United States of America GIS data

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2023 vintage; Generalized

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

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

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

Date of Images: September 19, 2018; August 9, 2018 Summary: Scientists at NASA's Marshall Space Flight Center created these water extents using Landsat imagery. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using the ESA Worldview. It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets. Suggested Use: This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from CDL that appear to have water and are potentially flooded. Blue (1): Known Water Red (2): Flooded Developed Green (3): Flooded Vegetation Orange (4): Flooded Cropland/Grassland Gray (5): Clouds/Cloud Shadow (0): No Data Satellite/Sensor: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on the right side of page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2601_201809_HURRICANE_FLORENCE/ex2601_landsat_WaterExtent/MapServer/WMSServer

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

Below are the 8 definition queries in order to filter out the geographies in the tree canopy data. Geography = 'Block Group' Geography = 'City of Charlotte' Geography = 'Council Districts' Geography = 'County' Geography = 'Jurisdiction' Geography = 'Land Ownership' Geography = 'NPA' Geography = 'Watershed'

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

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

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.

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.

This dataset contains the Commonwealth of Kentucky Boundary. The State boundary along Ohio River has been updated to reflect the Supreme Court Case regarding a boundary dispute between Kentucky, Indiana and Ohio in 1980 (Ohio v. Kentucky, 444 U.S. 335 (1980).  Online Linkage: https://ky.box.com/v/kymartian-ky-state-polygon

Descriptions of violations recorded during inspections by Louisville Metro Department of Public Health and Wellness. The Inspection_id column corresponds to the InspectionID column in the Inspections table. There are 0 to many inspection violations associated with each inspection in the Inspections table. Data Dictionary: inspection_id-system id of inspection InspTypeSpecificViolID-violatio number for type of inspection ViolationDesc-description of the violation weight-points taken off for violation critical_yn-is this a critical violation? If so means inspection failure comments rpt_area_id-facility type code delete_mark_id-system indicator id-violation system id Contact: Gerald Kaforski gerald.kaforski@louisvilleky.gov

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

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

Section 305(b) of the Clean Water Act requires states to assess the condition of the Waters of the US. The Kentucky Division of Water, in cooperation with other agencies, collects biological and water quality data to assess the condition of the resource and reports this to Congress every two years in the Integrated Report to Congress. (https://www.water.ky.gov/sw/swmonitor/305b/) These data represent the assessment results for surface waters of the Commonwealth of Kentucky. Box Download: https://ky.box.com/v/energy-environment/file/850630980093

Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web service depict ecosystem services at the census block group level for all EnviroAtlas communities. These layers illustrate the ecosystems and natural resources that are associated with clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, and aesthetics, and biodiversity conservation, and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

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

This Road data is not needed and recommend using the Street Centerlines which has all the Community, Postal, Postal Community, Incorporated, and Neighborhood fields populated. Add Data and add the service "STREET CENTERLINES" Key words are standardized for ArcGIS Pro users to be able to search through the County's Geo Portal web services without being logged in. A complex label PYTHON expression was created based on FULLST_NAME field using logic.

This dataset includes all block faces with parking in Washington, DC according to their digital curb identification number. Attributes include block address with side of street, resident permit parking zone, payment zones, rush hour restrictions, some revenue and generalized curbside policy.

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

This dataset depicts the Utah State Boundary as digitized from USGS 7.5 minute quad map series. The boundary should be coincident with the outer boundary of the SGID024.CountyBoundaries dataset. Minor adjustments were made to align with the Newest PLSS-GCDB layers from BLM (CadNSDIv2), biggest move was along the Wyoming and Summit County lines, 47 feet all in non-populated areas (7/18/2014).

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.

Vector label features for incorporated places and minor civil divisions, published by the US Census Bureau. Each feature carries a geographic identifier and a place name.

Polygon features representing tribal statistical areas, published by the US Census Bureau. Each feature includes a name, land and water area, and population and housing counts, with examples such as New Stuyahok, Portage Creek, and Togiak in Alaska.

Polygon features representing American Indian reservations and/or off-reservation trust lands, published by the US Census Bureau. Each feature includes a name, land and water area, and population and housing counts.

Vector label features for planning regions, published by the US Census Bureau. Each feature has a geographic identifier, a region code, and a name such as Aguadilla, Mayagüez, or Arecibo in Puerto Rico.

Polygon boundaries of Census Designated Places, published by the US Census Bureau. Each feature represents one designated place and includes its name, code, land and water area, and geographic center coordinates. Coverage is the United States.

Polygon boundaries of incorporated places and minor civil divisions, published by the US Census Bureau. Each feature represents one place or division and includes its name, code, land and water area, and geographic center coordinates. Coverage is the United States.

Polygon boundaries of consolidated cities, published by the US Census Bureau. Each feature represents one consolidated city and includes its name, code, land and water area, and geographic center coordinates. Coverage is the United States.

Point locations of principal cities within Core Based Statistical Areas, published by the US Census Bureau. Each point represents one principal city and includes its name and state code. Coverage is the United States.

Polygon boundaries of Tribal Statistical Areas, published by the US Census Bureau. Each feature represents one tribal statistical area and includes its name, code, land and water area, and population and housing unit counts. Coverage is the United States.

Polygon boundaries of American Indian reservations and off-reservation trust lands, published by the US Census Bureau. Each feature represents one reservation or trust land and includes its name, code, land and water area, and population and housing unit counts. Coverage is the United States.

Polygon boundaries of Primary Metropolitan Statistical Areas (PMSAs), published by the US Census Bureau. Each feature represents one PMSA and includes its name, code, land and water area, and population and housing unit counts. Coverage is the United States.

Boundary features labeling 108th Congressional Districts in the United States, published by the US Census Bureau. Each feature represents one congressional district, with columns for district number, housing unit count, and population count.