htmlAvailable download formats
    Dataset updated
    Feb 28, 2023
    Dataset provided by
    cjennings_BCGIS
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table P5 - Group Quarters Population by Group Quarters Type at the census tract level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  9. USA 2020 Census Population Characteristics - Tribal Geographies

    • datalibrary-lnr.hub.arcgis.com
    Updated Jun 1, 2023
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    Esri (2023). USA 2020 Census Population Characteristics - Tribal Geographies [Dataset]. https://datalibrary-lnr.hub.arcgis.com/datasets/esri::usa-2020-census-population-characteristics-tribal-geographies
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    Dataset updated
    Jun 1, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Tribal Subdivision, Tribal Census Tract, Tribal Block Group, Alaska Native Regional Corporation, American Indian, Alaska Native, or Native Hawaiian Area boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Tribal Subdivision, Tribal Census Tract, Tribal Block Group, Alaska Native Regional Corporation, American Indian Alaska Native Native Hawaiian AreaNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  10. 2020 American Community Survey: DP05 | ACS DEMOGRAPHIC AND HOUSING ESTIMATES...

    • data.census.gov
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    ACS, 2020 American Community Survey: DP05 | ACS DEMOGRAPHIC AND HOUSING ESTIMATES (ACS 5-Year Estimates Data Profiles) [Dataset]. https://data.census.gov/table/ACSDP5Y2020.DP05
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2020
    Description

    Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..For more information on understanding race and Hispanic origin data, please see the Census 2010 Brief entitled, Overview of Race and Hispanic Origin: 2010, issued March 2011. (pdf format).The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

  11. P

    Census 2020 Table P4 12011 Blocks

    • data.pompanobeachfl.gov
    • broward-county-demographics-bcgis.hub.arcgis.com
    • +1more
    Updated Feb 28, 2023
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    External Datasets (2023). Census 2020 Table P4 12011 Blocks [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p4-12011-blocks
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    kml, zip, arcgis geoservices rest api, geojson, html, csvAvailable download formats
    Dataset updated
    Feb 28, 2023
    Dataset provided by
    cjennings_BCGIS
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table P4 – Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over at the block level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  12. P

    Census 2020 Table P2 12011 Block Groups

    • data.pompanobeachfl.gov
    • geohub-bcgis.opendata.arcgis.com
    Updated Feb 28, 2023
    + more versions
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    External Datasets (2023). Census 2020 Table P2 12011 Block Groups [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p2-12011-block-groups
    Explore at:
    html, zip, csv, kml, arcgis geoservices rest api, geojsonAvailable download formats
    Dataset updated
    Feb 28, 2023
    Dataset provided by
    cjennings_BCGIS
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table P2 – Hispanic or Latino, and Not Hispanic or Latino by Race at the block group level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  13. P

    Census 2020 Table H1 12011 Blocks

    • data.pompanobeachfl.gov
    • geohub-bcgis.opendata.arcgis.com
    • +1more
    Updated Feb 27, 2023
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    External Datasets (2023). Census 2020 Table H1 12011 Blocks [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-h1-12011-blocks
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    html, csv, geojson, arcgis geoservices rest api, kml, zipAvailable download formats
    Dataset updated
    Feb 27, 2023
    Dataset provided by
    cjennings_BCGIS
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table H1 – Occupancy Status at the block level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  14. U.S. Educational Attainment [1995-2015]

    • kaggle.com
    zip
    Updated Nov 15, 2017
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    Roy Garrard (2017). U.S. Educational Attainment [1995-2015] [Dataset]. https://www.kaggle.com/noriuk/us-educational-attainment-19952015
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    zip(1072658 bytes)Available download formats
    Dataset updated
    Nov 15, 2017
    Authors
    Roy Garrard
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    Context

    The United States Census Bureau conducts regular surveys to assess education levels in the U.S. These surveys sample participants' highest levels of education (i.e. high school diploma, bachelor's degree, etc.) The attached CSV file aggregates data for the years 1995, 2005, and 2015.

    Content

    Data is organized into columns representing the survey year, age range, sex of participants, and education level. For example, [1995, 18_24, Male, ...] represents the 1995 survey for men ages 18-24.

    It's worth noting that the surveys varied somewhat in granularity. The 2015 survey divided categories more finely (18-24, 25-29, 29-34...) while the 2005 and 1995 surveys were coarser (18-24, 25-34, ...). This could create some distortion depending on the analysis used.

    Sources

    Main

    https://www.census.gov/topics/education/educational-attainment/data/tables.All.html
    

    2015

    Table 1. Educational Attainment of the Population 18 Years and Over, by Age, Sex, Race, and Hispanic Origin: 2015

    https://www.census.gov/data/tables/2015/demo/education-attainment/p20-578.html
    

    2005

    Table 6. Educational Attainment of Employed Civilians 18 to 64 Years, by Occupation, Age, Sex, Race, and Hispanic Origin: 2005

    https://www.census.gov/data/tables/2005/demo/educational-attainment/cps-detailed-tables.html
    

    1995

    Educational Attainment in the United States: March 1995

    https://www.census.gov/data/tables/1995/demo/educational-attainment/p20-489.html
    
  15. d

    ACS 5-Year Demographic Characteristics DC Census Tract

    • opendata.dc.gov
    • datasets.ai
    • +3more
    Updated Feb 28, 2025
    + more versions
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    City of Washington, DC (2025). ACS 5-Year Demographic Characteristics DC Census Tract [Dataset]. https://opendata.dc.gov/datasets/62e1f639627342248a4d4027140a1935
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    Dataset updated
    Feb 28, 2025
    Dataset authored and provided by
    City of Washington, DC
    License

