87 datasets found
  1. M

    DNR QuickLayers for ArcGIS Pro 3

    • gisdata.mn.gov
    esri_addin
    Updated Oct 3, 2025
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    Natural Resources Department (2025). DNR QuickLayers for ArcGIS Pro 3 [Dataset]. https://gisdata.mn.gov/dataset/quick-layers-pro3
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    esri_addinAvailable download formats
    Dataset updated
    Oct 3, 2025
    Dataset provided by
    Natural Resources Department
    Description

    The way to access Layers Quickly.

    Quick Layers is an Add-In for ArcGIS Pro 3 that allows rapid access to the DNR's Geospatial Data Resource Site (GDRS). The GDRS is a data structure that serves core geospatial dataset and applications for not only DNR, but many state agencies, and supports the Minnesota Geospatial Commons. Data added from Quick Layers is pre-symbolized, helping to standardize visualization and map production. Current version: 3.11

    To use Quick Layers with the GDRS, there's no need to download QuickLayers from this location. Instead, download a full copy or a custom subset of the public GDRS (including Quick Layers for ArcGIS Pro 3) using GDRS Manager.

    Quick Layers also allows users to save and share their own pre-symbolized layers, thus increasing efficiency and consistency across the enterprise.

    Installation:

    After using GDRS Manager to create a GDRS, including Quick Layers, add the path to the Quick Layers addin to the list of shared folders:
    1. Open ArcGIS Pro
    2. Project -> Add-In Manager -> Options
    3. Click add folder, and enter the location of the Quick Layers Pro app. For example, if your GDRS is mapped to the V drive, the path would be V:\gdrs\apps\pub\us_mn_state_dnr\quick_layers_pro3
    4. After you do this, the Quick Layers ribbon will be available. To also add Quick Layers to the Quick Access Toolbar at the top, right click Quick Layers, and select Add to Quick Access Toolbar

    The link below is only for those who are using Quick Layers without a GDRS. To get the most functionality out of Quick Layers, don't install it separately, but instead download it as part of a GDRS build using GDRS Manager.

  2. d

    Residential Schools Locations Dataset (Geodatabase)

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Orlandini, Rosa (2023). Residential Schools Locations Dataset (Geodatabase) [Dataset]. http://doi.org/10.5683/SP2/JFQ1SZ
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Orlandini, Rosa
    Time period covered
    Jan 1, 1863 - Jun 30, 1998
    Description

    The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

  3. r

    Add GTFS to a Network Dataset

    • opendata.rcmrd.org
    Updated Jun 27, 2013
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    ArcGIS for Transportation Analytics (2013). Add GTFS to a Network Dataset [Dataset]. https://opendata.rcmrd.org/content/0fa52a75d9ba4abcad6b88bb6285fae1
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    Dataset updated
    Jun 27, 2013
    Dataset authored and provided by
    ArcGIS for Transportation Analytics
    Description

    Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

  4. a

    USA Flood Hazard Areas

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • wifire-data.sdsc.edu
    • +1more
    Updated Jul 11, 2020
    + more versions
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    CA Governor's Office of Emergency Services (2020). USA Flood Hazard Areas [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/maps/CalEMA::usa-flood-hazard-areas
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    Dataset updated
    Jul 11, 2020
    Dataset authored and provided by
    CA Governor's Office of Emergency Services
    Area covered
    Description

    The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance.Dataset SummaryPhenomenon Mapped: Flood Hazard AreasCoordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, the Northern Mariana Islands and American SamoaVisible Scale: The layer is limited to scales of 1:1,000,000 and larger. Use the USA Flood Hazard Areas imagery layer for smaller scales.Source: Federal Emergency Management AgencyPublication Date: April 1, 2019This layer is derived from the April 1, 2019 version of the National Flood Hazard Layer feature class S_Fld_Haz_Ar. The data were aggregated into eight classes to produce the Esri Symbology field based on symbology provided by FEMA. All other layer attributes are derived from the National Flood Hazard Layer. The layer was projected to Web Mercator Auxiliary Sphere and the resolution set to 1 meter.To improve performance Flood Zone values "Area Not Included", "Open Water", "D", "NP", and No Data were removed from the layer. Areas with Flood Zone value "X" subtype "Area of Minimal Flood Hazard" were also removed. An imagery layer created from this dataset provides access to the full set of records in the National Flood Hazard Layer.A web map featuring this layer is available for you to use.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could change the symbology field to Special Flood Hazard Area and set a filter for = “T” to create a map of only the special flood hazard areas. Add labels and set their propertiesCustomize the pop-upUse in analysis tools to discover patterns in the dataArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Areas up to 1,000-2,000 features can be exported successfully.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

