100+ datasets found
  1. d

    Housing Database by 2020 Census Block

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Mar 22, 2025
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    data.cityofnewyork.us (2025). Housing Database by 2020 Census Block [Dataset]. https://catalog.data.gov/dataset/housing-database-by-census-block
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    Dataset updated
    Mar 22, 2025
    Dataset provided by
    data.cityofnewyork.us
    Description

    Net change in housing units arising from new buildings, demolitions, or alterations for NYC Census Blocks since 2010. The NYC Department of City Planning's (DCP) Housing Database provide the 2010 census count of housing units, the net change in Class A housing units since the census, and the count of units pending completion for commonly used political and statistical boundaries. These tables are aggregated from the DCP Housing Database, which is derived from Department of Buildings (DOB)-approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

  2. P

    Census 2020 Table P2 12011 Blocks

    • data.pompanobeachfl.gov
    • geohub-bcgis.opendata.arcgis.com
    Updated Feb 28, 2023
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    External Datasets (2023). Census 2020 Table P2 12011 Blocks [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-p2-12011-blocks
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    csv, html, geojson, zip, kml, 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 P2 – Hispanic or Latino, and Not Hispanic or Latino by Race 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.

  3. P

    Census 2020 Table H1 12011 County

    • data.pompanobeachfl.gov
    • hub.arcgis.com
    • +3more
    Updated Apr 14, 2022
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    External Datasets (2022). Census 2020 Table H1 12011 County [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-h1-12011-county
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    geojson, arcgis geoservices rest api, csv, zip, kml, htmlAvailable 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 H1 – Occupancy Status 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.

  4. N

    2020 Census Tracts

    • data.cityofnewyork.us
    Updated May 29, 2025
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    Department of City Planning (DCP) (2025). 2020 Census Tracts [Dataset]. https://data.cityofnewyork.us/City-Government/2020-Census-Tracts/63ge-mke6
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    csv, application/rssxml, tsv, kml, kmz, xml, application/rdfxml, application/geo+jsonAvailable download formats
    Dataset updated
    May 29, 2025
    Dataset authored and provided by
    Department of City Planning (DCP)
    Description

    Census Tracts from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file.

    All previously released versions of this data are available at the DCP Website: BYTES of the BIG APPLE.

  5. P

    Census 2020 Table H1 12011 Block Groups

    • data.pompanobeachfl.gov
    • geohub-bcgis.opendata.arcgis.com
    • +2more
    Updated Aug 10, 2023
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    External Datasets (2023). Census 2020 Table H1 12011 Block Groups [Dataset]. https://data.pompanobeachfl.gov/dataset/census-2020-table-h1-12011-block-groups
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    arcgis geoservices rest api, zip, geojson, csv, html, kmlAvailable download formats
    Dataset updated
    Aug 10, 2023
    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 H1 – Occupancy Status 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, 2020 Census State Public Law 94-171 Summary File Technical Documentation.

  6. d

    2020 Census Redistricting Data All Texas - Counties

    • catalog.data.gov
    • datahub.austintexas.gov
    • +1more
    Updated Apr 25, 2025
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    data.austintexas.gov (2025). 2020 Census Redistricting Data All Texas - Counties [Dataset]. https://catalog.data.gov/dataset/2020-census-redistricting-data-all-texas-counties
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    Dataset updated
    Apr 25, 2025
    Dataset provided by
    data.austintexas.gov
    Area covered
    Texas
    Description

    This is 2020 decennial census data at the county level. Technical documentation for the 2020 census is available here: https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_NationalTechDoc.pdf

  7. a

    City Limits (2020 Census)

    • hub.arcgis.com
    • data-nctcoggis.hub.arcgis.com
    Updated May 21, 2021
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    North Central Texas Council of Governments (2021). City Limits (2020 Census) [Dataset]. https://hub.arcgis.com/datasets/dde7be69466146a6992efcf8d42209f6
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    Dataset updated
    May 21, 2021
    Dataset authored and provided by
    North Central Texas Council of Governments
    Area covered
    Description

