100+ datasets found
  1. a

    Population Density in the US 2020 Census

    • hub.arcgis.com
    Updated Jun 20, 2024
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    University of South Florida GIS (2024). Population Density in the US 2020 Census [Dataset]. https://hub.arcgis.com/maps/58e4ee07a0e24e28949903511506a8e4
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    Dataset updated
    Jun 20, 2024
    Dataset authored and provided by
    University of South Florida GIS
    Area covered
    Description

    This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. 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. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." 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.

  2. W

    County Population Density- 2009

    • cloud.csiss.gmu.edu
    csv, json, rdf, xml
    Updated Oct 27, 2016
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    Open Africa (2016). County Population Density- 2009 [Dataset]. https://cloud.csiss.gmu.edu/uddi/ne/dataset/e892a2cb-9874-4c19-8e75-9093f10a7335
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    rdf, csv, xml, jsonAvailable download formats
    Dataset updated
    Oct 27, 2016
    Dataset provided by
    Open Africa
    License

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

    Description

    This dataset describes County population by gender, number of households, gender index, household density and area in square kilometers

  3. w

    County Population Density 1900 - 2010

    • data.wu.ac.at
    Updated Aug 27, 2016
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    Jennifer Johnson (2016). County Population Density 1900 - 2010 [Dataset]. https://data.wu.ac.at/schema/data_wa_gov/eHY0eS1mMmsy
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    Dataset updated
    Aug 27, 2016
    Dataset provided by
    Jennifer Johnson
    Description

    Washington state population density by county by decade 1900 to 2010.

  4. Population density in Sweden 2022, by county

    • statista.com
    Updated Jul 4, 2024
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    Statista (2024). Population density in Sweden 2022, by county [Dataset]. https://www.statista.com/statistics/526617/sweden-population-density-by-county/
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    Dataset updated
    Jul 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    Sweden
    Description

    Among the regions in Sweden, the the capital region Stockholm county had the highest population density in 2022, with 374.6 inhabitants per square kilometers. In 2021, more than 2.4 million people lived in Stockholm. In terms of highest population density, Stockholm county was followed by Skåne, with 129 inhabitants per square kilometer. The least populated county was Norrbotten, with only 2.6 inhabitants per square kilometer.

    Increasing population density

    The population in Sweden is increasing steadily and reached 10.52 million inhabitants in 2022. Because of the growing population, the population density in Sweden increased as well over the past 10 years. In 2012, there were 23.4 inhabitants per square kilometer and in 2022 the number had increased to 25.8. Despite this, Sweden is a relatively sparsely populated country.

    Highest rent per square meter in Stockholm

    As the most densely populated county, the rents for rented dwellings in Stockholm were higher than in Sweden’s other counties. In 2020, the average rent per square meter in Stockholm county amounted to almost 1,300 Swedish kronor, while the rent in Norrbotten, the least populated county, reached an average of 999 Swedish kronor per square meter.

  5. a

    2021 Population Density by County

    • gis-fdot.opendata.arcgis.com
    • mapdirect-fdep.opendata.arcgis.com
    • +2more
    Updated Aug 9, 2023
    + more versions
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    Florida Department of Transportation (2023). 2021 Population Density by County [Dataset]. https://gis-fdot.opendata.arcgis.com/maps/fdot::2021-population-density-by-county
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    Dataset updated
    Aug 9, 2023
    Dataset authored and provided by
    Florida Department of Transportation
    Area covered
    Description

    Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here.This dataset contains county boundaries in the State of Florida with 2021 population density estimates. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021). Please see the Data Dictionary for more information on data fields. Data Sources:FDOT FTO 2020 and 2021 Population Estimates by CountyUS Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2021 Date of Publication: October 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

  6. d

    Population and population density of Chiayi County

    • data.gov.tw
    xml
    Updated Jun 1, 2025
    + more versions
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    Directorate General of Budget, Accounting and Statistics, Executive Yuan, R.O.C. (2025). Population and population density of Chiayi County [Dataset]. https://data.gov.tw/en/datasets/18512
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    xmlAvailable download formats
    Dataset updated
    Jun 1, 2025
    Dataset authored and provided by
    Directorate General of Budget, Accounting and Statistics, Executive Yuan, R.O.C.
    License

    https://data.gov.tw/licensehttps://data.gov.tw/license

    Area covered
    Chiayi County
    Description

    The permanent population and population density of Chiayi County are published by townships and urban areas in the population and housing census.

