Important Note: This item is in mature support as of December 2024. See blog for more information.This world basemap presents OpenStreetMap (OSM) data hosted by Esri. This version of the map is rendered using OSM cartography. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.

This is a group layer item for the most commonly requested live feed & hazard data layers by Urban Search & Rescue stakeholders. Created on 12/07/2023. See links below for more details and metadata. These are natural hazard data, some are static and some are live/dynamic. Whenever possible use incident specific forecast and impact models. For ease of use the layers are grouped as shown below.Current WeatherTropical CyclonesSevere Weather & TornadoesFloodingEarthquakesWildfireSee the links below for more detailed information and metadata.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.

​Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. 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 portrays 141 forest types across the contiguous United States and Alaska. This 250m raster was derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The purpose of this layer is to portray broad distribution patterns of forest cover in the United States and provide input to national scale modeling projects. Knowing where various forest types occur can be used to predict wildlife movements or design corridors, or to predict the effect of climate change on forest species.Dataset Summary The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Remote Sensing Applications Center. The source format is 250-meter raster. This layer covers the entire United States.The original forest type layer is available from the USDA portal.What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit, which represents an area of nearly 450 miles on a side.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

This image is used as a background for various items.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

National Wildlife RefugesThis U.S. Fish and Wildlife Service (FWS) feature layer depicts National Wildlife Refuges. These Refuges display external boundary of lands and waters administered by FWS. According to FWS, "Each unit of the Refuge System — whether it is a wildlife refuge, a marine national monument, a conservation area or a waterfowl production area — is established to serve a statutory purpose that targets the conservation of native species dependent on its lands and water. All activities on those acres are reviewed for compatibility with this statutory purpose."Patuxent Research RefugeData currency: current Federal service (National Wildlife Refuge System Boundaries)Data modification(s): NoneFor more information: National Wildlife Refuge SystemFor feedback please contact: ArcGIScomNationalMaps@esri.comFish and Wildlife Service (FWS)Per FWS, " The U.S. Fish and Wildlife Service is the premier government agency dedicated to the conservation, protection, and enhancement of fish, wildlife and plants, and their habitats. We are the only agency in the federal government whose primary responsibility is the conservation and management of these important natural resources for the American public."

This image is used as a background for various items.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This image is used as a background for various items.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This image is used as a background for various items.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This image is an alternate Linn County, Iowa logo in white.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This image is the primary Linn County, Iowa logo in color.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This image is the primary Linn County, Iowa logo in white.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This image is an alternate Linn County, Iowa logo in white.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

Purpose: This Logo was initially published to support the Waikato Regional Hazards Portal.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Critical Habitat for Threatened and Endangered SpeciesThis National Geospatial Data Asset (NGDA) dataset, shared as a U.S. Fish and Wildlife Service (FWS) feature layer, displays proposed and designated critical habitat under the U.S. Endangered Species Act. According to the FWS, "When the Fish and Wildlife Service proposes a species for listing under the Endangered Species Act, we are required to consider whether there are geographic areas that contain essential features on areas that are essential to conserve the species. If so, we may propose designating these areas as critical habitat."Canada Lynx and Atlantic SalmonData currency: current federal service (USFWS Critical Habitat)NGDAID: 3 (FWS Critical Habitat for Threatened and Endangered Species Dataset)OGC API Features Link: Not AvailableFor more information:Critical HabitatUSFWS Threatened & Endangered Species Active Critical Habitat ReportFor feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Biodiversity and Ecosystems Theme Community. Per the Federal Geospatial Data Committee (FGDC), Biodiversity and Ecosystems is defined as pertaining to, or describing, "the dynamic processes, interactions, distributions, and relationships between and among organisms and their environments".For other NGDA Content: Esri Federal Datasets

Tornado TracksThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays tornadoes in the United States, Puerto Rico and U.S. Virgin Islands between 1950 and 2024. A tornado track shows the route of a tornado. Per NOAA, "A tornado is a narrow, violently rotating column of air that extends from a thunderstorm to the ground. Because wind is invisible, it is hard to see a tornado unless it forms a condensation funnel made up of water droplets, dust and debris. Tornadoes can be among the most violent phenomena of all atmospheric storms we experience. The most destructive tornadoes occur from supercells, which are rotating thunderstorms with a well-defined radar circulation called a mesocyclone. (Supercells can also produce damaging hail, severe non-tornadic winds, frequent lightning, and flash floods.)"EF-5 Tornado Track (May 3, 1999) near Oklahoma City, OklahomaData currency: December 30, 2024Data source: Storm Prediction CenterData modifications: Added field "Date_Calc"For more information: Severe Weather 101 - Tornadoes; NSSL Research: TornadoesSupport documentation: SPC Tornado, Hail, and Wind Database Format SpecificationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

This layer contains block group level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.* *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the block group itself.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

The agricultural land base for the Greater Golden Horseshoe is comprised of prime agricultural areas, including specialty crop areas, and rural lands that together create a continuous, productive land base for agriculture. The Province has issued the agricultural land base map as enabled by the Greenbelt Plan, 2017; the Growth Plan, 2017; the Oak Ridges Moraine Conservation Plan, 2017; and the Niagara Escarpment Plan, 2017. The agricultural land base mapping is to be used in conjunction with the Agricultural System policies in provincial plans, Implementation Procedures for the Agricultural System in the Greater Golden Horseshoe, and the Agricultural System Portal as a framework to protect farmland, while supporting the viability of the agri-food sector. Legislated or Legal Authority for Collection:

Places to Grow Act Greenbelt Act Oak Ridges Moraine Conservation Act Niagara Escarpment Planning and Development Act.

Status

Completed: Production of the data has been completed

Maintenance and Update Frequency Not stated

Contact

Arthur Churchyard, Food Safety and Environmental Policy Branch, Arthur.Churchyard@ontario.ca

This layer contains block level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for the state of Colorado. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block level, since this data has been protected using differential privacy.**To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual blocks will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the block itself.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program