    This dataset includes 2020 census places as identified or delineated by the U.S. Census Bureau and made available through their TIGER/Line files. Places include both Incorporated Places (usually cities, towns, or villages) and Census Designated Places (statistical entities). Incorporated places are those reported to the Census Bureau as legally in existence as of January 1, 2020. Incorporated places may extend across counties. Census Designated Places (CDPs) are the statistical counterparts of incorporated places. They are settled concentrations of population that are identifiable by name but not legally incorporated under the laws of the state in which the CDPs are located. The Census Bureau defines CDP boundaries in cooperation with local partners. CDP boundaries usually coincide with visible features or the boundary of an adjacent Incorporated Place or another legal entity boundary. They have no legal status, no elected officials to serve traditional municipal functions, and no population size requirements for classification. For more information about census geographies, see https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc_Ch4.pdf . This file is for reference use only. NCTCOG and its members are not responsible for errors or inaccuracies in the file.

  8. D

    2020 Census State Boundaries; PA, NJ, DE & MD

    • staging-catalog.cloud.dvrpc.org
    • catalog.dvrpc.org
    esri feature class +4
    Updated Feb 15, 2025
    + more versions
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    DVRPC (2025). 2020 Census State Boundaries; PA, NJ, DE & MD [Dataset]. https://staging-catalog.cloud.dvrpc.org/dataset/2020-census-state-boundaries-pa-nj-de-md
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    xml, geojson, json, html, esri feature classAvailable download formats
    Dataset updated
    Feb 15, 2025
    Dataset authored and provided by
    DVRPC
    Area covered
    New Jersey, Pennsylvania
    Description

    *USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*

    The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS).

    The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/).

    States and Equivalent Entities are the primary governmental divisions of the United States. In addition to the 50 states, the Census Bureau treats the District of Columbia, Puerto Rico, American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands as the statistical equivalents of states for the purpose of data presentation.

    Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/STATE/ on June 22, 2023

  9. PLACES: Census Tract Data (GIS Friendly Format), 2020 release

    • catalog.data.gov
    • data.virginia.gov
    • +2more
    Updated Jun 28, 2025
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    Centers for Disease Control and Prevention (2025). PLACES: Census Tract Data (GIS Friendly Format), 2020 release [Dataset]. https://catalog.data.gov/dataset/places-census-tract-data-gis-friendly-format-2020-release-2cba8
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    Dataset updated
    Jun 28, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

    This dataset contains model-based census tract level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 27 measures at the census tract level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

  10. g

    Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods |...

    • gimi9.com
    Updated Apr 6, 2022
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    (2022). Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_analysis-neighborhoods-2020-census-tracts-assigned-to-neighborhoods/
    Explore at:
    Dataset updated
    Apr 6, 2022
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    A. SUMMARY This dataset maps 2020 census tracts to Analysis Neighborhoods. The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department originally created the 41 Analysis Neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. They are not codified in Planning Code nor Administrative Code. B. HOW THE DATASET IS CREATED This dataset is produced by mapping the 2020 Census tracts to Analysis neighborhoods. C. UPDATE PROCESS This dataset is static. Changes to the census tract boundaries are tracked in multiple datasets. See here for the 2010 census tracts assigned to neighborhoods D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID, which is the primary key for census tracts in the dataset E. RELATED DATASET 2020 census tract boundaries for San Francisco can be found here

  11. A

    ‘2020 Census Tracts (water areas included) - Tabular’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 13, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘2020 Census Tracts (water areas included) - Tabular’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-2020-census-tracts-water-areas-included-tabular-cb7a/latest
    Explore at:
    Dataset updated
    Feb 13, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘2020 Census Tracts (water areas included) - Tabular’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ea43ac51-bbc0-4d4e-b943-01ed2896c230 on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    2020 Census Tracts (water areas included) from the US Census for New York City. These boundary files are derived from the US Census Bureau's TIGER data products and have been geographically modified to fit the New York City base map. All previously released versions of this data are available at BYTES of the BIG APPLE- Archive.

    --- Original source retains full ownership of the source dataset ---

  12. TIGER: US Census Tracts

    • developers.google.com
    • caribmex.com
    Updated Jan 2, 2020
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    United States Census Bureau (2020). TIGER: US Census Tracts [Dataset]. https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TRACT
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    Dataset updated
    Jan 2, 2020
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Time period covered
    Jan 1, 2020 - Jan 2, 2020
    Area covered
    Description

    The United States Census Bureau regularly releases a geodatabase named TIGER. This dataset contains the 2020 census tracts. Tract areas vary tremendously, but in urban areas are roughly equivalent to a neighborhood. There are just over 85000 polygon features covering the United States, the District of Columbia, Puerto Rico, and the Island areas. For full technical details on all TIGER 2020 products, see the TIGER technical documentation.

