United States of America GIS data
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ACS 2017 Legal and Statistical Entities Web Map Service; January 1, 2017 vintage
Legal and Statistical Entities Web Map Service; January 1, 2020 vintage
States and Counties (or statistically equivalent entities); January 1, 2015 vintage; Generalized
Congressional Districts, Voting Districts, State Legislative Districts - Upper, State Legislative Districts - Lower; January 1, 2015 vintage; Generalized
This dataset contains georeferenced aerial imagery from 1927 and 1930 covering the District of Columbia. The original photographs were created as part of early aerial survey efforts and are held within a combined National Archives and Records Administration (NARA) catalog entry (NARA ID 139056920), Record Group 328. The images were scanned by the National Capital Park and Planning Commission, Federal Projects Planning Division (ca. 1926–ca. 1952). A multi-agency collaboration—including the Historic Preservation Office, OCTO, DHS, OP, DOEE, DOH, and FEMS—completed the georeferencing and mosaic development. As part of this effort, the team georeferenced and cataloged the full 1927 aerial photo series of the District, derived from more than 700 high‑resolution image scans. The resulting mosaic and index provide a historic baseline for land use, urban development, environmental analysis, and cultural resource research.
ACS 2012 Legal and Statistical Entities Web Map Service; January 1, 2012 vintage
Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows hours worked, and those unemployed and not in labor force. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of unemployed population within the civilian labor force. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left. Vintage: 2019-2023ACS Table(s): B23020, B23025Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the Survey Geography & ACS Technical Documentation News & Updates This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. 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. Click here to learn more about ACS data releases. Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate. The data for this geographic area cannot be displayed because the number of sample cases is too small.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Legal and Statistical Entities Web Map Service; January 1, 2000 vintage
uav imagery by North Metro Fire Rescue District, acquired 2026-08-20 to 2026-08-20, 3.1 cm per pixel, published on OpenAerialMap.
ACS 2023 Legal and Statistical Entities Web Map Service; January 1, 2023 vintage
Legal and Statistical Entities Web Map Service; January 1, 2020 vintage
Legal and Statistical Entities Web Map Service; January 1, 2010 vintage
Legal and Statistical Entities Web Map Service; January 1, 2020 vintage
Legal and Statistical Entities Web Map Service; January 1, 2010 vintage
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized
Census Current (2026) WMS Legal and Statistical Entities Web Map Service; January 1, 2026 vintage
Polygon features representing areal hydrography such as lakes and ponds in the United States, published by the US Census Bureau. Each feature is a labeled water body, with attributes including name, area of land and water, and geographic coordinates.
ACS 2016 Legal and Statistical Entities Web Map Service; January 1, 2016 vintage
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
ACS 2022 Legal and Statistical Entities Web Map Service; January 1, 2022 vintage
Date of Images: 10/9/2025 - 10/28/2025 Please Note: Images with majority cloud cover are not shown in the Map Viewer, but are available in the service. Summary: These Black Marble Day-Night Band (BRDF-Corrected) cloud masks were created by the NASA Black Marble Science team. This is the layer to displaying where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=961f83b5aef64f75bc4caa9c4610f4ed WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_FLOOD_AK/2510_BlackMarble_brdf_cloud/ImageServer/WMSServer
uav imagery by Ben Scholer, acquired 2025-05-31 to 2025-06-01, 4.6 cm per pixel, published on OpenAerialMap.
Census Current (2026) WMS Legal and Statistical Entities Web Map Service; January 1, 2026 vintage
Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left. Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the Survey Geography & ACS Technical Documentation News & Updates This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. 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. Click here to learn more about ACS data releases. Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate. The data for this geographic area cannot be displayed because the number of sample cases is too small.
Alaska Native Regional Corporations (ANRC), American Indian Reservations (AIR), Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2015 vintage; Generalized
Spatial Dataset used to display Census Block Groups for the year 2010 in Mecklenburg County, North Carolina. This dataset includes attributes such as census tracts and block groups.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.
ACS 2018 Legal and Statistical Entities Web Map Service; January 1, 2018 vintage
ACS 2025 Legal and Statistical Entities Web Map Service; January 1, 2025 vintage
Wake County Street Intersection Points, updated weekly.
County Subdivisions, Subbarrios, Consolidated Cities, Incorporated Places, and Census Designated Places; January 1, 2015 vintage; Generalized
Census 2010 Legal and Statistical Entities Web Map Service; January 1, 2010 vintage
This dataset provides information about the number of programs that have received Agency Review funding; how many of those programs had defined measurable outcome goals (DMOG) specified in the agencies funding request applications; and how many programs achieved their DMOG. The Agency Review process was developed to distribute human services funds to non-profit agencies. Agency Review funds come from the City of Tempe General Revenue Fund, Federal Community Development Block Grants, and water utility customer donations through Tempe’s Help to Others. This page provides data for the Human Services Grant performance measure. This dataset provides information about the number of programs that have received Agency Review funding; how many of those programs had defined measurable outcome goals (DMOG) specified in the agencies funding request applications; and how many programs achieved their DMOG. The performance measure dashboard is available at 3.10 Human Services Grants Data Dictionary Additional Information Source: e-CImpact Contact: Octavia Harris Contact E-Mail: Octavia_Harris@tempe.gov Data Source Type: Excel Preparation Method: Data downloaded from e-CImpact, then compiled in spreadsheet to establish yes/no fields for aggregate calculations by population served Publish Frequency: Annual Publish Method: Manual
The dataset contains locations and attributes of owner lines with dimensions. The tax information (attribution) comes from the Office of Tax and Revenue's Public Extract file. The creation of this layer is automated, occurs weekly, and uses the most currently available tax information. The date of the extract can be found in the EXTRACTDAT field in this layer.
Congressional Districts, Voting Districts, State Legislative Districts - Upper, State Legislative Districts - Lower; January 1, 2021 vintage; Generalized