  5. World Soils 250m Percent Clay

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • cacgeoportal.com
    Updated Oct 25, 2023
    + more versions
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    Esri (2023). World Soils 250m Percent Clay [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/maps/esri::world-soils-250m-percent-clay/explore
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    Dataset updated
    Oct 25, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent clay (clay).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, clay is defined as particles that are smaller than 0.002mm, making them only visible in an electron microscope. Clay soils contain low amounts of air, and water drains through them very slowly.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for percent clay are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of clay particles (< 0.002 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for clay were used to create this layer. You may access the percent clay in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

  6. Sinks

    • sal-urichmond.hub.arcgis.com
    • hub.arcgis.com
    Updated Mar 16, 2023
    + more versions
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    Esri (2023). Sinks [Dataset]. https://sal-urichmond.hub.arcgis.com/datasets/esri::sinks-2
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    Dataset updated
    Mar 16, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

  7. Watershed Boundary Dataset HUC 12s

    • resilience-fema.hub.arcgis.com
    • resilience.climate.gov
    • +2more
    Updated Sep 6, 2023
    + more versions
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    Esri (2023). Watershed Boundary Dataset HUC 12s [Dataset]. https://resilience-fema.hub.arcgis.com/datasets/esri::watershed-boundary-dataset-huc-12s
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    Dataset updated
    Sep 6, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Each drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological Survey (WBD)Data Vintage: January 7, 2025What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Watershed Boundary Dataset" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

  8. d

    Test Resource for OGC Web Services

    • dataone.org
    • hydroshare.org
    • +2more
    Updated Apr 15, 2022
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    Jacob Wise Calhoon (2022). Test Resource for OGC Web Services [Dataset]. https://dataone.org/datasets/sha256%3A59bae29350865fc2ca6d4c4d3f5995a2a51b7b0ebb9cc8414122cf46a63846c0
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    Dataset updated
    Apr 15, 2022
    Dataset provided by
    Hydroshare
    Authors
    Jacob Wise Calhoon
    Time period covered
    Aug 6, 2020
    Area covered
    Description

    This resource contains the test data for the GeoServer OGC Web Services tutorials for various GIS applications including ArcGIS Pro, ArcMap, ArcGIS Story Maps, and QGIS. The contents of the data include a polygon shapefile, a polyline shapefile, a point shapefile, and a raster dataset; all of which pertain to the state of Utah, USA. The polygon shapefile is of every county in the state of Utah. The polyline is of every trail in the state of Utah. The point shapefile is the current list of GNIS place names in the state of Utah. The raster dataset covers a region in the center of the state of Utah. All datasets are projected to NAD 1983 Zone 12N.

  9. USA Soils Map Units

    • historic-cemeteries.lthp.org
    • a-public-data-collection-for-nepa-sandbox.hub.arcgis.com
    • +1more
    Updated Apr 5, 2019
    + more versions
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    Esri (2019). USA Soils Map Units [Dataset]. https://historic-cemeteries.lthp.org/maps/06e5fd61bdb6453fb16534c676e1c9b9
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    Dataset updated
    Apr 5, 2019
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

  10. World Soils 250m Nitrogen

    • cacgeoportal.com
    • hub.arcgis.com
    Updated Oct 25, 2023
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    Esri (2023). World Soils 250m Nitrogen [Dataset]. https://www.cacgeoportal.com/maps/9d097b7fa0ae40ca8aef757f163d5f75
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    Dataset updated
    Oct 25, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the chemical soil variable nitrogen (nitrogen).Nitrogen is an essential nutrient for sustaining life on Earth. Nitrogen is a core component of amino acids, which are the building blocks of proteins, and of nucleic acids, which are the building blocks of genetic material (RNA and DNA).This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for nitrogen are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Total nitrogen (N) in g/kgCell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for nitrogen were used to create this layer. You may access nitrogen values in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

