100+ datasets found
  1. d

    Postal Code Conversion File [Canada], November 2020, Census of Canada 2016

    • search.dataone.org
    • borealisdata.ca
    • +1more
    Updated Dec 11, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statistics Canada (2024). Postal Code Conversion File [Canada], November 2020, Census of Canada 2016 [Dataset]. http://doi.org/10.5683/SP3/ULVZKO
    Explore at:
    Dataset updated
    Dec 11, 2024
    Dataset provided by
    Borealis
    Authors
    Statistics Canada
    Area covered
    Canada
    Description

    The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. The geographic coordinates, which represent the standard geostatistical areas linked to each postal codeOM on the PCCF, are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for marketing, planning, or research purposes. In April 1983, the Statistical Registers and Geography Division released the first version of the PCCF, which linked postal codesOM to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codesOM are directly geocoded to 2016 Census geography while others are linked via various conversion processes. A quality indicator for the confidence of this linkage is available in the PCCF.

  2. TIGER/Line Shapefile, 2020, County, Gwinnett County, GA, Topological Faces...

    • datasets.ai
    • s.cnmilf.com
    • +1more
    55, 57
    Updated Jan 28, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Census Bureau, Department of Commerce (2024). TIGER/Line Shapefile, 2020, County, Gwinnett County, GA, Topological Faces (Polygons With All Geocodes) [Dataset]. https://datasets.ai/datasets/tiger-line-shapefile-2020-county-gwinnett-county-ga-topological-faces-polygons-with-all-geocode
    Explore at:
    57, 55Available download formats
    Dataset updated
    Jan 28, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    U.S. Census Bureau, Department of Commerce
    Area covered
    Georgia, Gwinnett County
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  3. Estimated Cohen’s Kappa and percent disagreement of census tract and block...

    • plos.figshare.com
    xls
    Updated Jan 31, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Tyler Schappe; Lisa M. McElroy; Moronke Ogundolie; Roland Matsouaka; Ursula Rogers; Nrupen A. Bhavsar (2025). Estimated Cohen’s Kappa and percent disagreement of census tract and block group Federal Information Processing Standards (FIPS) assignments resulting from DeGAUSS and vendor tool geocoding process, stratified by urban/rural category. [Dataset]. http://doi.org/10.1371/journal.pone.0317215.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Tyler Schappe; Lisa M. McElroy; Moronke Ogundolie; Roland Matsouaka; Ursula Rogers; Nrupen A. Bhavsar
    License

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

    Description

    Estimated Cohen’s Kappa and percent disagreement of census tract and block group Federal Information Processing Standards (FIPS) assignments resulting from DeGAUSS and vendor tool geocoding process, stratified by urban/rural category.

  4. U.S. Federal Superfund Sites

    • kaggle.com
    zip
    Updated Nov 17, 2017
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    4d4stra (2017). U.S. Federal Superfund Sites [Dataset]. https://www.kaggle.com/srrobert50/federal-superfunds
    Explore at:
    zip(321301070 bytes)Available download formats
    Dataset updated
    Nov 17, 2017
    Authors
    4d4stra
    License

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

    Area covered
    United States
    Description

    Context

    Federal Superfund sites are some of the most polluted in the United States. This dataset contains a multifaceted view of Superfunds, including free-form text descriptions, geography, demographics and socioeconomics.

    Content

    The core data was scraped from the National Priorities List (NPL) provided by the U.S. Environmental Protection Agency (EPA). This table provides basic information such as site name, site score, date added, and links to a site description and current status. Apache Tika was used to extract text from the site description pdfs. The addresses were scraped from site status pages, and used to geocode to latitude and longitude and Census block group. The block group assignment was used to join with the Census Bureau's planning database, a rich source of nationwide demographic and socioeconomic data. The full source code used to generate the data can be found here, on github.

    I have provided three separate downloads to explore:

    • priorities_list_full.json: the NPL containing all geographic, site information, text descriptions, and Census Bureau data from the relevant block groups.
    • pdb_tract.csv: the planning database aggregated on the tract level with an additional indicator (has_superfund) noting whether or not the tract contains the address of a Superfund site.
    • pdb_block_group.csv: the planning database aggregated on the block group level with an additional indicator (has_superfund) noting whether or not the block group contains the address of a Superfund site.

    Some caveats:

    1. The planning database contains 300+ columns. For a full description of these columns, please see the documentation here.
    2. Since the Google geocoder is relatively aggressive in providing address matches, geocoding was done through a hierarchy of queries (full address, city-state-zip, and zipcode only) to prevent gross errors. The address string used to geocode is noted through the 'geocode_source' column.
    3. While this data is linked to demographic and socioeconomic data based on either the block group (tract for pdb_tract.csv), the impacts of a particular site's pollution may extend beyond these geographic regions.

