97 datasets found
  1. U

    U.S. block-level population density rasters for 1990, 2000, and 2010

    • data.usgs.gov
    • dataone.org
    • +3more
    Updated Jan 6, 2025
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    James Falcone (2025). U.S. block-level population density rasters for 1990, 2000, and 2010 [Dataset]. http://doi.org/10.5066/F74J0C6M
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    Dataset updated
    Jan 6, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    James Falcone
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    United States
    Description

    This dataset consists of three raster datasets representing population density for the years 1990, 2000, and 2010. All three rasters are based on block-level census geography data. The 1990 and 2000 data are derived from data normalized to 2000 block boundaries, while the 2010 data are based on 2010 block boundaries. The 1990 and 2000 data are rasters at 100-meter (m) resolution, while the 2010 data are at 60-m resolution. See details about each dataset in the specific metadata for each raster.

  2. a

    POPULATION Per 10m Cell 2000 NBEP2017 (raster)

    • hub.arcgis.com
    • narragansett-bay-estuary-program-nbep.hub.arcgis.com
    Updated Feb 4, 2020
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    NBEP_GIS (2020). POPULATION Per 10m Cell 2000 NBEP2017 (raster) [Dataset]. https://hub.arcgis.com/datasets/56e6888c71084dc6a541d1056ede28d5
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    Dataset updated
    Feb 4, 2020
    Dataset authored and provided by
    NBEP_GIS
    Description

    Population density in 2000 within the boundaries of the Narragansett Bay watershed, the Southwest Coastal Ponds watershed, and the Little Narragansett Bay watershed. The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 2000 10m cell population density raster was produced using Rhode Island (2003-2004) state land use data, Massachusetts (1999) state land use, Connecticut (2001) NLCD land use data, and U.S. Census data (2000). To generate a population estimate (number of persons) for any given area within the boundaries of this raster, use the Zonal Statistics as Table tool to sum the 10m cell density values within your zone dataset (e.g., watershed polygon layer). For more information, please reference the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org).

  3. d

    Georeferenced Population Datasets of Mexico (GEO-MEX): Raster Based GIS...

    • catalog.data.gov
    • gimi9.com
    • +4more
    Updated Apr 24, 2025
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    SEDAC (2025). Georeferenced Population Datasets of Mexico (GEO-MEX): Raster Based GIS Coverage of Mexican Population [Dataset]. https://catalog.data.gov/dataset/georeferenced-population-datasets-of-mexico-geo-mex-raster-based-gis-coverage-of-mexican-p
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    SEDAC
    Area covered
    Mexico
    Description

    The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

  4. Missouri Raster Data

    • figshare.com
    tiff
    Updated Jul 7, 2023
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    Melanie Boudreau (2023). Missouri Raster Data [Dataset]. http://doi.org/10.6084/m9.figshare.23646456.v1
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    tiffAvailable download formats
    Dataset updated
    Jul 7, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Melanie Boudreau
    License

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

    Description

    Rasters assocaited with elevation (from the National elevation dataset), slope (created from the elevation dataset using ArcGIS), a Shannon diversity index as a metric of landscape fragmentation (created from the forest/shrub layer using Fragstats), distance to all roads (created in ArcGIS using a road TIGER shapefile), distance to forest/shrubs (created using NLCD 2016 data), human population density (created using data from the US Census Bureau). All rasters are at a 90m resolution.

  5. n

    Gridded Population of the World, Version 4 (GPWv4): Population Density,...

    • earthdata.nasa.gov
    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    • +2more
    Updated Jun 17, 2025
    + more versions
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    ESDIS (2025). Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 [Dataset]. http://doi.org/10.7927/H49C6VHW
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    Dataset updated
    Jun 17, 2025
    Dataset authored and provided by
    ESDIS
    Area covered
    World
    Description

    The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020.�A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions.

