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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 demographic context for emergency response efforts. This is shown by tract, county, and state centroids. 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 households without access to internet. The size of the symbol represents the count of households without internet access. 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): B01001, B08201, B09021, B16003, B16004, B17020, B18101, B25040, B25117, B27010, B28001, B28002Data 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 2022 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 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.
The goal of the updated map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. An updated timestamp (2022 vs. 2016) and finer grained resolution (10m vs. 30m) improves the utility of the data for planning purposes. The LST dataset from 2016 can be accessed via the Minnesota Geospatial Commons. High resolution (10 meter) land surface temperature (LST) from September 1, 2022 is mapped for the seven-county metropolitan region of the Twin Cities. The goal of the map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. The raster dataset was computed at 30 meters using satellite imagery from Landsat 9 and downscaled to 10 meters using Copernicus Sentinel-2. These datasets were integrated using techniques modified from Ermida et al. 2020 and Onačillová et al. 2022). Open water was removed using ancillary data from OpenStreetMap and 2020 Generalized Land Use for the Twin Cities (Metropolitan Council). First, Landsat 9 imagery taken at 11:59 am CDT on September 01, 2022 was processed into 30-meter resolution LST (based on Ermida et al. 2020). At this time, the air temperature was 88° F at the Minneapolis-St. Paul International Airport (NOAA). A model predicting LST based on spectral indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) was created and applied to 10-meter Sentenel-2 imagery. Sentinel-2 imagery was also taken on September 1, 2022, and this resulted in a 10-meter downscaled LST image (based on Onačillová et al. 2022). To account for anomalies in NDVI on the primary image date of September 1 (e.g., recently harvested agricultural fields), maximum NDVI occurring between July 1, 2022 and September 1, 2022 was used for both Landsat and Sentinel image processing. Water bodies were removed for all processing steps (OpenStreetMap 2023, Metropolitan Council 2021). This dataset is an update to the 2016 LST data for the Twin Cities Region (Metropolitan Council). The code to create and processes this dataset is available at: https://github.com/Metropolitan-Council/extreme.heat Sources: Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471. Metropolitan Council. 2021. Generalized Land Use 2020. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-generl-lnduse2020 Metropolitan Council. 2017. Land Surface Temperature for Climate Vulnerability Analysis. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-env-cva-lst2016 NOAA, National Oceanic and Atmospheric Administration, National Centers for Environmental Information, station USW00014922. September 1, 2022. Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022. Combining Landsat 8 and Sentinel 2 data in Google Earth Engine to derive higher resolution land surface temperature maps in urban environment. Remote Sensing, 14 (16), 4076. https://doi.org/10.3390/rs14164076. OpenStreetMap contributors. 2023. Retrieved from https://planet.openstreetmap.org on April 12, 2023.
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 six different types of disability. This is shown by tract, county, and state centroids. 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 count and percent of population with a disability. 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): B18101, B18102, B18103, B18104, B18105, B18106, B18107, C18108 (Not all lines of these ACS tables 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 tenure (owner or renter) by type of heating fuel. This is shown by tract, county, and state centroids. 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 predominant type of heating fuel mostly used in occupied housing units. Only predominant values are shown in the legend, but the count of all housing units is represented in the popup. 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): B25040, B25117 (Not all lines of ACS table B25117 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.
satellite imagery by -, acquired 2026-02-23 to 2026-02-23, 91.0 cm per pixel, published on OpenAerialMap.
uav imagery by Hawkinson Media, LLC, acquired 2026-07-07 to 2026-07-07, 6.6 cm per pixel, published on OpenAerialMap.
uav imagery by nyenvironcom , acquired 2026-07-08 to 2026-07-08, 4.3 cm per pixel, published on OpenAerialMap.
uav imagery by NYenvironcom , acquired 2026-07-08 to 2026-07-08, 3.8 cm per pixel, published on OpenAerialMap.
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 language group of language spoken at home by age. 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 the population age 5+ who speak Spanish at home. 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): B16007Data 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.
The review of these roads was conducted by the Mobility Safety team as part of the Safer Mobility Plan. This dataset is used to track locations that have been reviewed and generate work plans for reducing road speed limits as well as communicating reviewed roads to the general public. This dataset is a public view layer created from CALGIS_MOBILITY_SAFETY_COLLECTOR_SPEED_LIMIT_REVIEWED_ROADS and is updated as needed via HYDRA. Data Steward: Mobility - Safety Email: MobilitySafety@calgary.ca
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 centroids. 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.