  13. N

    Huntingdon County, PA Census Bureau Gender Demographics and Population...

    • neilsberg.com
    Updated Feb 19, 2024
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    Neilsberg Research (2024). Huntingdon County, PA Census Bureau Gender Demographics and Population Distribution Across Age Datasets [Dataset]. https://www.neilsberg.com/research/datasets/e18b5d75-52cf-11ee-804b-3860777c1fe6/
    Explore at:
    Dataset updated
    Feb 19, 2024
    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
    Huntingdon County, Pennsylvania
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Huntingdon County population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Huntingdon County.

    Content

    The dataset constitues the following two datasets across these two themes

    • Huntingdon County, PA Population Breakdown by Gender
    • Huntingdon County, PA Population Breakdown by Gender and Age

    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/.

  14. a

    2020 Census Tracts in Boston

    • bostonopendata-boston.opendata.arcgis.com
    • data.boston.gov
    • +3more
    Updated Apr 21, 2023
    + more versions
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    BostonMaps (2023). 2020 Census Tracts in Boston [Dataset]. https://bostonopendata-boston.opendata.arcgis.com/datasets/boston::2020-census-tracts-in-boston
    Explore at:
    Dataset updated
    Apr 21, 2023
    Dataset authored and provided by
    BostonMaps
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    Description

    Census tracts are created by the U.S. Census Bureau to be small, relatively permanent statistical subdivisions of a county. Census tracts average about 4,000 inhabitants: minimum population –1,200 and maximum population –8,000. Census tracts are split or merged every 10 years, depending on population change, with local feedback through the Participant Statistical Areas Program (PSAP).

  15. N

    Brice, OH annual median income by work experience and sex dataset: Aged 15+,...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). Brice, OH annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/brice-oh-income-by-gender/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 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
    Brice, Ohio
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Brice. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

    Key observations: Insights from 2023

    Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Brice, while the Census reported a median income of $55,134 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

    Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the village of Brice was not possible.

    - Full-time workers, aged 15 years and older: In Brice, among full-time, year-round workers aged 15 years and older, males earned a median income of $98,229, while females earned $38,750, leading to a 61% gender pay gap among full-time workers. This illustrates that women earn 39 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

    Gender classifications include:

    • Male
    • Female

    Employment type classifications include:

    • Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
    • Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

    Variables / Data Columns

    • Year: This column presents the data year. Expected values are 2010 to 2023
    • Male Total Income: Annual median income, for males regardless of work hours
    • Male FT Income: Annual median income, for males working full time, year-round
    • Male PT Income: Annual median income, for males working part time
    • Female Total Income: Annual median income, for females regardless of work hours
    • Female FT Income: Annual median income, for females working full time, year-round
    • Female PT Income: Annual median income, for females working part time

    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 Brice median household income by race. You can refer the same here

  16. TIGER/Line Shapefile, 2020, State, Tennessee, 2020 Census Block

    • s.cnmilf.com
    • catalog.data.gov
    Updated Jan 28, 2024
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch (Point of Contact) (2024). TIGER/Line Shapefile, 2020, State, Tennessee, 2020 Census Block [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/tiger-line-shapefile-2020-state-tennessee-2020-census-block
    Explore at:
    Dataset updated
    Jan 28, 2024
    Dataset provided by
    United States Department of Commercehttp://www.commerce.gov/
    United States Census Bureauhttp://census.gov/
    Area covered
    Tennessee
    Description

    The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2020 Census blocks nest within every other 2020 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.