  11. Built Up Areas Dataset

    • ecat.ga.gov.au
    • researchdata.edu.au
    Updated Aug 31, 2023
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    Commonwealth of Australia (Geoscience Australia) (2023). Built Up Areas Dataset [Dataset]. https://ecat.ga.gov.au/geonetwork/srv/api/records/0508a90a-f048-460a-9bca-4f7f437274d0
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    www:link-1.0-http--linkAvailable download formats
    Dataset updated
    Aug 31, 2023
    Dataset provided by
    Geoscience Australiahttp://ga.gov.au/
    Time period covered
    Jan 1, 2013 - Jan 1, 2018
    Area covered
    Description
    Built up area polygons designed for the AUSTopo - Australian Digital Topographic Map Series 250k. Generated from Bing Building Footprints using the Delineate Built Up Area tool in ArcGIS Pro. Feature class attributes include polygon area (in m2) and feature type (Builtup Area).
  12. USA Census Populated Place Areas

    • hub.arcgis.com
    • prep-response-portal.napsgfoundation.org
    • +4more
    Updated May 5, 2022
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    Esri (2022). USA Census Populated Place Areas [Dataset]. https://hub.arcgis.com/datasets/d8e6e822e6b44d80b4d3b5fe7538576d
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    Dataset updated
    May 5, 2022
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    U.S. Census Populated Place Areas represents the 2020 U.S. Census populated place areas of the United States that include incorporated places, cities, and census designated places identified by the U.S. Census Bureau.This layer is updated annually. The geography is sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2020 total population from the U.S. Census Public Law 94 data. The Population Class field values represent population ranges as follows:Population from 0 - 249Population from 250 - 499Population from 500 - 999Population from 1,000 - 2,499Population from 2,500 - 9,999Population from 10,000 - 49,999Population from 50,000 - 99,999Population from 100,000 - 249,999Population from 250,000 - 499,999Population 500,000 and overThis ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

  13. d

    Florida Reef Tract 2016-2019 Seafloor Elevation Stability Models, Maps, and...

    • catalog.data.gov
    • data.usgs.gov
    Updated Oct 1, 2025
    + more versions
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    U.S. Geological Survey (2025). Florida Reef Tract 2016-2019 Seafloor Elevation Stability Models, Maps, and Tables [Dataset]. https://catalog.data.gov/dataset/florida-reef-tract-2016-2019-seafloor-elevation-stability-models-maps-and-tables
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    Dataset updated
    Oct 1, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2016 and 2019 along the Florida Reef Tract (FRT) from Miami to Key West within a 939.4 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Fehr and others (2021) derived from an elevation-change analysis between two elevation datasets acquired in 2016/2017 and 2019 using the methods of Yates and others (2017). Most of the elevation data from the 2016/2017 time period were collected during 2016, so as an abbreviated naming convention, we refer to this time period as 2016. Due to file size limitations, the elevation-change data was divided into five blocks. A seafloor stability threshold was determined for the 2016-2019 FRT elevation-change datasets based on the vertical uncertainty of the 2016 and 2019 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (total of 235,153,117 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2016 to 2019 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created for each block at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and 14 habitat types found along the FRT. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS Pro map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files for each block; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

  14. Geostatistical Analysis of SARS-CoV-2 Positive Cases in the United States

    • zenodo.org
    • data.niaid.nih.gov
    Updated Sep 17, 2020
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    Peter K. Rogan; Peter K. Rogan (2020). Geostatistical Analysis of SARS-CoV-2 Positive Cases in the United States [Dataset]. http://doi.org/10.5281/zenodo.4032708
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    Dataset updated
    Sep 17, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Peter K. Rogan; Peter K. Rogan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

    This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8th, 2020):

    Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

    Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

    Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

    These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

    The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

  15. Watershed Boundary Dataset HUC 2s

    • anrgeodata.vermont.gov
    • gisnation-sdi.hub.arcgis.com
    • +1more
    Updated Sep 6, 2023
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    Esri (2023). Watershed Boundary Dataset HUC 2s [Dataset]. https://anrgeodata.vermont.gov/datasets/bc0cc62461d1442580412b4fdd49d2b4
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    Dataset updated
    Sep 6, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Each drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological Survey (WBD)Data Vintage: January 7, 2025What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "subsidence" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "subsidence" in the search box, browse to the layer then click OK.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

