100+ datasets found
  1. a

    mpls com gar inven

    • umn.hub.arcgis.com
    Updated Jul 31, 2020
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    University of Minnesota (2020). mpls com gar inven [Dataset]. https://umn.hub.arcgis.com/datasets/UMN::mpls-com-gar-inven/about
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    Dataset updated
    Jul 31, 2020
    Dataset authored and provided by
    University of Minnesota
    Area covered
    Description

    Feature layer generated from running the Merge Layers solution.

  2. a

    LEARN (Virtual) 5 19 21 - copy

    • hub.arcgis.com
    Updated May 25, 2021
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    eva.pereira_lahub (2021). LEARN (Virtual) 5 19 21 - copy [Dataset]. https://hub.arcgis.com/maps/lahub::learn-virtual-5-19-21-copy
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    Dataset updated
    May 25, 2021
    Dataset authored and provided by
    eva.pereira_lahub
    Area covered
    Description

    Feature layer generated from running the Merge Layers solution.

  3. USDA Census of Agriculture 2017 - Wheat Production

    • resilience.climate.gov
    • ars-geolibrary-usdaars.hub.arcgis.com
    Updated Aug 16, 2022
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    Esri (2022). USDA Census of Agriculture 2017 - Wheat Production [Dataset]. https://resilience.climate.gov/datasets/070ce5f4390c4be4b077ab88820052a7
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    Dataset updated
    Aug 16, 2022
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes wheat production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Wheat ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedOperations with SalesProduction in BushelsSales in US DollarsIrrigated Area Harvested in AcresOperations with Irrigated Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.Many other ready-to-use layers derived from the Census of Agriculture can be found in the Living Atlas Agriculture of the USA group.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

  4. d

    ArcGIS Online: Map Viewer

    • fed.dcceew.gov.au
    Updated Apr 3, 2023
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    Dept of Climate Change, Energy, the Environment & Water (2023). ArcGIS Online: Map Viewer [Dataset]. https://fed.dcceew.gov.au/datasets/arcgis-online-map-viewer
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    Dataset updated
    Apr 3, 2023
    Dataset authored and provided by
    Dept of Climate Change, Energy, the Environment & Water
    Description

    This Guide is designed to assist you with using ArcGIS Online (AGOL)'s Map Viewer.An ArcGIS web map is an interactive display of geographic information. Web maps are made by adding and combining layers. The layers are made from data, they are logical collections of geographic data.Map Viewer can be used to view, explore and create web maps in ArcGIS Online.

  5. Range: Pasture (Feature Layer)

    • agdatacommons.nal.usda.gov
    • datadiscoverystudio.org
    • +4more
    bin
    Updated Apr 1, 2025
    + more versions
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    U.S. Forest Service (2025). Range: Pasture (Feature Layer) [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Range_Pasture_Feature_Layer_/25973101
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    binAvailable download formats
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    U.S. Forest Service
    License

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

    Description

    Designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

  6. a

    Group Layer - Vermont Active Parcels, Property Transfers, and Land Surveys

    • geodata1-59998-vcgi.opendata.arcgis.com
    • geodata.vermont.gov
    • +2more
    Updated Apr 3, 2025
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    VT Center for Geographic Information (2025). Group Layer - Vermont Active Parcels, Property Transfers, and Land Surveys [Dataset]. https://geodata1-59998-vcgi.opendata.arcgis.com/content/1ca61e19b6f843e2843d85146cac8159
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    Dataset updated
    Apr 3, 2025
    Dataset authored and provided by
    VT Center for Geographic Information
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    Created April 2025.This ArcGIS Online group layer retains the functionality found in the Vermont Parcel Viewer for dynamically relating information across all three layers, including Arcade expressions and dependent popup configurations.Documentation of arcade expressions in use is available on github here.For information regarding parcel data, see the Vermont Parcel Program: https://vcgi.vermont.gov/data-and-programs/parcel-programFor information regarding spatialized property transfers, see this post: https://vcgi.vermont.gov/data-release/vermont-property-transfers-now-available-spatial-dataFor information regarding digital copies of land surveys, see the Vermont Survey Library: https://landsurvey.vermont.gov/Access Vermont's standardized GIS parcel data here: https://geodata.vermont.gov/pages/parcelsNOTE: this group layer is shared separately as a stand-alone item rather than being accessible through the Vermont Parcel Viewer.