    Acknowledgements

    I would like to thank the EPA and the Census Bureau for making such detailed information publicly available. For relevant academic work, please see Burwell-Naney et al. (2013) and references, both to and therein.

    Please let me know if you have any suggestions for improving the dataset!

  5. Results of likelihood ratio tests for differences in percent disagreement...

    • plos.figshare.com
    xls
    Updated Jan 31, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Tyler Schappe; Lisa M. McElroy; Moronke Ogundolie; Roland Matsouaka; Ursula Rogers; Nrupen A. Bhavsar (2025). Results of likelihood ratio tests for differences in percent disagreement among strata of census tract and block group assignments between DeGAUSS and vendor tool geocoding. [Dataset]. http://doi.org/10.1371/journal.pone.0317215.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Tyler Schappe; Lisa M. McElroy; Moronke Ogundolie; Roland Matsouaka; Ursula Rogers; Nrupen A. Bhavsar
    License

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

    Description

    Results of likelihood ratio tests for differences in percent disagreement among strata of census tract and block group assignments between DeGAUSS and vendor tool geocoding.

  6. g

    Geocoded data from the census of licences and clubs from sports federations...

    • gimi9.com
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Geocoded data from the census of licences and clubs from sports federations approved by the Ministry responsible for sports | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_53699ebba3a729239d205f58
    Explore at:
    License

    Licence Ouverte / Open Licence 1.0https://www.etalab.gouv.fr/wp-content/uploads/2014/05/Open_Licence.pdf
    License information was derived automatically

    Description

    The annual inventory of licences from sports federations approved by the Ministry responsible for sports makes it possible to measure the level and evolution over time of supervised sports practice. These statistics shed light on public policies for the development of sport, both at national and territorial level. This is a census at the person's place of residence and not at the place of practice. The data from the census are then geocoded by INSEE for metropolis + DROM (excluding Mayotte), in order to be able to communicate these files at the municipal level. Data are not available for all federations. A number of them did not have fully geolocatable data to the municipality allowing exhaustive exploitation. The geocoded data have therefore been processed in order to be able to provide a estimate of the number of licences per municipality and federation. The data for vintage N correspond to season N-1/N or calendar year N depending on the functioning of the federations (e.g. lic-data-2021 is a distribution of licenses for the 2020/2021 season or the year 2021). The 2019 data have been revised (2nd geocoding operation required). From 2019, some changes have been made in the files transmitted: -Common precision level-QPV and no longer common -Age steps of the licence census and not of the five-year census -Population data not included in the file -Distinction of out-of-field data (Mayotte, Monaco, COM, Foreign) vs. undistributed data -Data for the municipalities of Mayotte not included (excluding geocoding) -Addition of licenses not distributed in the file (sum of licenses corresponds to the result of the census) -The distribution for 3 federations is limited to the department level (FF Maccabi, FS of the National Police, F of the defense clubs)

  7. d

    Postal Code Conversion File [Canada], November 2014, Census of Canada 2011

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 11, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Geography Division (2024). Postal Code Conversion File [Canada], November 2014, Census of Canada 2011 [Dataset]. http://doi.org/10.5683/SP3/WUZCRR
    Explore at:
    Dataset updated
    Dec 11, 2024
    Dataset provided by
    Borealis
    Authors
    Geography Division
    Area covered
    Canada
    Description

    The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. The geographic coordinates, which represent the standard geostatistical areas linked to each postal codeOM on the PCCF, are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for marketing, planning, or research purposes. In April 1983, the Statistical Registers and Geography Division released the first version of the PCCF, which linked postal codesOM to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codesOM are directly geocoded to 2011 Census geography while others are linked via various conversion processes. A quality indicator for the confidence of this linkage is available in the PCCF.

  8. TIGER/Line Shapefile, 2022, County, Clermont County, OH, Topological Faces...

    • catalog.data.gov
    • s.cnmilf.com
    Updated Jan 27, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch (Point of Contact) (2024). TIGER/Line Shapefile, 2022, County, Clermont County, OH, Topological Faces (Polygons With All Geocodes) [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-2022-county-clermont-county-oh-topological-faces-polygons-with-all-geocode
    Explore at:
    Dataset updated
    Jan 27, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Clermont County
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  9. a

    Census Block Group Map

    • hub.arcgis.com
    • tea-texas.hub.arcgis.com
    • +3more
    Updated Jul 15, 2019
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Texas Education Agency (2019). Census Block Group Map [Dataset]. https://hub.arcgis.com/datasets/TEA-Texas::census-block-group-map/api
    Explore at:
    Dataset updated
    Jul 15, 2019
    Dataset authored and provided by
    Texas Education Agency
    Area covered
    Description

    The map provide functions for individual to look up locations and the boundaries of Census Block Group numbers by address or Census Block Group Number. The data resources are based on Esri ArcGIS (www.arcgis.com) and Census Block 2010 Data (www.census.gov/). It covers Census Block's demographic information which are population, race, gender, age, and household. The geocoder which used through the Esri ArcGIS may not be able to provide rooftop accuracy since it is that the addresses are in the range dataset instead of the accurate points. The spatial data may haven't been updated to cause error. You can find additional information .You can find additional information on https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?ref=addr&refresh=t#.