  6. c

    Wildfire Risk to Communities Housing Unit Density (Image Service)

    • resilience.climate.gov
    • agdatacommons.nal.usda.gov
    • +8more
    Updated Apr 14, 2021
    + more versions
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    U.S. Forest Service (2021). Wildfire Risk to Communities Housing Unit Density (Image Service) [Dataset]. https://resilience.climate.gov/datasets/6f49e46d5a2743c8bef156f1d7157121
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    Dataset updated
    Apr 14, 2021
    Dataset authored and provided by
    U.S. Forest Service
    License

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

    Area covered
    Description

    The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

  7. a

    POPULATION CHANGE 1990-2010 NBEP2017 (raster)

    • hub.arcgis.com
    • narragansett-bay-estuary-program-nbep.hub.arcgis.com
    Updated Feb 4, 2020
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    NBEP_GIS (2020). POPULATION CHANGE 1990-2010 NBEP2017 (raster) [Dataset]. https://hub.arcgis.com/datasets/249ce397feee4612a0434f7019d292b1
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    Dataset updated
    Feb 4, 2020
    Dataset authored and provided by
    NBEP_GIS
    Description

    Raster dataset of a kernel density analysis of population change from 1990 to 2010 in the Narragansett Bay, Little Narragansett Bay, and Southwest Coastal ponds watersheds. The raster is for visualization of population change, showing where population has moved within the watershed, and where those changes were most substantial. This dataset was developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. For more information, please reference the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org).

  8. n

    Gridded Population of the World, Version 4 (GPWv4): Population Density,...

    • earthdata.nasa.gov
    Updated Jun 17, 2025
    + more versions
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    ESDIS (2025). Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 10 [Dataset]. http://doi.org/10.7927/H4DZ068D
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    Dataset updated
    Jun 17, 2025
    Dataset authored and provided by
    ESDIS
    Area covered
    World, Earth
    Description

    The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 10 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions to produce density rasters at these resolutions.

  9. s

    Population density grid for 2006 based on GEOSTAT data (raster)

    • geodcat-ap.semic.eu
    • sdi.eea.europa.eu
    Updated Nov 30, 2021
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    (2021). Population density grid for 2006 based on GEOSTAT data (raster) [Dataset]. https://geodcat-ap.semic.eu/csw-4-web/eea-csw/resource/d82219d7-79bd-416b-81b7-c521c6cbc835
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    https://geodcat-ap.semic.eu/csw-4-web/eea-csw/resource/d82219d7-79bd-416b-81b7-c521c6cbc835#_sid=rd30, https://geodcat-ap.semic.eu/csw-4-web/eea-csw/resource/d82219d7-79bd-416b-81b7-c521c6cbc835#_sid=rd26Available download formats
    Dataset updated
    Nov 30, 2021
    Variables measured
    https://geodcat-ap.semic.eu/csw-4-web/eea-csw/resource/d82219d7-79bd-416b-81b7-c521c6cbc835
    Description

    This dataset contains the number of inhabitants per km² for the reference year 2006 and located within the Grid_ETRS89-LAEA_1K. The data set should be referred to GEOSTAT_Grid_POP_2006_1K. The dataset is compiled from the following data sources: aggregated residential population for the year 2006 (AT, SE, FI, SI, NL); estimated residential population for the year 2006 based on mixed national sources (EE, PT, FR, NO, PL, UK (England, Wales)); disaggregated residential population for the year 2006 using using population statistics at LAU2 level for 2006 as data input and Soil Sealing and Corine LC 2006 (BE, BG, CH, CZ, DE, EL, ES, HU, IE, IS, IT, LI, LT, LU, LV, MT, RO, SK, UK (Scotland, Northern Ireland) as ancillary data for the disaggregation. No data available for CY due to absent LAU2 data for Cyprus for the reference year 2006. The dataset is based on a product of the GEOSTAT project which is supported by the European Commission and the European Forum for Geostatistics EFGS. This abstract is based on the abstract provided with the original dataset (CSV file).