  7. u

    GIS Clipping and Summarization Toolbox

    • data.nkn.uidaho.edu
    • verso.uidaho.edu
    Updated Dec 15, 2021
    + more versions
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    Justin L. Welty; Michelle I. Jeffries; Robert S. Arkle; David S. Pilliod; Susan K. Kemp (2021). GIS Clipping and Summarization Toolbox [Dataset]. http://doi.org/10.5066/P99X8558
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    zip compressed directory(688 kilobytes)Available download formats
    Dataset updated
    Dec 15, 2021
    Dataset provided by
    USGS Public Project Explorer
    Authors
    Justin L. Welty; Michelle I. Jeffries; Robert S. Arkle; David S. Pilliod; Susan K. Kemp
    License

    https://creativecommons.org/licenses/publicdomain/https://creativecommons.org/licenses/publicdomain/

    https://spdx.org/licenses/CC-PDDChttps://spdx.org/licenses/CC-PDDC

    Description

    Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

  8. NOAA METAR Station Data

    • hub.arcgis.com
    Updated Dec 13, 2021
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    Esri Tutorials (2021). NOAA METAR Station Data [Dataset]. https://hub.arcgis.com/maps/LearnGIS::noaa-metar-station-data
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    Dataset updated
    Dec 13, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Tutorials
    Area covered
    Description

    Feature layer generated from the Current Weather and Wind Station Data Living Atlas layer for the Learn ArcGIS lesson Predict weather with real-time data.

  9. Imhof style for ArcGIS Pro

    • cacgeoportal.com
    • hub.arcgis.com
    Updated Aug 29, 2018
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    Esri Styles (2018). Imhof style for ArcGIS Pro [Dataset]. https://www.cacgeoportal.com/content/1f25b31793cd4e7391b0cd51b9b79783
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    Dataset updated
    Aug 29, 2018
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Styles
    Description

    Created to honor the impressionistic atmospheric quality of the work of Swiss topographic painter and cartographer, Eduard Imhof. These symbols and palettes allow for the application of an homage aesthetic when applied to layered hillshades and digital elevation models. An accompanying how-to resource is forthcoming.In the meantime, the Hillshade color scheme is intended to be applied to a traditional hillshade layer and a multidirectional hillshade layer. The Mist color scheme is intended to be applied to a DEM layer. When viewed in concert with an imagery basemap, the hues and opacities combine to create a distinctive quality.Here it is at a broader scale...Here is a map that uses the Area of Interest, Mask, and Locator layers...Contents:Alternatively, you can download an ArcGIS Pro project with the data and styles already implemented, and you can just start cranking away at Imhofs.Happy Topographic Painting! John Nelson

  10. a

    India: Surface Water

    • hub.arcgis.com
    Updated Mar 22, 2022
    + more versions
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    GIS Online (2022). India: Surface Water [Dataset]. https://hub.arcgis.com/maps/esriindia1::india-surface-water/about?path=
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    Dataset updated
    Mar 22, 2022
    Dataset authored and provided by
    GIS Online
    Area covered
    Description

    Water bodies are a key element in the landscape. This layer provides a global map of large water bodies for use in landscape-scale analysis.Dataset SummaryThis layer provides access to a 250m cell-sized raster of surface water created by extracting pixels coded as water in the Global Lithological Map and the Global Landcover Map. The layer was created by Esri in 2014.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. 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.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

  11. California Overlapping Cities and Counties and Identifiers with Coastal...

    • data.ca.gov
    • gis.data.ca.gov
    • +1more
    Updated Feb 20, 2025
    + more versions
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    California Department of Technology (2025). California Overlapping Cities and Counties and Identifiers with Coastal Buffers [Dataset]. https://data.ca.gov/dataset/california-overlapping-cities-and-counties-and-identifiers-with-coastal-buffers
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    kml, gdb, zip, gpkg, xlsx, arcgis geoservices rest api, geojson, csv, txt, htmlAvailable download formats
    Dataset updated
    Feb 20, 2025
    Dataset authored and provided by
    California Department of Technologyhttp://cdt.ca.gov/
    Area covered
    California
    Description

    WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:

    • Metadata is missing or incomplete for some layers at this time and will be continuously improved.
    • We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.
    This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.

    Purpose

    County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.