  10. TIGER/Line Shapefile, 2020, County, Cuyahoga County, OH, Topological Faces...

    • catalog.data.gov
    • s.cnmilf.com
    Updated Jan 27, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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, County, Cuyahoga County, OH, Topological Faces (Polygons With All Geocodes) [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-2020-county-cuyahoga-county-oh-topological-faces-polygons-with-all-geocode
    Explore at:
    Dataset updated
    Jan 27, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Cuyahoga County
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  11. H

    Replication Data for: Minmaxing of Bayesian Improved Surname and Geography...

    • dataverse.harvard.edu
    Updated Sep 29, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Jesse Clark; John Curiel; Tyler Steelman (2022). Replication Data for: Minmaxing of Bayesian Improved Surname and Geography Level Ups in Predicting Race [Dataset]. http://doi.org/10.7910/DVN/IH7ICK
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 29, 2022
    Dataset provided by
    Harvard Dataverse
    Authors
    Jesse Clark; John Curiel; Tyler Steelman
    License

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

    Area covered
    United States
    Description

    Racial identification is a critical factor in understanding a multitude of important outcomes in many fields. However, inferring an individual’s race from ecological data is prone to bias and error. This process was only recently improved via Bayesian Improved Surname Geocoding (BISG). With surname and geographic-based demographic data, it is possible to more accurately estimate individual racial identification than ever before. However, the level of geography used in this process varies widely. Whereas some existing work makes use of geocoding to place individuals in precise census blocks, a substantial portion either skips geocoding altogether or relies on estimation using surname or county-level analyses. Presently, the tradeoffs of such variation are unknown. In this letter we quantify those tradeoffs through a validation of BISG on Georgia’s voter file using both geocoded and non-geocoded processes and introduce a new level of geography--ZIP codes--to this method. We find that when estimating the racial identification of White and Black voters, non-geocoded ZIP code-based estimates are acceptable alternatives. However, census blocks provide the most accurate estimations when imputing racial identification for Asian and Hispanic voters. Our results document the most efficient means to sequentially conduct BISG analysis to maximize racial identification estimation while simultaneously minimizing data missingness and bias.

  12. TIGER/Line Shapefile, 2020, County, Onondaga County, NY, Topological Faces...

    • catalog.data.gov
    • s.cnmilf.com
    Updated Jan 28, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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, County, Onondaga County, NY, Topological Faces (Polygons With All Geocodes) [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-2020-county-onondaga-county-ny-topological-faces-polygons-with-all-geocode
    Explore at:
    Dataset updated
    Jan 28, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    United States Department of Commercehttp://commerce.gov/
    Area covered
    New York, Onondaga County
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  13. d

    Postal Code Conversion File [Canada], July 2009, Census of Canada 2006

    • search.dataone.org
    Updated Dec 18, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statistics Canada. Geography Division (2024). Postal Code Conversion File [Canada], July 2009, Census of Canada 2006 [Dataset]. http://doi.org/10.5683/SP3/ZRJPL9
    Explore at:
    Dataset updated
    Dec 18, 2024
    Dataset provided by
    Borealis
    Authors
    Statistics Canada. Geography Division
    Area covered
    Canada
    Description

    The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. In April 1983, the Geography Division released the first version of the PCCF, which linked postal codes to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codes are directly geocoded to 2006 Census geography. This improves precision of the file over the previous conversion process used to align postal code linkages to new geographic areas after each census. About 94% of the postal codes were linked to geographic areas using the new automated process. A quality indicator for the confidence of this linkage is available in the PCCF.

  14. TIGER/Line Shapefile, 2022, County, Pinellas County, FL, Topological Faces...

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Jan 27, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch (Point of Contact) (2024). TIGER/Line Shapefile, 2022, County, Pinellas County, FL, Topological Faces (Polygons With All Geocodes) [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-2022-county-pinellas-county-fl-topological-faces-polygons-with-all-geocode
    Explore at:
    Dataset updated
    Jan 27, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Pinellas County, Florida
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  15. TIGER/Line Shapefile, 2022, County, Campbell County, SD, Topological Faces...