  10. d

    Digital data sets describing population density in the conterminous US

    • catalog.data.gov
    • datadiscoverystudio.org
    • +2more
    Updated Sep 2, 2024
    + more versions
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    U.S. Geological Survey (2024). Digital data sets describing population density in the conterminous US [Dataset]. https://catalog.data.gov/dataset/digital-data-sets-describing-population-density-in-the-conterminous-us
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    Dataset updated
    Sep 2, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Contiguous United States, United States
    Description

    Grid of population density in the conterminous United States at a resolution of one kilometer. The grid was converted from an ASCII file obtained from the Consortium for International Earth Science Information Network (CIESIN).

  11. Wildfire Risk to Communities Population Density (Image Service)

    • resilience.climate.gov
    • s.cnmilf.com
    • +7more
    Updated Apr 14, 2021
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    U.S. Forest Service (2021). Wildfire Risk to Communities Population Density (Image Service) [Dataset]. https://resilience.climate.gov/datasets/2770d391dd894782b567a6becc4b32fd
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    Dataset updated
    Apr 14, 2021
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    U.S. Forest Service
    License

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

    Area covered
    Description

    The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

  12. Z

    GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Mar 24, 2025
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    Vanderhaeghe, Floris (2025). GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10 hierarchical cell address levels at the original resolution [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3354403
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    Dataset updated
    Mar 24, 2025
    Dataset authored and provided by
    Vanderhaeghe, Floris
    License

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

    Description

    The data source file is a 10-layered GeoTIFF file, derived from the raster data source GRTSmaster_habitats (link). Both GeoTIFFs (GRTSmaster_habitats, GRTSmh_brick) use the INT4S datatype. The GRTSmh_brick data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from GRTSmaster_habitats (with GRTS cell addresses at the resolution level).

    See R-code in the GitHub repository 'n2khab-preprocessing' at commit ecadaf5 for its creation from the GRTSmaster_habitats data source.

    A reading function to return the data source in a standardized way into the R environment is provided by the R-package n2khab.

    The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector 32 * 2^(0:9) (minimum: 32 meters, maximum: 16384 meters).

    Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).

  13. w

    GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000,...

    • data.wu.ac.at
    n/a
    Updated Nov 29, 2016
    + more versions
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    JRC DataCatalogue (2016). GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015) [Dataset]. https://data.wu.ac.at/odso/drdsi_jrc_ec_europa_eu/NTQ0YTVjMWQtNTJmOS00Mzc2LTlmZGYtMWMxNWU2MzczM2Y2
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    n/aAvailable download formats
    Dataset updated
    Nov 29, 2016
    Dataset provided by
    JRC DataCatalogue
    Description

    The Global Human Settlement Layer (GHSL) project is supported by European Commission, Joint Research Center and Directorate-General for Regional and Urban Policy. The GHSL produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet.

    The GHSL relies on the design and implementation of new spatial data mining technologies allowing to process automatically and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources.

    This spatial raster dataset depicts the distribution and density of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

  14. E

    UK gridded population based on Census 2011 and Land Cover Map 2007

    • catalogue.ceh.ac.uk
    • cloud.csiss.gmu.edu
    • +3more
    Updated Feb 15, 2016
    + more versions
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    S. Reis; S. Steinle; E. Carnell; D. Leaver; M. Vieno; R. Beck; U. Dragosits (2016). UK gridded population based on Census 2011 and Land Cover Map 2007 [Dataset]. http://doi.org/10.5285/61f10c74-8c2c-4637-a274-5fa9b2e5ce44
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    Dataset updated
    Feb 15, 2016
    Dataset provided by
    NERC EDS Environmental Information Data Centre
    Authors
    S. Reis; S. Steinle; E. Carnell; D. Leaver; M. Vieno; R. Beck; U. Dragosits
    License

    https://eidc.ceh.ac.uk/licences/open-government-licence-ceh-ons/plainhttps://eidc.ceh.ac.uk/licences/open-government-licence-ceh-ons/plain

    Time period covered
    Jan 1, 2011 - Dec 31, 2011
    Area covered
    Description

    This dataset contains gridded population with a spatial resolution of 1 km x 1 km for the UK based on Census 2011 and Land Cover Map 2007 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies . While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2011 UK Census population data on Output Area level with Land Cover Map 2007 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum).

