United States of America GIS data

Point features labeling Metropolitan Statistical Areas in the United States, published by the US Census Bureau. Each feature represents one metropolitan statistical area, identified by its name in the BASENAME column, such as San Angelo, TX or Johnstown, PA.

Label points for metropolitan divisions, published by the US Census Bureau. Each feature represents one metropolitan division, identified by its name in the BASENAME field, such as Fort Lauderdale-Pompano Beach-Deerfield Beach, FL. Covers the United States.

Label points for combined statistical areas, published by the US Census Bureau. Each feature represents one combined statistical area, identified by its name in the BASENAME field, such as Youngstown-Warren-East Liverpool, OH-PA. Covers the United States.

Label points for New England city and town area divisions, published by the US Census Bureau. Each feature represents one division, identified by its name in the BASENAME field, such as Lowell-Billerica-Chelmsford, MA-NH. Covers New England in the United States.

Label points for combined New England city and town areas, published by the US Census Bureau. Each feature represents one combined area, identified by its name in the BASENAME field, such as Boston-Worcester-Manchester, MA-NH-CT-ME. Covers New England in the United States.

Urban cluster label points from the US Census Bureau, each representing a named urban cluster with its population and housing unit counts. The BASENAME field gives the cluster name, such as Lindsay, CA. Covers urban clusters in the United States.

Urbanized area label points from the US Census Bureau, each representing a named urbanized area with its population and housing unit counts. The BASENAME field gives the area name, such as Visalia, CA. Covers urbanized areas in the United States.

Census Regions (Labels) is a vector dataset published by the US Census Bureau. Each feature represents a labeled census region, identified by a BASENAME. The data covers the United States of America.

Census Divisions (Labels) is a vector dataset published by the US Census Bureau. Each feature represents a labeled census division, identified by a BASENAME. The data covers the United States of America.

Voting Districts (Labels) is a vector dataset published by the US Census Bureau. Each feature represents a labeled voting district, identified by a BASENAME. The data covers the United States of America.

2010 State Legislative Districts - Lower (Labels) is a vector dataset published by the US Census Bureau. Each feature represents a labeled lower state legislative district as of 2010, with attributes including district number, housing unit count, and population count. The data covers the United States of America.

2010 State Legislative Districts - Upper (Labels) is a vector dataset published by the US Census Bureau. Each feature represents a labeled upper state legislative district as of 2010, with attributes including district number, housing unit count, and population count. The data covers the United States of America.

Traffic Analysis Zones (Labels) is a vector dataset published by the US Census Bureau. Each feature represents a labeled traffic analysis zone, identified by a GEOID, BASENAME, and NAME. The data covers the United States of America.

Labels for counties in the United States, published by the US Census Bureau. Each feature represents a county and includes a base name such as Calhoun, Poquoson, or Pocahontas.

Labels for areal hydrography features in the United States, published by the US Census Bureau. Each feature represents an area of water and includes attributes such as name, coordinates, land area, and water area.

Labels for metropolitan statistical areas in the United States, published by the US Census Bureau. Each feature represents a metropolitan statistical area and includes a base name such as Bellingham, WA.

Labels for metropolitan divisions in the United States, published by the US Census Bureau. Each feature represents a metropolitan division and includes a base name such as Montgomery County-Bucks County-Chester County, PA.

Labels for combined statistical areas in the United States, published by the US Census Bureau. Each feature represents a combined statistical area, with the BASENAME column giving its name, such as Burlington-Fort Madison-Keokuk, IA-IL-MO.

Labels for micropolitan New England city and town areas, published by the US Census Bureau. Each feature represents a micropolitan area, with the BASENAME column giving its name, such as Athol, MA or Augusta, ME.

Labels for metropolitan New England city and town areas, published by the US Census Bureau. Each feature represents a metropolitan area, with the BASENAME column giving its name, such as Bangor, ME or Boston-Cambridge-Newton, MA-NH.

uav imagery by WebODM, acquired 2025-10-05 to 2025-10-05, 5.0 cm per pixel, published on OpenAerialMap.

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

Census Current (2020) Physical Features Web Map Service; January 1, 2020 vintage

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

This feature layer provides counts of forecasted chargers needed and current gap in charging for census tracts in Tempe. Each record represents a census tract in Tempe and includes details on forecasted charger count, existing charger, and gap in chargers at level 2 and DC Fast charging power levels. This modeled dataset was created by Catalyst Mobility (formerly Calstart). This information is a component of ongoing efforts for EV charging infrastructure development for Tempe. Data Scope  Charger forecasts are to 2050 for level 2 and DC fast charging power levels.   Data Use  Intended to be used in the Tempe EVCI Development Strategy Map.   Data Limitations  Charger count data is forecasted and may not reflect actual future conditions.   Data Dictionary   Additional Information Contact Name: Sustainability Division Contact Email: sustainability@tempe.gov Data Source:  Catalyst Mobility (formerly Calstart) Data Source Type:  GIS Feature Layer  Preparation Method:  See Task 2 of Electric Vehicle Charging Infrastructure (EVCI) Siting Plan Assessment Publish Method:  Manual Publish Frequency:  One-time static

uav imagery by Skytracking Drone NC, acquired 2025-04-20 to 2025-04-20, 5.0 cm per pixel, published on OpenAerialMap.

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

uav imagery by Philip Gandy, acquired 2025-04-21 to 2025-04-21, 5.5 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: 6/29/2026, 7/02/2026, 7/04/2026 Pre-Event: 6/19/2026 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 Color Infrared composite is created using the near-infrared, red, and green channels. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. 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. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. 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/portal/services/DISASTERS_202607_FIRE_CO/202614_sentinel2/ImageServer/WMSServer

uav imagery by Skytracking Drone NC, acquired 2025-04-26 to 2025-04-26, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Philip Gandy, acquired 2025-04-28 to 2025-04-28, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Philip Gandy, acquired 2025-05-01 to 2025-05-01, 3.0 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: August 26, 2026 Pre-Event: August 4, 2026 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. A Normalized Difference Water Index (NDWI) threshold of less than 0 from August 4, 2026 was used to mask out permanent water bodies. These layers are binary NDVI change polygons (threshold of areas where NDVI decreased by -0.25 or more) for August 26, 2026 as compared to August 4, 2026. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page

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 median earnings by occupational group broken down by sex. 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. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC).  This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. 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): B24022 (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.

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 youth (age 16-19) school enrollment and employment status. 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. Estimates here for 'disconnected youth' differ from estimates of 'idle youth' on Census Bureau's website because idle youth includes those unemployed (actively looking for work).  This layer is symbolized by the percentage of youth who were disconnected. 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): B14005Data 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.

This GIS data layer represents the geographic boundaries used by the City-County Inspections Department to organize and manage inspection services. This layer delineates distinct districts within the jurisdiction, ensuring efficient allocation of resources and streamlined communication between inspectors and other officials.  This data supports an essential service to residents and businesses across Durham, North Carolina.