  7. Highest population density by country 2024

    • statista.com
    Updated May 27, 2025
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    Statista (2025). Highest population density by country 2024 [Dataset]. https://www.statista.com/statistics/264683/top-fifty-countries-with-the-highest-population-density/
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    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    World
    Description

    Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

  8. a

    Population Density GIS

    • hub.arcgis.com
    • data-sccphd.opendata.arcgis.com
    Updated Aug 24, 2022
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    Santa Clara County Public Health (2022). Population Density GIS [Dataset]. https://hub.arcgis.com/datasets/ea6103fd5a8d461ea4305a7b26334aae
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    Dataset updated
    Aug 24, 2022
    Dataset authored and provided by
    Santa Clara County Public Health
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Table contains total population and population density summarized at county, city, zip code, and census tract level. Population density is defined as number of people residing per square mile of area. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B01001; data accessed on April 11, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (String): Geography IDNAME (String): Name of geographyt_pop (Numeric): Total populationpop_density (Numeric): Area in square milesarea (Numeric): Population density

  9. f

    Data Sources for Each County.

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Melissa P. L. Chan; Robert S. Weinhold; Reuben Thomas; Julia M. Gohlke; Christopher J. Portier (2023). Data Sources for Each County. [Dataset]. http://doi.org/10.1371/journal.pone.0137832.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Melissa P. L. Chan; Robert S. Weinhold; Reuben Thomas; Julia M. Gohlke; Christopher J. Portier
    License

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

    Description

    Data Sources for Each County.

  10. f

    Environmental Predictors of US County Mortality Patterns on a National Basis...

    • plos.figshare.com
    pdf
    Updated May 31, 2023
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    Melissa P. L. Chan; Robert S. Weinhold; Reuben Thomas; Julia M. Gohlke; Christopher J. Portier (2023). Environmental Predictors of US County Mortality Patterns on a National Basis [Dataset]. http://doi.org/10.1371/journal.pone.0137832
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Melissa P. L. Chan; Robert S. Weinhold; Reuben Thomas; Julia M. Gohlke; Christopher J. Portier
    License

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

    Area covered
    United States
    Description

    A growing body of evidence has found that mortality rates are positively correlated with social inequalities, air pollution, elevated ambient temperature, availability of medical care and other factors. This study develops a model to predict the mortality rates for different diseases by county across the US. The model is applied to predict changes in mortality caused by changing environmental factors. A total of 3,110 counties in the US, excluding Alaska and Hawaii, were studied. A subset of 519 counties from the 3,110 counties was chosen by using systematic random sampling and these samples were used to validate the model. Step-wise and linear regression analyses were used to estimate the ability of environmental pollutants, socio-economic factors and other factors to explain variations in county-specific mortality rates for cardiovascular diseases, cancers, chronic obstructive pulmonary disease (COPD), all causes combined and lifespan across five population density groups. The estimated models fit adequately for all mortality outcomes for all population density groups and, adequately predicted risks for the 519 validation counties. This study suggests that, at local county levels, average ozone (0.07 ppm) is the most important environmental predictor of mortality. The analysis also illustrates the complex inter-relationships of multiple factors that influence mortality and lifespan, and suggests the need for a better understanding of the pathways through which these factors, mortality, and lifespan are related at the community level.

  11. N

    Dubuque County, IA Age Cohorts Dataset: Children, Working Adults, and...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Dubuque County, IA Age Cohorts Dataset: Children, Working Adults, and Seniors in Dubuque County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/dubuque-county-ia-population-by-age/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Iowa, Dubuque County
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Dubuque County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Dubuque County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 57,947 (58.56% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Dubuque County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Dubuque County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Dubuque County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Dubuque County Population by Age. You can refer the same here

  12. f

    Analyzing and interpreting spatial and temporal variability of the United...

    • plos.figshare.com
    docx
    Updated Jun 1, 2023
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    Meng Xu; Joel E. Cohen (2023). Analyzing and interpreting spatial and temporal variability of the United States county population distributions using Taylor's law [Dataset]. http://doi.org/10.1371/journal.pone.0226096
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    docxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Meng Xu; Joel E. Cohen
    License

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

    Area covered
    United States
    Description

    We study the spatial and temporal variation of the human population in the United States (US) counties from 1790 to 2010, using an ecological scaling pattern called Taylor's law (TL). TL states that the variance of population abundance is a power function of the mean population abundance. Despite extensive studies of TL for non-human populations, testing and interpreting TL using data on human populations are rare. Here we examine three types of TL that quantify the spatial and temporal variation of US county population abundance. Our results show that TL and its quadratic extension describe the mean-variance relationship of county population distribution well. The slope and statistics of TL reveal economic and demographic trends of the county populations. We propose TL as a useful statistical tool for analyzing human population variability. We suggest new ways of using TL to select and make population projections.