  15. a

    Infrastructure and population impacted by 1 meter sea level rise

    • keep-cool-global-community.hub.arcgis.com
    • ai-climate-hackathon-global-community.hub.arcgis.com
    Updated Nov 30, 2022
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    ArcGIS Living Atlas Team (2022). Infrastructure and population impacted by 1 meter sea level rise [Dataset]. https://keep-cool-global-community.hub.arcgis.com/maps/0d3b5964407e465ab23df87fab3a09a9
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    Dataset updated
    Nov 30, 2022
    Dataset authored and provided by
    ArcGIS Living Atlas Team
    Area covered
    Description

    This map illustrates where infrastructure and population could be potentially impacted by a one meter sea level rise by the year 2100. Examples of infrastructure: airports, education establishments, medical facilities, and buildings. The pattern is shown along coastal areas by both tracts and counties. The sea level rise model comes from the Climate Mapping Resilience and Adaptation (CMRA) portal. As you zoom into the map, you can see the pattern by where human settlement exists. This helps illustrate the pattern by where people live.Airport data: Airports (National) - National Geospatial Data Asset (NGDA) AirportsData can be accessed hereOpenStreetMap Data:BuildingsMedical FacilitiesEducation EstablishmentsPopulation data: ACS Table(s): B01001Data downloaded from: Census Bureau's API for American Community Survey Data can be accessed hereHuman Settlement data:WorldPop Population Density 2000-2020 100mData can be accessed hereAbout the CMRA data:The Climate Mapping Resilience and Adaptation (CMRA) portal provides a variety of information for state, local, and tribal community resilience planning. A key tool in the portal is the CMRA Assessment Tool, which summaries complex, multidimensional raster climate projections for thresholded temperature, precipitation, and sea level rise variables at multiple times and emissions scenarios. This layer provides the geographical summaries. What's included?Census 2019 counties and tracts; 2021 American Indian/Alaska Native/Native Hawaiian areas25 Localized Constructed Analogs (LOCA) data variables (only 16 of 25 are present for Hawaii and territories)Time periods / climate scenarios: historical; RCP 4.5 early-, mid-, and late-century; RCP 8.5 early-, mid-, and late-centuryStatistics: minimum, mean, maximumSeal level rise (CONUS only)Original Layers in Living Atlas:U.S. Climate Thresholds (LOCA)U.S. Sea Level Rise Source Data:Census TIGER/Line dataAmerican Indian, Alaska Native, and Native Hawaiian areasLOCA data (CONUS)LOCA data (Hawaii and territories)Sea level rise

  16. f

    Population raster

    • figshare.com
    tiff
    Updated Jan 13, 2021
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    Nandi (2021). Population raster [Dataset]. http://doi.org/10.6084/m9.figshare.13542110.v1
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    tiffAvailable download formats
    Dataset updated
    Jan 13, 2021
    Dataset provided by
    figshare
    Authors
    Nandi
    License

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

    Description

    Population raster approximately 5x5km2 across Madagascar.

  17. u

    Population distribution - 1 km (GHSL - JRC)

    • datacore-gn.unepgrid.ch
    Updated Sep 29, 2016
    + more versions
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    Joint Resaerch Centre (JRC) (2016). Population distribution - 1 km (GHSL - JRC) [Dataset]. https://datacore-gn.unepgrid.ch/geonetwork/srv/api/records/a8ffab32-99cb-4590-a717-6c57d7d8fd45
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    www:link-1.0-http--linkAvailable download formats
    Dataset updated
    Sep 29, 2016
    Dataset provided by
    Joint Resaerch Centre (JRC)
    Time period covered
    1975 - 2015
    Area covered
    Description

    Population counts at 1000 m resolution.

