The Digital Geologic-GIS Map of War in the Pacific National Historical Park and Vicinity, Guam is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (wapa_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (wapa_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (wapa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wapa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (wapa_geology_metadata_faq.pdf). Please read the wapa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wapa_geology_metadata.txt or wapa_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:50,000 and United States National Map Accuracy Standards features are within (horizontally) 25.4 meters or 83.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for Guam in a GIS-friendly format. The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly. The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

This feature layer contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities. In this update the TRAUMA field was populated for 172 additional hospitals and helipad presence were verified for all hospitals.

This map viewer includes shallow-water (0-50 meter) benthic habitat data collected in submerged lands around Guam managed by Naval Facilities Engineering Command Marianas (NAVFAC Marianas). Military activities have brought economic stimulus to Guam but also displaced and impacted marine ecosystems in the area. The Navy's integrated resource plan recommends potential ways to mitigate the impact of naval activities on Guam’s ecosystems, including its coral reefs. To implement these strategies, NAVFAC Marianas requested new maps for submerged lands under their management. No benthic habitat maps had been produced around Guam since 2010. To meet this need, NOAA's National Centers for Coastal Ocean Science (NCCOS) collaborated with NAVFAC Marianas to develop detailed maps of the distribution of seafloor habitats, beginning with Apra Harbor and Haputo Ecological Reserve Area (ERA). The map products included in this viewer were designed with these and other potential management uses in mind. The geographic information systems (GIS) data behind these layers is also available for download from NOAA National Centers for Environmental Information. For more information about these products, please visit our project pages here and here.

This GIS grid atlas contains precipitation frequency estimates for the Pacific Islands that are based on precipitation data. This atlas is a new release from the NWS and does not update any other publication. The precipitation frequency grids are available for durations from 5 minutes through 60 days, and for average recurrence intervals of 1 year through 1,000 years. All grids are in geographic coordinate system (WGS84 horizontal datum) and units are in 1000th of inches. AMS and PDS results are provided; refer to published documentation for differences between the two.

This data layer was compiled to support the development of spatial priorities for seafloor mapping in Guam. The hexagons included in this data layer identify the study area and sample framework for hallow mapping prioritization for coral management. These hexagons have a side length of 1km (2.6km area), and were created using a hexagon tessellation within the area of the shoreline and 40m depth contours. These data support work that is funded by NOAA’s Coral Reef Conservation Program (CRCP). CRCP has partnered with NOAA’s National Centers for Coastal Ocean Science (NCCOS) to conduct a comprehensive, shallow-water (up to 40 m) seafloor mapping gap analysis within CRCPs jurisdictional regions. NCCOS is leading a mapping prioritization effort and data inventory to 1) identify availability of existing data and products and 2) identify priority locations and approaches for future data collection. Results from this project will enable CRCP to identify priority locations of seafloor mapping needs, technology to be used, and alignment of data needs to the CRCP main pillars of work. Further, the priorities identified through this project will improve coordination among local research and management organizations helping them efficiently leverage resources to map and explore seafloor areas in support of their individual objectives, mandates, and missions.These data and additional details on this project are available here: https://coastalscience.noaa.gov/project/defining-future-seafloor-mapping-priorities-to-inform-shallow-coral-reef-management/

This dataset was created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The purpose of this dataset is to show potential inundation of current Mean Higher High Water (MHHW) for the area.For more information visit the Sea Level Rise Impacts Viewer (https://coast.noaa.gov/slr). For metadata and source map service, see https://coast.noaa.gov/arcgis/rest/services/dc_slr/slr_0ft/MapServer. For additional information or questions, contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

This data analyzes coral bleaching in Guam between 2013-2014. The data supports the Global Coral Bleaching Event (GCBE) sotry map: https://noaa.maps.arcgis.com/home/item.html?id=26f67fa930ae4594b88512ee0a65b841.

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.