  16. Global Land Cover 1992-2020

    • cacgeoportal.com
    • climat.esri.ca
    • +3more
    Updated Apr 2, 2020
    + more versions
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    Esri (2020). Global Land Cover 1992-2020 [Dataset]. https://www.cacgeoportal.com/datasets/1453082255024699af55c960bc3dc1fe
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    Dataset updated
    Apr 2, 2020
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meter Source Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary Sphere Extent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer? This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro. In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend. To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth. Different Classifications Available to Map Five processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display. Using Time By default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year. In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change. Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009. This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover. Land Cover Processing To provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015. Source data The datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.php CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) 50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

  17. U

    USA SSURGO - Soil Hydrologic Group

    • data.unep.org
    • idaho-epscor-gem3-uidaho.hub.arcgis.com
    • +2more
    Updated Dec 9, 2022
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    UN World Environment Situation Room (2022). USA SSURGO - Soil Hydrologic Group [Dataset]. https://data.unep.org/app/dataset/wesr-arcgis-wm-usa-ssurgo---soil-hydrologic-group
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    Dataset updated
    Dec 9, 2022
    Dataset provided by
    UN World Environment Situation Room
    Area covered
    United States
    Description

    When rain falls over land, a portion of it runs off into stream channels and storm water systems while the remainder infiltrates into the soil or returns to the atmosphere directly through evaporation.Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow.For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55).Dataset SummaryPhenomenon Mapped: Soil hydrologic groupUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic group is derived from the gSSURGO map unit aggregated attribute table field Hydrologic Group - Dominant Conditions (hydgrpdcd).The seven classes of hydrologic soil group followed by definitions:Group A - Group A soils consist of deep, well drained sands or gravelly sands with high infiltration and low runoff rates.Group B - Group B soils consist of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of infiltration and runoff.Group C - Group C consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of infiltration.Group D - Group D consists of soils with a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a clay pan or clay layer at or near the surface, and soils that are shallow over nearly impervious material.Group A/D - Group A/D soils naturally have a very slow infiltration rate due to a high water table but will have high infiltration and low runoff rates if drained.Group B/D - Group B/D soils naturally have a very slow infiltration rate due to a high water table but will have a moderate rate of infiltration and runoff if drained.Group C/D - Group C/D soils naturally have a very slow infiltration rate due to a high water table but will have a slow rate of infiltration if drained.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "soil hydrologic group" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "soil hydrologic group" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

  18. Z

    Dataset for prediction of Nitrogen Dioxide in Madrid city

    • data.niaid.nih.gov
    Updated Feb 14, 2022
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    Trilles, Sergio (2022). Dataset for prediction of Nitrogen Dioxide in Madrid city [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5101389
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    Dataset updated
    Feb 14, 2022
    Dataset provided by
    Iskandaryan, Ditsuhi
    Ramos, Francisco
    Trilles, Sergio
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The dataset consists of nitrogen dioxide, meteorological data, and traffic data from January to June 2019, which were generated taking into account the spatial distribution of the monitoring stations. Using the ArcGIS Pro software, a grid was created (Top -4,486,449.725263 m; Bottom - 4,466,449.725263 m; Left - 434,215.234430 m; Right - 451,215.234430 m) with a cell size having a width and height equal to 1000 m. There are 340 cells in total (20 by 17). Each cell value includes nitrogen dioxide, meteorological, and traffic attributes from assigned stations at a certain time. The cell value without stations was assigned to zero. The generated grid was exported as Comma Separated Values (CSV) files. Overall, 4,344 CSV files were generated every hour during the first six months of 2019. Meteorological data include ultraviolet radiation, wind speed, wind direction, temperature, relative humidity, barometric pressure, solar irradiance, and precipitation, traffic data includes intensity, occupation, load, and average speed. The datasets have an hourly rate. The data were obtained from the Open Data portal of the Madrid City Council. There are 24 air quality monitoring stations, 26 meteorological monitoring stations, and more than 4,000 traffic measurement points (the location of the measurement points was provided on a monthly basis as these points changed monthly).