  17. u

    2020 Census Tracts New Mexico

    • gstore.unm.edu
    csv, geojson, gml +5
    Updated Sep 10, 2021
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    Earth Data Analysis Center (2021). 2020 Census Tracts New Mexico [Dataset]. https://gstore.unm.edu/apps/rgis/datasets/27ac29fd-b6b9-4a93-9258-33091f06e3b1/metadata/FGDC-STD-001-1998.html
    Explore at:
    gml(5), zip(5), geojson(5), xls(5), json(5), kml(5), shp(5), csv(5)Available download formats
    Dataset updated
    Sep 10, 2021
    Dataset provided by
    Earth Data Analysis Center
    Time period covered
    May 23, 2020
    Area covered
    United States, Federal Information Processing Standards (FIPS), ANSI, and feature names., New Mexico, West Bounding Coordinate -109.050431 East Bounding Coordinate -103.002043 North Bounding Coordinate 37.000233 South Bounding Coordinate 31.33216
    Description

    The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2020 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

  18. D

    ARCHIVED: COVID-19 Cases by Population Characteristics Over Time

    • data.sfgov.org
    • healthdata.gov
    • +2more
    application/rdfxml +5
    Updated Sep 11, 2023
    + more versions
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    (2023). ARCHIVED: COVID-19 Cases by Population Characteristics Over Time [Dataset]. https://data.sfgov.org/Health-and-Social-Services/ARCHIVED-COVID-19-Cases-by-Population-Characterist/j7i3-u9ke
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    xml, csv, json, application/rdfxml, tsv, application/rssxmlAvailable download formats
    Dataset updated
    Sep 11, 2023
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

    B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

    Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

    Gender * The City collects information on gender identity using these guidelines.

    Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

    Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

    Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

    Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

    Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

    Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

    C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

    D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

    This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

    New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

    This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

    To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

    E. CHANGE LOG

    • 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
    • 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
    • 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
    • 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
    • 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
    • 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
    • 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
    • 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
    • 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
    • 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

  19. d

    ACS 5-Year Demographic Characteristics DC Census Tract

    • opendata.dc.gov
    • opdatahub.dc.gov
    • +4more
    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.

  20. d

    Loudoun 2020 Census Block Groups

    • catalog.data.gov
    • data.virginia.gov
    • +4more
    Updated Sep 20, 2024
    + more versions
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    Loudoun County GIS (2024). Loudoun 2020 Census Block Groups [Dataset]. https://catalog.data.gov/dataset/loudoun-2020-census-block-groups-f9e6d
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    Dataset updated
    Sep 20, 2024
    Dataset provided by
    Loudoun County GIS
    Area covered
    Loudoun County
    Description

    This GIS layer contains the geographical boundaries of the 2020 census block groups for Loudoun County, Virginia. The 2020 Census block group boundaries are used for Census Bureau statistical data tabulation purposes, including the 2020 Decennial Census and American Community Surveys. Census block groups are part of the sub-county census geography hierarchy of tracts, block groups, and blocks. The three census geographies nest to each other, forming a hierarchy of census tract, followed by block groups, and then blocks, with blocks being the smallest. A census block group is a cluster of census blocks within the same census tract that have the same first digit of their four-digit census block numbers within a census tract. For example, block group 3 within census tract 610700 is a cluster of all the blocks numbered from 3000 to 3999 in that census tract. Block groups are uniquely numbered within census tracts, with the block group's valid range being 0 to 9. Block Groups are designed to be relatively homogeneous units with respect to population characteristics, economic status, and living conditions, census tracts and generally contain between 600 and 3,000 people or 240 and 1,200 housing units. This 2010 Census block group GIS layer's boundaries are based on the U.S. Census Bureau Census 2020 TIGER/Line files. The boundaries are an extract of aerial photography and cartographic information, such as roads and streams, from the Loudoun County GIS system. Census block groups are bounded on all sides by visible features, such as roads, streams, lakes, power lines, and railroad tracks, and/or by non-visible boundaries such as town and county boundaries, and short line-of-sight extensions of streets and roads.

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data.cityofnewyork.us (2025). Housing Database by 2020 Census Block [Dataset]. https://catalog.data.gov/dataset/housing-database-by-census-block

Housing Database by 2020 Census Block

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Dataset updated
Mar 22, 2025
Dataset provided by
data.cityofnewyork.us
Description

Net change in housing units arising from new buildings, demolitions, or alterations for NYC Census Blocks since 2010. The NYC Department of City Planning's (DCP) Housing Database provide the 2010 census count of housing units, the net change in Class A housing units since the census, and the count of units pending completion for commonly used political and statistical boundaries. These tables are aggregated from the DCP Housing Database, which is derived from Department of Buildings (DOB)-approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

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