Brampton Plan, Schedule “11” Precinct Plan Areas identify specific locations within Brampton that require a more detailed level of planning to guide future development, redevelopment, or intensification. Precinct Plans establish area-specific policies and frameworks for land use, urban design, transportation, infrastructure, public spaces, environmental features, and community services to ensure growth occurs in a coordinated and comprehensive manner. These plans implement the broader objectives of Brampton Plan while responding to the unique opportunities and constraints of each area. This data is intended to be used in conjuction with other related mapping layers for this schedule, and may be impacted by other schedules, with consideration to the text of Brampton Plan.
This item is deprecated. Please refer to https://hub.arcgis.com/maps/NBDNR::nbhn-named-features-nbhn-0000-nf-0-nbhn-0000-nf-2-and-nbhn-0000-names/about?locale=en for updated data Cet article est obsolète. Veuillez consulter https://hub.arcgis.com/maps/NBDNR::nbhn-named-features-nbhn-0000-nf-0-nbhn-0000-nf-2-and-nbhn-0000-names/about?locale=fr pour des données à jour Layers and table represent features with a known name identified as part of the creation of the NBHN. Point Layer - NBHN_0000_NF_0Polygon Layer - NBHN_0000_NF_2Typonomic Names - NBHN_0000_Names The metadata document can be downloaded from https://nbdnr.maps.arcgis.com/sharing/rest/content/items/d5f765b86027426c90c97c820c1a3309/data Ces couches et le tableau représentent les entités possédant un nom connu, identifiés dans le cadre de la création du RHNB. Point Layer - NBHN_0000_NF_0Polygon Layer - NBHN_0000_NF_2Typonomic Names - NBHN_0000_Names Le document de métadonnées peut être téléchargé à partir de https://nbdnr.maps.arcgis.com/sharing/rest/content/items/d5f765b86027426c90c97c820c1a3309/data
uav imagery by WebODM, acquired 2025-06-05 to 2025-06-05, 2.0 cm per pixel, published on OpenAerialMap.
uav imagery by WebODM, acquired 2025-05-19 to 2025-05-19, 2.0 cm per pixel, published on OpenAerialMap.
uav imagery by Gary Stebbins, acquired 2025-10-05 to 2025-10-05, 1.1 cm per pixel, published on OpenAerialMap.
Mapping depicts Brampton Plan Schedule 3B Support Corridors for potential rapid transit. Corridors are strategic linear growth areas located along major transportation routes where mixed-use development, intensification, employment, and enhanced mobility are encouraged. This data is intended to be used in conjuction with other related mapping layers for this schedule, and may be impacted by other schedules, with consideration to the text of Brampton Plan.
uav imagery by Bard College, acquired 2025-12-08 to 2025-12-08, 2.5 cm per pixel, published on OpenAerialMap.
OverviewThis feature service provides land use information for Westbank First Nation and supports planning and land management activities within the community. Feature LayerWFN Land Use tracks land use designations and associated planning categories across the service area. The layer includes a land use label and an official community plan code, making it useful for viewing, interpreting, and managing future land use patterns. Use CasesCommunity and land use planning Future development review Map display and reference for land use designations Supporting planning and policy workflows Geographic CoverageThe service covers Westbank First Nation lands in the Central Okanagan region of British Columbia. Access and LicensingThe service is hosted by the GIS Section of the Regional District of Central Okanagan and maintained by Westbank First Nations. It is shared under the Regional District of Central Okanagan Open Data License and related terms of use.
uav imagery by Hawkinson Media, LLC, acquired 2026-07-09 to 2026-07-10, 4.7 cm per pixel, published on OpenAerialMap.
Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents the month of January within a dataset of 12 raster files (one for each month) representing total precipitation values across North America for the 1981–2010 climatological reference period, in millimeters. The dataset is derived from a kilometer-scale climate product generated using the CHELSA downscaling model and represents long-term climatological means. The North American monthly precipitation layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric precipitation fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly precipitation across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download
Overview This dataset shows North American land cover for 2010 at a spatial resolution of 250 meters, based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. It provides a harmonized view of the physical cover of Earth's surface across the continent, classified into nineteen Level II land cover classes defined using the Land Cover Classification System (LCCS) standard developed by the Food and Agriculture Organization (FAO) of the United Nations. The dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations: the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), the National Commission for the Knowledge and Use of Biodiversity (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation (CEC), an international organization created by the governments of Canada, Mexico, and the United States under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. Land Cover Classes The raster attribute table provides 19 thematic land cover classes: 1: Temperate or sub-polar needleleaf forest 2: Sub-polar taiga needleleaf forest 3: Tropical or sub-tropical broadleaf evergreen forest 4: Tropical or sub-tropical broadleaf deciduous forest 5: Temperate or sub-polar broadleaf deciduous forest 6: Mixed forest 7: Tropical or sub-tropical shrubland 8: Temperate or sub-polar shrubland 9: Tropical or sub-tropical grassland 10: Temperate or sub-polar grassland 11: Sub-polar or polar shrubland-lichen-moss 12: Sub-polar or polar grassland-lichen-moss 13: Sub-polar or polar barren-lichen-moss 14: Wetland 15: Cropland 16: Barren land 17: Urban and built-up 18: Water 19: Snow and ice Geographic Coverage This dataset covers Canada, Alaska, the conterminous United States, and Mexico. Data Sources and Licensing This dataset builds on the initial 2005 North American Land Cover product, updated to reflect land cover changes detected through 2010. No changes were mapped in Hawaii due to a lack of newer data. This version (2.0) supersedes Edition 1.0, correcting labeling and classification errors identified after the initial release, and removes the water buffer along the coastline of North America to ensure consistent statistics for the water class. The entire dataset is produced by the Commission for Environmental Cooperation (CEC), derived from national land cover maps provided by Natural Resources Canada, the National Commission for the Knowledge and Use of Biodiversity, the National Institute of Statistics and Geography, the National Forestry Commission; and the United States Geological Survey. The compiled dataset is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: As needed Limitations of use: Intended for continental-scale reference. Vintage: 2010 Applications (Intended Use) Land cover review and continental- to national-scale mapping. Environmental monitoring and change detection over time. Spatial analysis and integration with other thematic layers in the North American Environmental Atlas. Supporting cross-border environmental cooperation and policy-making among North American nations. Files Download
The dataset was created to show individual 311 incoming calls and the general reason for the call as identified by the municipal department or program the call was directed to. Metadata Note: There is currently an issue with the timestamp for this datasets where the time is offset by 3-4 hours (depending on daylight savings). The issue has been logged with ESRI and we hope it will be resolved as soon as possible.
uav imagery by WebODM-LowestPotential, acquired 2025-04-12 to 2025-04-12, 5.0 cm per pixel, published on OpenAerialMap.
uav imagery by WebODM, acquired 2025-06-14 to 2025-06-14, 2.0 cm per pixel, published on OpenAerialMap.
____________________________________________________________ NOTE - although it's recommended to use the feature layer web service, the file geodatabase is available for download. Download FGDB ____________________________________________________________ More Details The MUSA Composite dataset depicts current and future boundaries of the Urban Service Areas (sewer service areas) based on communities' comprehensive plans for the seven-county metropolitan area of the Twin Cities of Minneapolis and Saint Paul. The dataset does not depict the precise location of current urban services (Sewer service). In other words, the Urban Service Areas designate areas that might be serviced, it does not represent the urban areas that are serviced. The depiction of Urban Service Areas provides a framework in which known urban development and anticipated future development can exist with adequate sewer capacity at efficient service levels. Seven other datasets are derived from these data: MUSA_2020, MUSA_2020_Outline, MUSA_2030, MUSA_2030_Outline, MUSA_2040, MUSA_2040_Outline, MUSA_2050, MUSA_2050_Outline, MUSA Undesignated_Reserve (see below). NOTE: As of August 2020, the derived dataset MUSA_Undesignated is no longer being used as a desingation but remain in the Attributes for an unknown period of time for historical comparison purposes but there will no longer be a designated layer as part of any download. Each community (city or township) in the seven-county Twin Cities metropolitan area is required to complete a comprehensive plan for approval by the Metropolitan Council per the Metropolitan Land Planning Act of 1995 (Minn. Stat 473.864, Subd 2 and 473.175, Subd 1). The comprehensive plan must include a depiction of current and future boundaries of the Urban Service Areas (i.e., 2040, 2050, Post-2050). As of March 2011, a new designation area was created specifically to address the agreed upon development plan for southeastern Scott County between the county, the city of Elko New Market and the town of New Market : 'Undesignated MUSA Reserve.' This is an area where an agreed upon acreage of urban sewer service can be added to the current MUSA by 2040 through their 2040 Comprehensive Plan. The distinction between 'Undesignated MUSA' and 'Undesignated MUSA Reserve' is that 'Undesignated MUSA' is an area within a municipal boundary defined by that municipality's comprehensive plan, whereas, 'Undesignated MUSA Reserve' is an area beyond the current municipal boundary (i.e., surrounding township) that is designated in a joint agreement by the city, town, and county to accommodate future municipal growth. Urban sewer services will not be provided to these areas until they have been annexed by the municipality (i.e., Elko New Market). Although the Metropolitan Council provides the majority of the urban sewer service in the seven-county metropolitan area, several smaller, free-standing rural communities in the region (primarily in Carver and Scott Counties), private systems, and independent nations (Shakopee Mdewakanton Sioux Community) provide their own urban services. Where information on their urban service areas are available, it is included in this dataset. Although the information included in this dataset is derived from the communities' 2008 and 2018 comprehensive plans, subsequent plan amendments are intended to be current within 4 months, for exact MUSA information, please contact the community. When referring to CURRENT MUSA extent, this includes areas up to 2025 MUSA. For communities who have had their 2040 Comprehensive Plan adopted (Benchmark 2040), this includes areas up to 2030 MUSA. These data were previously incorporated in the Comprehensive Plan Composite dataset.
Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), shows the percentage of the total land area that was equipped for irrigation, around the year 2000. Irrigation is the practice of applying water to land to promote the growth of vegetation such as crops and pastures. The land area actually irrigated is unknown but was smaller than the amount equipped for irrigation. The data are derived from the Global Map of Irrigation Areas, version 4.0.1, produced by the Johann Wolfgang Goethe University, Frankfurt am Main, Germany, and the Food and Agriculture Organization of the United Nations (FAO), Rome, Italy. Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Siebert, S., P. Döll, S. Feick, J. Hoogeveen and K. Frenken. 2007. Global map of irrigation areas, version 4.0.1. Johann Wolfgang Goethe University, Frankfurt am Main, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy. The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Not Planned Limitations of use: None. Acknowledgement of the Commission for Environmental Cooperation and (or) the Food and Agriculture Organization of the United Nations (FAO) – Global Information System on Water and Agriculture would be appreciated in products derived from these data. Vintage: 2000 Applications (Intended Use) Assessing the extent and distribution of irrigated agriculture across North America. Water resource and agricultural planning. Supporting research and education on land and water use for agriculture. Cross-border environmental cooperation and reporting among North American nations. Files Download
Overview This feature service provides building footprint information for Westbank First Nation, supporting a current view of built structures within the community. Feature Layers WFN Buildings — Polygon features representing individual building footprints. The layer includes building classification and detail attributes, along with basic measurements such as height, area, and perimeter-related information, which support planning, asset inventory, and spatial analysis. Geographic Coverage The service covers the Westbank First Nation area in British Columbia, Canada. Use Cases Community and land-use planning Building inventory and asset management Map display and reference for local infrastructure Spatial analysis of building distribution and development License and Attribution This item is owned and maintained by Westbank First Nation (WFN) and made available under the Regional District of Central Okanagan Open Data License and related terms of use and disclaimer.
A research point is a feature representing single coordinate or point based locations, where some form of research observation, test, trial, measure, or monitoring activity has, or will take place. The mapped features are generally small in extent (for example, soil water table monitor, tree cavity nest), and can be used to represent the locations of sampling techniques (for example, basal area prism sweep). This data class is one of three primitive data classes: Research Point (RESPOINT) Research Line (RESLINE) Research Polygon (RESPOLY) Status: On going: data is continually being updated Maintenance and Update Frequency: As required Contact Calvin Julie, Natural Resources Information Unitcalvin.julie@ontario.ca Note Additional features licensed under the Ontario Geospatial Data Exchange (OGDE) Agreement are available to members of the OGDE for professional, non-commercial use only. To find out more about the OGDE visit Geospatial Ontario on Ontario.ca.
This layer is a high-resolution tree canopy change-detection layer for Cambridge, MA. It contains three tree-canopy classes for the period 2018-2024: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2018 and 2024 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2018 and 2024 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction at a scale of 1:2500. Explore all our data on the Cambridge GIS Data Dictionary. Attributes NameType DetailsDescription Class_name type: String width: 255 precision: 0 Classification name. Contains three tree-canopy classes for the period 2009-2014: (1) No Change; (2) Gain; and (3) Loss
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 housing costs as a percentage of household income. 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. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). 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): B25070, B25091Data 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 place of birth by citizenship 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. This layer is symbolized to show the predominant area of birth among those who are foreign born. 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): B05002 (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.
uav imagery by WebODM, acquired 2025-06-01 to 2025-06-01, 2.0 cm per pixel, published on OpenAerialMap.
Table of repealed legislation documents and links to the pdf documents.The data was compiled by the office of the Municipal Clerk to provide access to repealed legislation documents.Metadata
uav imagery by WebODM, acquired 2025-08-10 to 2025-08-10, 5.0 cm per pixel, published on OpenAerialMap.
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 Hispanic or Latino origin by specific origin. 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 the population with Hispanic or Latino origins. 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): B03001Data 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.