    Related Layers

    This dataset is part of a grouping of many datasets:

    1. Cities: Only the city boundaries and attributes, without any unincorporated areas
    2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
    3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
    4. Place Abbreviations
    5. Unincorporated Areas (Coming Soon)
    6. Census Designated Places (Coming Soon)
    7. Cartographic Coastline
    Working with Coastal Buffers
    The dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.

    Point of Contact

    California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

    Field and Abbreviation Definitions

    • COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system
    • Place Name: CDTFA incorporated (city) or county name
    • County: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
    • Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
    • GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
    • GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information System
    • Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
    • CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
    • Area_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
    • COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
    • GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

    Accuracy

    CDTFA"s source data notes the following about accuracy:

    City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated

  12. Range: Allotment (Feature Layer)

    • catalog.data.gov
    • datadiscoverystudio.org
    • +8more
    Updated May 8, 2025
    + more versions
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    U.S. Forest Service (2025). Range: Allotment (Feature Layer) [Dataset]. https://catalog.data.gov/dataset/range-allotment-feature-layer-3c514
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    Dataset updated
    May 8, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Description

    Designates boundaries to establish extent of livestock distribution and management within the allotment. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.

  13. USA Aquifers

    • colorado-river-portal.usgs.gov
    • resilience-fema.hub.arcgis.com
    • +1more
    Updated Feb 28, 2022
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    Esri (2022). USA Aquifers [Dataset]. https://colorado-river-portal.usgs.gov/datasets/e049a24713bf4377aa9e8f268f960af4
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    Dataset updated
    Feb 28, 2022
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Aquifers are underground layers of gravel, sand, or permeable rock that contain ground water. This water can be extracted using a well and provides an important source of water in many regions of the world.This layer, produced as part of the Ground Water Atlas of the United States, provides access to the areal extent of the principal aquifers of the United States. In areas where multiple aquifers exist at different depths below the surface only the shallowest aquifer is included.This layer does not display all areas where ground water exists. The U.S. Geologic Survey (USGS) mapped these aquifers by interpreting surface features and aquifers may extend beyond these features. Ground water areas along watercourses and ground water in unconsolidated glacial sand and gravel deposits are not included in this layer. Data on these areas are provided in the layer Aquifers of Alluvial and Glacial Origin from the USGS.Dataset SummaryPhenomenon Mapped: Aquifers of the United StatesCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Hawaii, Puerto Rico, and the U.S. Virgin IslandsVisible Scale: All ScalesSource: Groundwater Atlas of the United StatesPublication Date: October 1, 2003Please note: "This dataset, published in 2003, contains the shallowest principal aquifers of the conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands, portrayed as polygons. The map layer was developed as part of the effort to produce the maps published at 1:2,500,000 in the printed series Ground Water Atlas of the United States. The published maps contain base and cultural features not included in these data. Please note that the maps do not show the entire extent of an aquifer, only its subcrop or outcrop area. Refer to the metadata for a complete description of the files and how they were generated." (Source USGS)What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

  14. a

    USDA Census of Agriculture 2017 - Sales and Equipment

    • ars-geolibrary-usdaars.hub.arcgis.com
    • resilience.climate.gov
    Updated Aug 16, 2022
    + more versions
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    Esri (2022). USDA Census of Agriculture 2017 - Sales and Equipment [Dataset]. https://ars-geolibrary-usdaars.hub.arcgis.com/datasets/esri::usda-census-of-agriculture-2017-sales-and-equipment
    Explore at:
    Dataset updated
    Aug 16, 2022
    Dataset authored and provided by
    Esri
    Area covered
    Description

    The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes farm and ranch sales plus the number and value of machines and trucks owned by operators from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: Farm and Ranch Sales, Machinery and Truck inventory and ValueCoordinate System: Web Mercator Auxiliary SphereExtent: United States including Hawaii and AlaskaVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Number of Operations - AnimalsSales in US Dollars - AnimalsNumber of Operations - CropsSales in US Dollars - CropsTotal Value in US Dollars - MachineryTractors - InventoryTrucks Including Pickups - InventoryAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

  15. Sentinel-2 10m Land Use/Land Cover Change from 2018 to 2021 (Mature Support)...

    • hub.arcgis.com
    • geoportal-pacificcore.hub.arcgis.com
    • +1more
    Updated Feb 10, 2022
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    Esri (2022). Sentinel-2 10m Land Use/Land Cover Change from 2018 to 2021 (Mature Support) [Dataset]. https://hub.arcgis.com/datasets/30c4287128cc446b888ca020240c456b
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    Dataset updated
    Feb 10, 2022
    Dataset authored and provided by
    Esrihttp://esri.com/
    License

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

    Area covered
    Description

    Important Note: This item is in mature support as of February 2023 and will be retired in December 2025. 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 displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 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 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. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas 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.2. TreesAny significant clustering of tall (~15-m 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).4. Flooded 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.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built 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.8. Bare 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.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open 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.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.For questions please email environment@esri.com

  16. a

    United States of America Soile Survey Geographic Database (SSURGO) - Soil...