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Jan 28, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch (Point of Contact) (2024). TIGER/Line Shapefile, 2022, County, Campbell County, SD, Topological Faces (Polygons With All Geocodes) [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-2022-county-campbell-county-sd-topological-faces-polygons-with-all-geocode
    Explore at:
    Dataset updated
    Jan 28, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Campbell County
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  16. TIGER/Line Shapefile, 2020, County, St. Louis County, MO, Topological Faces...

    • datasets.ai
    • s.cnmilf.com
    • +1more
    55, 57
    Updated Jan 28, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Census Bureau, Department of Commerce (2024). TIGER/Line Shapefile, 2020, County, St. Louis County, MO, Topological Faces (Polygons With All Geocodes) [Dataset]. https://datasets.ai/datasets/tiger-line-shapefile-2020-county-st-louis-county-mo-topological-faces-polygons-with-all-geocode
    Explore at:
    57, 55Available download formats
    Dataset updated
    Jan 28, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    U.S. Census Bureau, Department of Commerce
    Area covered
    Missouri, St. Louis County
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  17. Data from: Public Housing Developments

    • opendata.atlantaregional.com
    • data.lojic.org
    • +1more
    Updated Mar 2, 2016
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Department of Housing and Urban Development (2016). Public Housing Developments [Dataset]. https://opendata.atlantaregional.com/datasets/5c96143f79c940a0a8cedae99a1ac562
    Explore at:
    Dataset updated
    Mar 2, 2016
    Dataset provided by
    United States Department of Housing and Urban Developmenthttp://www.hud.gov/
    Authors
    Department of Housing and Urban Development
    Area covered
    Description

    HUD furnishes technical and professional assistance in planning, developing and managing these developments. Public Housing Developments are depicted as a distinct address chosen to represent the general location of an entire Public Housing Development, which may be comprised of several buildings scattered across a community. The building with the largest number of units is selected to represent the location of the development. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Data Dictionary: DD_Public Housing Developments

  18. e

    GIS Shapefile - Telephone Survey 2006, Geocoded, Baltimore County

    • portal.edirepository.org
    • search.dataone.org
    zip
    Updated Sep 10, 2004
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Jarlath O'Neil-Dunne (2004). GIS Shapefile - Telephone Survey 2006, Geocoded, Baltimore County [Dataset]. http://doi.org/10.6073/pasta/251e295195064f1dbf1feed5fad47140
    Explore at:
    zip(651 kilobyte)Available download formats
    Dataset updated
    Sep 10, 2004
    Dataset provided by
    EDI
    Authors
    Jarlath O'Neil-Dunne
    Time period covered
    Jan 1, 1999 - Dec 31, 2011
    Area covered
    Description

    Tags

       survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES
    
    
    
    
       Summary
    
    
       BES Research, Applications, and Education
    
    
       Description
    
    
       Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 
    
    
       The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 
    
    
    
       The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 
    
    
       The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 
    
    
       Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.
    
    
       This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because
    
  19. TIGER/Line Shapefile, 2020, County, Delaware County, NY, Topological Faces...

    • s.cnmilf.com
    • datasets.ai
    • +1more
    Updated Jan 27, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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, County, Delaware County, NY, Topological Faces (Polygons With All Geocodes) [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/tiger-line-shapefile-2020-county-delaware-county-ny-topological-faces-polygons-with-all-geocode
    Explore at:
    Dataset updated
    Jan 27, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    United States Department of Commercehttp://commerce.gov/
    Area covered
    Delaware County, New York
    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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

  20. f

    HOLC Frequency in real and virtual enumeration districts.

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jan 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Shuo Jim Huang; Michel Boudreaux; Kellee White Whilby; Rozalina G. McCoy; Neil Jay Sehgal (2025). HOLC Frequency in real and virtual enumeration districts. [Dataset]. http://doi.org/10.1371/journal.pgph.0004067.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    PLOS Global Public Health
    Authors
    Shuo Jim Huang; Michel Boudreaux; Kellee White Whilby; Rozalina G. McCoy; Neil Jay Sehgal
    License

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

    Description

    HOLC Frequency in real and virtual enumeration districts.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Statistics Canada (2024). Postal Code Conversion File [Canada], November 2020, Census of Canada 2016 [Dataset]. http://doi.org/10.5683/SP3/ULVZKO

Postal Code Conversion File [Canada], November 2020, Census of Canada 2016

Explore at:
Dataset updated
Dec 11, 2024
Dataset provided by
Borealis
Authors
Statistics Canada
Area covered
Canada
Description

The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. The geographic coordinates, which represent the standard geostatistical areas linked to each postal codeOM on the PCCF, are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for marketing, planning, or research purposes. In April 1983, the Statistical Registers and Geography Division released the first version of the PCCF, which linked postal codesOM to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codesOM are directly geocoded to 2016 Census geography while others are linked via various conversion processes. A quality indicator for the confidence of this linkage is available in the PCCF.

Search
Clear search
Close search
Google apps
Main menu