This data was downloaded by NOAA's National Centers for Coastal Ocean Science (NCCOS) and was used as background data to help inform decisions for future coral reef management priorities. Link to data sources:CNMI:https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=9473/details/9473& Guam:https://www.coast.noaa.gov/dataviewer/#/lidar/search/-24329478.048348647,1334040.1523264172,-23642645.479524814,1879983.9943472901/details/9322This effort is part of the Guam and the Commonwealth of the Northern Mariana Islands Coral Reef Prioritization Project. Visit the project pages below for more information. https://us-shallow-coral-reef-mapping-priorities-noaa.hub.arcgis.com/https://coastalscience.noaa.gov/project/defining-future-seafloor-mapping-priorities-to-inform-shallow-coral-reef-management/

NOTE: This data is considered preliminary until released with the final report, which is expected the winter of 2023. Neither NOAA or CRCP will be held liable for any decisions made using this data.NOAA developed a systematic, quantitative approach and online spatial prioritization widget to gather mapping priorities from researchers and coral reef managers within the Guam and The Commonwealth of the Northern Mariana Islands (CNMI) coral reef jurisdictions. Participants used virtual coins placed in a customized grid (hexagon with 1 km sides, or 2.5 square km) to express mapping interests, with pull-down menus to indicate data needs and the rationale for their selections. Participants’ inputs were analyzed and used to determine spatial priorities, what Management Uses will be met, and what Map Product Requirements were needed. Priority areas were identified and an assessment was conducted to recommend gap filling methods and the intended management uses. This grid layer contains results from this effort conducted in 2023. Participants completed inputting their priorities from May - July 2023.

This data was downloaded by NOAA's National Centers for Coastal Ocean Science (NCCOS) and was used as background data to help inform decisions for future coral reef management priorities. Footprints of the survey area were generated from downloaded Bathymetric Attributed Grid (BAG) files downloaded from the NOAA National Centers for Environmental Information (NCEI) Bathymetric Data Viewer. The survey area was in multiple sections, thus the individual footprints were then merged into a single layer with records for each region of the survey. Links to data sources:NOAA NCEI Bathymetric Data Viewerhttps://www.ncei.noaa.gov/maps/bathymetry/This effort is part of the Guam and the Commonwealth of the Northern Mariana Islands Coral Reef Prioritization Project. Visit the project pages below for more information. https://us-shallow-coral-reef-mapping-priorities-noaa.hub.arcgis.com/https://coastalscience.noaa.gov/project/defining-future-seafloor-mapping-priorities-to-inform-shallow-coral-reef-management/

This map and data layers were compiled to support the development of spatial priorities for seafloor mapping in shallow waters (-40m) for Guam and the Commonwealth of the Northern Mariana Islands. This work is funded by NOAA’s Coral Reef Conservation Program (CRCP). CRCP has partnered with NOAA’s National Centers for Coastal Ocean Science (NCCOS) to conduct a comprehensive, shallow-water (up to 40 m) seafloor mapping gap analysis within CRCPs jurisdictional regions. NCCOS is leading a mapping prioritization effort and data inventory to 1) identify availability of existing data and products and 2) identify priority locations and approaches for future data collection. Results from this project will enable CRCP to identify priority locations of seafloor mapping needs, technology to be used, and alignment of data needs to the CRCP main pillars of work. Further, the priorities identified through this project will improve coordination among local research and management organizations helping them efficiently leverage resources to map and explore seafloor areas in support of their individual objectives, mandates, and missions.Additional details on this project are available here: https://coastalscience.noaa.gov/project/defining-future-seafloor-mapping-priorities-to-inform-shallow-coral-reef-management/

This EnviroAtlas dataset represents the percentage of land area within each census place in Guam that is classified as Canopy, Impervious Surface, and Vegetation using the 2022 NOAA Coastal Change Analysis Program (C-CAP) dataset. It also includes calculations of these landcover classes per capita (square meters per person) by place based on 2020 United States Census data. Finally, ratios of canopy to impervious surface and vegetation to impervious surface were calculated by place. Vegetation includes canopy and scrub/shrub classes, but does not include other types of green space. Table download This is only the data table. To associate with Census Places, download https://enviroatlas.epa.gov/download/Pacific_Territories_bgrps_and_places.gdb.zip.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Erosion, the loss of soil due to the effects of water and wind, can lead to serious degradation of lands and the loss of agricultural productivity.This layer classifies the amount of soil loss in the top soil layers in 5 classes:None: Area of soil deposition.Class 1: In this map unit,1 to 25 percent of the original topsoil has been lost to erosion. Class 2: In this map unit, 1 to 25 percent of the original topsoil has been lost to erosion.Class 3: In this map unit, 75 to 99 percent of the original topsoil has been lost to erosion.Class 4: In this map unit, all of the original topsoil has been lost to erosionDataset SummaryPhenomenon Mapped: Top soil loss due to erosionUnits: 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 runoff is derived from the gSSURGO component table field Erosion Class (erocl). The value in this layer is the dominant condition found within the map unit.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 "erosion class" 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 "erosion class" 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.