    These spatial raster datasets depict the distribution and density of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000, and 2015 provided by CIESIN were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layers for 1975, 1990, 2000, and 2014. Values are expressed as decimals (‘Float’).”

  18. GHS-POP R2015A - GHS population grid, derived from GPW4, multitemporal...

    • data.europa.eu
    tiff
    Updated Jan 12, 2015
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    Joint Research Centre (2015). GHS-POP R2015A - GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015) - OBSOLETE RELEASE [Dataset]. https://data.europa.eu/data/datasets/jrc-ghsl-ghs_pop_gpw4_globe_r2015a?locale=en
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    tiffAvailable download formats
    Dataset updated
    Jan 12, 2015
    Dataset authored and provided by
    Joint Research Centrehttps://joint-research-centre.ec.europa.eu/index_en
    License

    http://data.europa.eu/eli/dec/2011/833/ojhttp://data.europa.eu/eli/dec/2011/833/oj

    Description

    OBSOLETE RELEASE Get the latest release at https://ghsl.jrc.ec.europa.eu/download.php

    The Global Human Settlement Layer (GHSL) project is supported by European Commission, Joint Research Center and Directorate-General for Regional and Urban Policy. The GHSL produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet.

    The GHSL relies on the design and implementation of new spatial data mining technologies allowing to process automatically and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources.

    This spatial raster dataset depicts the distribution and density of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

  19. c

    Gridded Population of the World, Version 4 (GPWv4): Basic Demographic...

    • s.cnmilf.com
    • data.nasa.gov
    • +3more
    Updated Apr 24, 2025
    + more versions
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    SEDAC (2025). Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/gridded-population-of-the-world-version-4-gpwv4-basic-demographic-characteristics-revision
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    SEDAC
    Area covered
    World, Earth
    Description

    The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010. To estimate the male and female populations by age in 2010, the proportions of males and females in each 5-year age group from ages 0-4 to ages 85+ for the given census year were calculated. These proportions were then applied to the 2010 estimates of the total population to obtain 2010 estimates of male and female populations by age. In some cases, the spatial resolution of the age and sex proportions was coarser than the resolution of the total population estimates to which they were applied. The population density rasters were created by dividing the population count rasters by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

  20. a

    North America Population Density 2020

    • hub.arcgis.com
    Updated Apr 19, 2023
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    CECAtlas (2023). North America Population Density 2020 [Dataset]. https://hub.arcgis.com/maps/1d0db1455e014ffe92ea4265145f045b
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    Dataset updated
    Apr 19, 2023
    Dataset authored and provided by
    CECAtlas
    License
    Area covered
    Description

    The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1-degree resolutions to produce density rasters at these resolutions.Source: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). Available at https://doi.org/10.7927/H49C6VHW. (October 2022)Files Download

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James Falcone (2025). U.S. block-level population density rasters for 1990, 2000, and 2010 [Dataset]. http://doi.org/10.5066/F74J0C6M

U.S. block-level population density rasters for 1990, 2000, and 2010

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20 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jan 6, 2025
Dataset provided by
United States Geological Surveyhttp://www.usgs.gov/
Authors
James Falcone
License

U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically

Area covered
United States
Description

This dataset consists of three raster datasets representing population density for the years 1990, 2000, and 2010. All three rasters are based on block-level census geography data. The 1990 and 2000 data are derived from data normalized to 2000 block boundaries, while the 2010 data are based on 2010 block boundaries. The 1990 and 2000 data are rasters at 100-meter (m) resolution, while the 2010 data are at 60-m resolution. See details about each dataset in the specific metadata for each raster.

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