United States of America GIS data

ACS 2025 Legal and Statistical Entities Web Map Service; January 1, 2025 vintage

ACS 2025 Legal and Statistical Entities Web Map Service; January 1, 2025 vintage

ACS 2025 Legal and Statistical Entities Web Map Service; January 1, 2025 vintage

uav imagery by Skytracking Drone NC, acquired 2025-11-22 to 2025-11-22, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-12-20 to 2025-12-20, 5.0 cm per pixel, published on OpenAerialMap.

ACS 2018 Legal and Statistical Entities Web Map Service; January 1, 2018 vintage

The visible-wavelength true color RGB digital camera images were taken by astronauts aboard the International Space Station showing areas affected by the flooding in Hawaii March 2026. Date of Images: April 4, 2026 Suggested Use: This image provides regional context, and may be useful for visualization of the ongoing event. Higher spatial resolution images may be suitable for spatial analysis to support decision making or research applications. Sensor: Camera: Nikon Z9 Focal Length: 200mm Additional Information about the astronaut photography from the ISS. Credits: International Space Station Program, NASA Johnson Space Center Earth Science and Remote Sensing Unit, ARES Division, Exploration Architecture, Integration & Science Directorate Esri REST Endpoint: See URL to the right WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202603_FLOOD_HI/2603_iss/ImageServer/WMSServer Data Download: Full datasets are available for download at the Gateway to Astronaut Photography of Earth. Download for this specific data can be found at: https://eol.jsc.nasa.gov/WebShare/WebShare.pl?folder=NASADisastersProgram

Legal and Statistical Entities Web Map Service; January 1, 2000 vintage

Census 2020 Legal and Statistical Entities Web Map Service; January 1, 2020 vintage

ACS 2018 Legal and Statistical Entities Web Map Service; January 1, 2018 vintage

Legal and Statistical Entities Web Map Service; January 1, 2010 vintage

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes. The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

Safe, secure, and abundant bicycle parking is necessary to support the District’s growing number of people using bikes for transportation, commuting, and recreation. Providing sufficient bicycle parking is part of DDOT's strategy to promote bicycling in the District of Columbia and reduces the number of bikes locked to trees, benches, and railings.

Point features representing business licenses, with each record giving the business name, address, city and state, zip, phone, license activity, and the date filed and expiration date. Published by SCGISAdmin.

Last Update: 02/2021 This datasets was was downloaded from the 2020 Census Redistricting Data (P.L. 94-171) page. All 2020 census boundaries are current to January 1, 2020. The Census Bureau will release the first set of corresponding demographic data in September 2021 (the 2020 Census Redistricting P.L. 94-171 Summary Files). Following that release, AGRC will append the demographic data to the existing 2020 geographies served on this page. Visit the SGID 2020 Census data page for more information.

This map layer will depict the groomed cross country ski trails available at commercial and public venues in Utah. The map layer is currently incomplete and includes trails from Mountain Dell golf course and Soldier Hollow, digitized from aerial photography in 2007. Trail features carry the trail name, the XC ski area they are within, and the length in meters. Trails are missing from: Brian Head, Eden Valley North Fork, Jeremy Ranch Park City Basin Rec/Round Valley, Park City White Pine, Snowbasin, and Sundance. Trails will be added by AGRC as received, or as staff time allows.

Data provided here are from the DC's 311 service request center. They represent all service requests such as abandoned automobiles, parking meter repair and bulk trash pickup. Requests are received by the Office of Unified Communications (OUC) through the Mayor's Call Center (311), citizens web intake at 311.dc.gov, electronic and US mail service or via other methods of communication. The Office of Unified Communications (OUC) oversees the designated call center for all 311 calls and for all District 911 calls. Please also visit the DC 311 Service Request Map which allows the public to see service requests in the last 30 days. Users can view requests by Ward within charts. Just set the area filter to select service requests. Click on a service request to view details.

The "Utah Small Health Statistics Areas" feature layer was developed by the Office of Public Health Assessment, Utah Department of Health using the Utah Local Health District and Small Area Designation Algorithm (see https://ibis.health.utah.gov/pdf/resource/Algorithm.pdf). Small Areas are defined based on ZIP Codes, population size, local health district and county boundaries, similarity of ZIP Code area income levels, community political boundaries, and input from local community representatives.

Legal and Statistical Entities Web Map Service; January 1, 2000 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.