Polygons of administrative boundaries within U.S. EPA Region 9. This map services contains the following layers:

Villages, American Somoa, 1981, ASDoC Cities, Region 9, 2017, NAVTEQ Urban Areas (Designated), Region 9, 2018, TIGER Municipal Villages, Guam, 2005, BoP Detailed Territories, Guam and MP, 2012, NOAA Islands, Region 9, 2017, NAVTEQ Tribal Sub-divisions, Region 9, 2018, TIGER Tribal Lands, Region 9, 2016, BIA and BLM Tribal Lands Generalized, Region 9, 2018, TIGER County Sub-divisions, Region 9, 2018, TIGER Counties, MP, 2005, USGS Counties, Region 9, 2017, NAVTEQ States and Territories, Region 9, 2015, TIGER

IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.The data is part of the Benthic Habitat Mapping in American Samoa, Guam, and Commonwealth of the Northern Mariana Islands project, which mapped the coral reef habitats of the islands by visual interpretation and manual delineation of IKONOS satellite imagery. The project produced a georeferenced, digital atlas of benthic habitat maps for the shallow-water (< 30m) coral ecosystem habitats that support a variety of management applications, including informing resource management decisions, ensuring safe navigation, supporting coastal communities, sustaining coastal habitats, and mitigating coastal hazards.Learn more at the project page and download data and metadata at the Guam data page.

Web Map for the National Geodetic Survey (NGS) GPS on Bench Marks for the Transformation Tool (GPSonBM TT) Campaign that is currently underway and will continue until early 2023. This campaign will provide users the ability to explore the priority list of bench marks and will be regularly updated to provide the best marks to help NGS develop the best transformation tool for converting heights from the current vertical datums to the North American-Pacific Geopotential Datum of 2022 (NAPGD2022) that is planned to be available at the end of 2022. This includes the following vertical datums: the North American Vertical Datum of 1988 (NAVD 88) for the Conterminous US and Alaska; the Puerto Rico Vertical Datum of 2002 (PRVD02) for Puerto Rico; the Virgin Islands Vertical Datum of 2009 (VIVD09) for the US Virgin Islands; the Guam Vertical Datum of 2004 (GUVD04) for Guam; and the Northern Marianas Vertical Datum of 2003 (NMVD03) for the Commonwealth of the Northern Mariana Islands. Note that the current American Samoa Vertical Datum of 2002 (ASVD02) is planned to be deprecated since it is no longer valid due to an earthquake. For more information about vertical datums visit the NGS Vertical Datums web page.This Web Map is the foundation to the NGS GPS on Bench Marks Transformation Tool Web Map ApplicationData for this web map can be found here:NGS GPS on Bench Marks Web Site

Soil loss tolerance factor is the maximum rate of soil loss that will permit crop productivity to be sustained economically and indefinitely on a given soil. Soil loss tolerance is expressed as tons/acre/year. The primary use for soil loss tolerance factor is evaluating the effectiveness of erosion control measures on farmland. Soil loss tolerance factor serves as a quantitative standard to compare to erosion rate estimates from models such as the Revised Universal Soil Loss Equation. Farmlands where soil loss tolerance factor is less than modeled erosion rates are considered unsustainable.Dataset SummaryPhenomenon Mapped: Soil loss toleranceUnits: tons/acre/yearCell 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 soil loss tolerance is derived from the gSSURGO component table field T (tfact). The value in this layer is the average value for all components of each map unit weighted by component percent (comppct_r). 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 "loss tolerance" 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 "loss tolerance" 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.

The National Commodity Crop Productivity Index (NCCPI) ranks the inherent capability of soils to produce agricultural crops without irrigation. For more information on how the NCCPI is calculated see User Guide for the National Commodity Crop Productivity Index.Dataset SummaryPhenomenon Mapped: National Commodity Crop Productivity Index version 3.0Units: Thousandths of nccpi3all index value, served as integers (this layer's value of 889 equals 0.889 in the nccpi3all)Cell 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 2021, except Puerto Rico and US Virgin Islands which are July 2020.ArcGIS 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 the National Commodity Crop Productivity Index is derived from the gSSURGO valu1 table field nccpi3all.Note: This layer serves the National Commodity Crop Productivity Index value from the 2021 version for Puerto Rico and the US Virgin Islands. In 2022 the gNATSGO source was missing its Valu1 table for Puerto Rico and the US Virgin Islands.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 crop production" 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 crop production" 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.