    • chi-phi-nmcdc.opendata.arcgis.com
    Updated May 19, 2022
    + more versions
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    New Mexico Community Data Collaborative (2022). United States of America Soile Survey Geographic Database (SSURGO) - Soil Hydrologic Group, 2021 [Dataset]. https://chi-phi-nmcdc.opendata.arcgis.com/maps/82e3a76807c04c08bdbf6bf321fa8c18
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    Dataset updated
    May 19, 2022
    Dataset authored and provided by
    New Mexico Community Data Collaborative
    Area covered
    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.

  17. a

    USDA Census of Agriculture 2017 - Cotton Production

    • ars-geolibrary-usdaars.hub.arcgis.com
    • resilience.climate.gov
    • +1more
    Updated Aug 16, 2022
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    Esri (2022). USDA Census of Agriculture 2017 - Cotton Production [Dataset]. https://ars-geolibrary-usdaars.hub.arcgis.com/items/1462b2f8a2944a25ba04a81739202dba
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    Dataset updated
    Aug 16, 2022
    Dataset authored and provided by
    Esri
    Area covered
    Description

    The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes cotton production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Cotton ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedOperations with SalesProduction in BalesSales in US DollarsIrrigated Area Harvested in AcresOperations with Irrigated Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

  18. a

    Virginia State Parks Locations

    • umn.hub.arcgis.com
    Updated Feb 17, 2022
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    University of Minnesota (2022). Virginia State Parks Locations [Dataset]. https://umn.hub.arcgis.com/maps/UMN::virginia-state-parks-locations
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    Dataset updated
    Feb 17, 2022
    Dataset authored and provided by
    University of Minnesota
    Area covered
    Description

    Feature layer generated from running the Merge Layers solution.

  19. e

    Geodatabase for the Baltimore Ecosystem Study Spatial Data

    • portal.edirepository.org
    • search.dataone.org
    application/vnd.rar
    Updated May 4, 2012
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    Jarlath O'Neal-Dunne; Morgan Grove (2012). Geodatabase for the Baltimore Ecosystem Study Spatial Data [Dataset]. http://doi.org/10.6073/pasta/377da686246f06554f7e517de596cd2b
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    application/vnd.rar(29574980 kilobyte)Available download formats
    Dataset updated
    May 4, 2012
    Dataset provided by
    EDI
    Authors
    Jarlath O'Neal-Dunne; Morgan Grove
    Time period covered
    Jan 1, 1999 - Jun 1, 2014
    Area covered
    Description

    The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

       BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.
    
    
       Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.
    
    
       For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.
    
    
       Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 
    
    
       This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.
    
    
       See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt
    
    
       See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt
    
  20. c

    USDA Census of Agriculture 2017 - Hay Production

    • resilience.climate.gov
    • ars-geolibrary-usdaars.hub.arcgis.com
    Updated Aug 16, 2022
    + more versions
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    Esri (2022). USDA Census of Agriculture 2017 - Hay Production [Dataset]. https://resilience.climate.gov/datasets/esri::usda-census-of-agriculture-2017-hay-production-
    Explore at:
    Dataset updated
    Aug 16, 2022
    Dataset authored and provided by
    Esri
    Area covered
    Description

    The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes hay production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Hay ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Alaska, and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedProduction in TonsAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.Many other ready-to-use layers derived from the Census of Agriculture can be found in the Living Atlas Agriculture of the USA group.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

Share
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University of Minnesota (2020). mpls com gar inven [Dataset]. https://umn.hub.arcgis.com/datasets/UMN::mpls-com-gar-inven/about

mpls com gar inven

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Dataset updated
Jul 31, 2020
Dataset authored and provided by
University of Minnesota
Area covered
Description

Feature layer generated from running the Merge Layers solution.

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