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

    Area covered
    Description

    Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2019-2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

  16. a

    US Census 2020 Incorporated Places Population Data

    • hub.arcgis.com
    • city-of-vancouver-strategic-plan-dashboard-cityofvancouver.hub.arcgis.com
    • +1more
    Updated May 25, 2023
    + more versions
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    Vancouver Online Maps (2023). US Census 2020 Incorporated Places Population Data [Dataset]. https://hub.arcgis.com/maps/CityOfVancouver::us-census-2020-incorporated-places-population-data
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    Dataset updated
    May 25, 2023
    Dataset authored and provided by
    Vancouver Online Maps
    Area covered
    Description

    This feature layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this file.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Incorporated Place The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  17. Urban and Rural Population in the US (2020 Census)

    • data-bgky.hub.arcgis.com
    Updated Jun 7, 2023
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    Esri (2023). Urban and Rural Population in the US (2020 Census) [Dataset]. https://data-bgky.hub.arcgis.com/items/0f98fc66a9fa4f94953718578cbd77a8
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    Dataset updated
    Jun 7, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This map's colors indicate which population is larger in each area: urban (green) or rural (yellow). The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico.The U.S. Census designates each census block as part of an urban area or as rural. Larger geographies in this map such as block group, tract, county and state can therefore have a mix of urban and rural population. This map illustrates the 100% urban areas in dark green, and 100% rural areas in dark yellow. Areas with mixed urban/rural population have softer shades of green or yellow, to give a visual indication of where change may be happening. From the Census:"The Census Bureau’s urban-rural classification is a delineation of geographic areas, identifying both individual urban areas and the rural area of the nation. The Census Bureau’s urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses. The Census Bureau delineates urban areas after each decennial census by applying specified criteria to decennial census and other data. Rural encompasses all population, housing, and territory not included within an urban area.For the 2020 Census, an urban area will comprise a densely settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  18. P

    Census 2020 Table P2 12011 County

    • data.pompanobeachfl.gov
    • broward-innovation-citizen-portal-bcgis.hub.arcgis.com
    • +2more
    Updated Apr 14, 2022
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    External Datasets (2022). Census 2020 Table P2 12011 County [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p2-12011-county
    Explore at:
    html, arcgis geoservices rest api, zip, geojson, kml, csvAvailable download formats
    Dataset updated
    Apr 14, 2022
    Dataset provided by
    BCGISData
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table P2 – Hispanic or Latino, and Not Hispanic or Latino by Race at the county level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  19. P

    Census 2020 Table P5 12011 County

    • data.pompanobeachfl.gov
    • broward-county-demographics-bcgis.hub.arcgis.com
    • +2more
    Updated Feb 28, 2023
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    External Datasets (2023). Census 2020 Table P5 12011 County [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p5-12011-county
    Explore at:
    html, zip, csv, arcgis geoservices rest api, kml, geojsonAvailable download formats
    Dataset updated
    Feb 28, 2023
    Dataset provided by
    BCGISFL
    Authors
    External Datasets
    Description

    2020 Census P.L. 94-171 is the first detailed data release from the 2020 Decennial Census of Population and Housing. The web layer is based on an extract for Table P5 - Group Quarters Population by Group Quarters Type at the county level geography of Broward County, Florida. The data extract was then joined to the 2020 Census TIGER/Line Shapefiles.

    For details on field names, table hierarchy, and table contents refer to TABLE (MATRIX) SECTION in Chapter 6. Data Dictionary, https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf" STYLE="text-decoration:underline;">2020 Census State Public Law 94-171 Summary File Technical Documentation.

  20. l

    LA County Language Spoken at Home (census tract)

    • data.lacounty.gov
    • egis-lacounty.hub.arcgis.com
    • +2more
    Updated Jul 28, 2025
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    County of Los Angeles (2025). LA County Language Spoken at Home (census tract) [Dataset]. https://data.lacounty.gov/datasets/la-county-language-spoken-at-home-census-tract
    Explore at:
    Dataset updated
    Jul 28, 2025
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    US Census American Community Survey Custom Tabulation (ST542) by Census Tract. Language spoken at home for population 5 years and over by ability to speak English, summarized by census tract for 114 languages spoken across LA County, 5-year estimates 2019-2023.See also source data tables:Census Tracts: Language Spoken at Home LA County Census TractsLA County: Language Spoken at Home LA County Headings:GEOIDGeography identificationCT20Census tract (2020)NameCensus tract nameCSACountywide Statistical Area (city or community)SPAService Planning AreaSDSupervisorial Districttotal_popPopulation over 5 years old in census tract (universe)total_limited_engPopulation that speaks English less than "very well"total_limited_eng_pctPercent of population that speaks English less than "very well"

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ACS, 2023 American Community Survey: S0101 | Age and Sex (ACS 1-Year Estimates Subject Tables) [Dataset]. https://data.census.gov/table/ACSST1Y2023.S0101
Organization logo

2023 American Community Survey: S0101 | Age and Sex (ACS 1-Year Estimates Subject Tables)

2023: ACS 1-Year Estimates Subject Tables

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Dataset provided by
United States Census Bureauhttp://census.gov/
Authors
ACS
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Time period covered
2023
Description

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The age dependency ratio is derived by dividing the combined under-18 and 65-and-over populations by the 18-to-64 population and multiplying by 100..The old-age dependency ratio is derived by dividing the population 65 and over by the 18-to-64 population and multiplying by 100..The child dependency ratio is derived by dividing the population under 18 by the 18-to-64 population and multiplying by 100..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

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