  19. d

    Historical coregonine spawning, nursery, and general occurrence point...

    • catalog.data.gov
    • data.usgs.gov
    • +2more
    Updated Oct 1, 2025
    + more versions
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    U.S. Geological Survey (2025). Historical coregonine spawning, nursery, and general occurrence point locations in the Great Lakes of North America and their tributaries [Dataset]. https://catalog.data.gov/dataset/historical-coregonine-spawning-nursery-and-general-occurrence-point-locations-in-the-great
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    Dataset updated
    Oct 1, 2025
    Dataset provided by
    U.S. Geological Survey
    Area covered
    North America, The Great Lakes
    Description

    The dataset presented here, a historical coregonine spawning database, or CORHIST for short, is the result of several years of coordinated research in archives, libraries, and field stations, to track down evidence of spawning locations for the Coregoninae sub-family of ciscoes and whitefishes in the Great Lakes of North America and their tributaries. Our objective was to accurately identify location information to coordinates and add all associated data and metadata to a database built specifically for these types of records (a database capable of storing historical, geospatial, and biological data). Data for a total of 11 accepted species of coregonines are included in this dataset. Spawning or nursery habitat designations were assigned based on a wide-range of evidence from original sources, including descriptions of physiology, ontogeny, and behaviors, interviews, first-hand and Indigenous Ecological Knowledge, and by our own examination of museum specimens. Georeferencing was completed using evidence from original records, including navigational information such as dead reckonings, landmarks like islands, lighthouses, reefs, and river mouths, and by using depth and substrate descriptions. Occasionally, supplemental sources including various historical maps and/or published bathymetry and substrate layers were used to assist in georeferencing points. Data points were summarized and quality-checked using ArcMap 10.8 and ArcGIS Pro (datum: WGS84). Reference tables are also included with this dataset.

  20. i

    HUC8 Boundaries of Indiana 2025

    • indianamap.org
    Updated Jun 4, 2025
    + more versions
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    IndianaMap (2025). HUC8 Boundaries of Indiana 2025 [Dataset]. https://www.indianamap.org/items/cc0a3e7cffcc40299bdeacee93336af3
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    Dataset updated
    Jun 4, 2025
    Dataset authored and provided by
    IndianaMap
    Area covered
    Description

    Source data found here: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServerEach drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological SurveyPublication Date: January 7, 2025What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Watershed Boundary Dataset" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.

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Natural Resources Department (2025). DNR QuickLayers for ArcGIS Pro 3 [Dataset]. https://gisdata.mn.gov/dataset/quick-layers-pro3

DNR QuickLayers for ArcGIS Pro 3

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esri_addinAvailable download formats
Dataset updated
Oct 3, 2025
Dataset provided by
Natural Resources Department
Description

The way to access Layers Quickly.

Quick Layers is an Add-In for ArcGIS Pro 3 that allows rapid access to the DNR's Geospatial Data Resource Site (GDRS). The GDRS is a data structure that serves core geospatial dataset and applications for not only DNR, but many state agencies, and supports the Minnesota Geospatial Commons. Data added from Quick Layers is pre-symbolized, helping to standardize visualization and map production. Current version: 3.11

To use Quick Layers with the GDRS, there's no need to download QuickLayers from this location. Instead, download a full copy or a custom subset of the public GDRS (including Quick Layers for ArcGIS Pro 3) using GDRS Manager.

Quick Layers also allows users to save and share their own pre-symbolized layers, thus increasing efficiency and consistency across the enterprise.

Installation:

After using GDRS Manager to create a GDRS, including Quick Layers, add the path to the Quick Layers addin to the list of shared folders:
1. Open ArcGIS Pro
2. Project -> Add-In Manager -> Options
3. Click add folder, and enter the location of the Quick Layers Pro app. For example, if your GDRS is mapped to the V drive, the path would be V:\gdrs\apps\pub\us_mn_state_dnr\quick_layers_pro3
4. After you do this, the Quick Layers ribbon will be available. To also add Quick Layers to the Quick Access Toolbar at the top, right click Quick Layers, and select Add to Quick Access Toolbar

The link below is only for those who are using Quick Layers without a GDRS. To get the most functionality out of Quick Layers, don't install it separately, but instead download it as part of a GDRS build using GDRS Manager.

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