Boundaries of Kentucky Department of Fish and Wildlife Resources Public Hunting Areas. These are both Wildlife Management Areas (WMA), Hunting Access Areas, Outdoor Recreation Area, and areas for which there is some kind of public hunting allowed (liek state forests and state parks). The current hunting and trapping guide should be referred to for specific regulations. Tracts of different ownership are merged into a single polygon for each of the public hunting areas. Not all WMAs are in public ownership. Some are privately owned and have short term agreements to allow public hunting access. There are two other feature classes, " Wildlife Management Areas" and "Non_WMA_Hunting_Area_tracts" that are broken down by tract. That Wildlife Management Area feature class includes the ownership for each tract of the WMA.

ACS 2026 Legal and Statistical Entities Web Map Service; January 1, 2026 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 2023 Legal and Statistical Entities Web Map Service; January 1, 2023 vintage

OverviewThis image service provides 2023 orthophotography from the Orthoimagery Aerials2023 Eagle View mosaic dataset. It delivers RGB imagery as a three band, 8 bit raster service with 0.25 meter pixel size and bilinear resampling. The service covers a local area in spatial reference 2881 and is delivered as a managed image collection with catalog access. Raster PropertiesData type: RGB imagery Pixel type: U8 Band count: 3 Band values: Band 1, Band 2, Band 3 Pixel size: 0.25 x 0.25 Dataset format: AMD Compression: None Resampling method: Bilinear Value range: 0 to 255 for each band Mean values: 110.08, 109.55, 82.63 Standard deviations: 31.21, 26.24, 25.53 Histogram range: -0.5 to 255.5 for each band Raster functions: None and Eagle View Stretch Mosaic PropertiesThe service is built from a mosaic dataset and combines multiple raster items on the fly. It supports Catalog, Download, Image, and Metadata capabilities. Users can browse individual catalog items, review metadata, and access imagery for display and download. Supported mosaic methods include NorthWest, Center, LockRaster, ByAttribute, Nadir, Viewpoint, Seamline, and None. The service can composite up to 42 images at once. Catalog fields describe each raster item with metadata such as name, pixel size range, category, tag, group name, product name, center location, and z order. The category domain includes values such as Primary, Overview, Uploaded, and Custom. Geographic CoverageThe imagery covers a local area of approximately 0.43 degrees in latitude and longitude extent, centered in northeastern Florida. Extent:xmin: -81.52329610896626 ymin: 29.256706249842132 xmax: -81.09774534379683 ymax: 29.683933726633093 Spatial reference: 2881 Use CasesThis service supports imagery viewing, catalog browsing, metadata review, and image download workflows. It allows users to copy data and run analysis, and it supports standardized catalog queries with statistics, SQL expressions, ordering, pagination, and distinct queries. Downloads are limited to 20 images per request with a 2048 MB size limit. The service does not provide mensuration capabilities.

Census Current (2026) WMS Legal and Statistical Entities Web Map Service; January 1, 2026 vintage

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Dates of Images: Post-Event: 12/26/2026, 12/27/2026, 12/28/2026, 12/29/2026 Pre-Event: 12/30/2025, 01/01/2026, 01/18/2026, 01/19/2026 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The Short Wave Infrared (SWIR) RGB is a product that is created using the SWIR, NIR, and Red channels of the respective instrument. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. The Short Wave Infrared (SWIR) RGB is a product that can provides value in flood detection. Areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta, snow will appear as a bright blue/cyan, and bare soils being multicolor dependent on their makeup. Compare pre-event imagery to post-event imagery to identify potential flooding. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: True Color: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/gis05/services/DISASTERS_202601_WINTERWX_US/202601_sentinel2/ImageServer/WMSServer

ACS 2024 Legal and Statistical Entities Web Map Service; January 1, 2024 vintage

Census Current (2026) WMS Legal and Statistical Entities Web Map Service; January 1, 2026 vintage

Legal and Statistical Entities Web Map Service; January 1, 2020 vintage

ACS 2021 Legal and Statistical Entities Web Map Service; January 1, 2021 vintage

Legal and Statistical Entities Web Map Service; January 1, 2000 vintage

ACS 2015 Legal and Statistical Entities Web Map Service; January 1, 2015 vintage

ACS 2017 Legal and Statistical Entities Web Map Service; January 1, 2017 vintage

Legal and Statistical Entities Web Map Service; January 1, 2010 vintage