  13. Regression Parameters Derived from Stepwise Regression Analysis of Variables...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Melissa P. L. Chan; Robert S. Weinhold; Reuben Thomas; Julia M. Gohlke; Christopher J. Portier (2023). Regression Parameters Derived from Stepwise Regression Analysis of Variables for Life Expectancy, All-Causes Mortality and Cardiovascular Diseases for Five Population Density Groups. [Dataset]. http://doi.org/10.1371/journal.pone.0137832.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Melissa P. L. Chan; Robert S. Weinhold; Reuben Thomas; Julia M. Gohlke; Christopher J. Portier
    License

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

    Description

    Regression Parameters Derived from Stepwise Regression Analysis of Variables for Life Expectancy, All-Causes Mortality and Cardiovascular Diseases for Five Population Density Groups.

  14. N

    Daggett County, UT Age Cohorts Dataset: Children, Working Adults, and...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Daggett County, UT Age Cohorts Dataset: Children, Working Adults, and Seniors in Daggett County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/daggett-county-ut-population-by-age/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Daggett County, Utah
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Daggett County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Daggett County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 443 (59.30% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Daggett County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Daggett County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Daggett County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Daggett County Population by Age. You can refer the same here

  15. N

    Inyo County, CA Age Cohorts Dataset: Children, Working Adults, and Seniors...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
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    Neilsberg Research (2025). Inyo County, CA Age Cohorts Dataset: Children, Working Adults, and Seniors in Inyo County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/inyo-county-ca-population-by-age/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Inyo County, California
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Inyo County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Inyo County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 10,390 (55.26% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Inyo County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Inyo County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Inyo County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Inyo County Population by Age. You can refer the same here

  16. N

    Menominee County, WI Age Cohorts Dataset: Children, Working Adults, and...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Menominee County, WI Age Cohorts Dataset: Children, Working Adults, and Seniors in Menominee County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/menominee-county-wi-population-by-age/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Menominee County, Wisconsin
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Menominee County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Menominee County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 2,287 (53.74% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Menominee County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Menominee County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Menominee County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Menominee County Population by Age. You can refer the same here

  17. N

    Los Angeles County, CA Age Cohorts Dataset: Children, Working Adults, and...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
    Share
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    Cite
    Neilsberg Research (2025). Los Angeles County, CA Age Cohorts Dataset: Children, Working Adults, and Seniors in Los Angeles County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/4b903ce3-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Los Angeles County, California
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Los Angeles County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Los Angeles County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 6.34 million (64.40% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Los Angeles County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Los Angeles County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Los Angeles County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Los Angeles County Population by Age. You can refer the same here

  18. N

    Bureau County, IL Age Cohorts Dataset: Children, Working Adults, and Seniors...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
    Share
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    Cite
    Neilsberg Research (2025). Bureau County, IL Age Cohorts Dataset: Children, Working Adults, and Seniors in Bureau County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/bureau-county-il-population-by-age/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Bureau County, Illinois
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Bureau County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Bureau County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 18,617 (56.37% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Bureau County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Bureau County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Bureau County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Bureau County Population by Age. You can refer the same here

  19. N

    Pickaway County, OH Age Cohorts Dataset: Children, Working Adults, and...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
    Share
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    Cite
    Neilsberg Research (2025). Pickaway County, OH Age Cohorts Dataset: Children, Working Adults, and Seniors in Pickaway County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/pickaway-county-oh-population-by-age/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Pickaway County, Ohio
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Pickaway County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Pickaway County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 37,135 (62.51% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Pickaway County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Pickaway County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Pickaway County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Pickaway County Population by Age. You can refer the same here

  20. N

    Eau Claire County, WI Age Cohorts Dataset: Children, Working Adults, and...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
    Share
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    Cite
    Neilsberg Research (2025). Eau Claire County, WI Age Cohorts Dataset: Children, Working Adults, and Seniors in Eau Claire County - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/4b7cd0b1-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Eau Claire County, Wisconsin
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Eau Claire County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Eau Claire County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 67,375 (63.37% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Eau Claire County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Eau Claire County is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Eau Claire County is shown in the following column.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Eau Claire County Population by Age. You can refer the same here

Share
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Email
Click to copy link
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Close
Cite
University of South Florida GIS (2024). Population Density in the US 2020 Census [Dataset]. https://hub.arcgis.com/maps/58e4ee07a0e24e28949903511506a8e4

Population Density in the US 2020 Census

Explore at:
Dataset updated
Jun 20, 2024
Dataset authored and provided by
University of South Florida GIS
Area covered
Description

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. 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. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." 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.

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