The ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) Project Package includes all of the layers that are in the NTIA Permitting and Environmental Information Application as well as the APPEIT Tool which will allow users to input a project area and determine what layers from the application overlap with it. An overview of the project package and the APPEIT tool is provided below.

User instructions on how to use the tool are available here. A video explaining how to use the Project Package is also available here.

Project Package Overview

This map package includes all of the layers from the NTIA Permitting and Environmental Information Application. The layers included are all feature services from various Federal and State agencies. The map package was created with ArcGIS Pro 3.4.0. The map package was created to allow users easy access to all feature services including symbology. The map package will allow users to avoid downloading datasets individually and easily incorporate into their own GIS system. The map package includes three maps.

1. Permitting and Environmental Information Application Layers for GIS Analysis - This map includes all of the map tabs shown in the application, except State Data which is provided in another tab. This map includes feature services that can be used for analysis with other project layers such as a route or project area.

2. Permitting and Environmental Information Application Layers – For Reference Only - This map includes layers that cannot be used for analysis since they are either imagery or tile layers.

3. State Data - Reference Only - This map includes all relevant state data that is shown in the application.

The NTIA Permitting and Environmental Information Application was created to help with your permitting planning and environmental review preparation efforts by providing access to multiple maps from publicly available sources, including federal review, permitting, and resource agencies. The application should be used for informational purposes only and is intended solely to assist users with preliminary identification of areas that may require permits or planning to avoid potentially significant impacts to environmental resources subject to the National Environmental Policy Act (NEPA) and other statutory requirements. Multiple maps are provided in the application which are created from public sources. This application does not have an exhaustive list of everything you need for permitting or environmental review for a project but is an initial starting point to see what might be required.

APPEIT Tool OverviewThe Department of Commerce’s National Telecommunications and Information Administration (NTIA) is providing the ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) to help federal broadband grant recipients and subgrantees identify permits and environmental factors as they plan routes for their broadband deployments. Identifying permit requirements early, initiating pre-application coordination with permitting agencies, and avoiding environmental impacts help drive successful infrastructure projects. NTIA’s public release of the APPEIT tool supports government-wide efforts to improve permitting and explore how online and digital technologies can promote efficient environmental reviews.

This Esri ArcGIS Pro tool is included in the map package and was created to support permitting, planning, and environmental review preparation efforts by providing access to data layers from publicly available sources, including federal review, permitting, and resource agencies. An SOP on how to use the tool is available here. For the full list of APPEIT layers, see Appendix Table 1 in the SOP. The tool is comprised of an ArcGIS Pro Project containing a custom ArcGIS Toolbox tool, linked web map shared by the NTIA’s National Broadband Map (NBAM), a report template, and a Tasks item to guide users through using the tool. This ArcGIS Pro project and its contents (maps and data) are consolidated into this (.ppkx) project file.

To use APPEIT, users will input a project area boundary or project route line in a shapefile or feature class format. The tool will return as a CSV and PDF report that lists any federal layers from the ArcGIS Pro Permitting and Environmental Information Web Map that intersect the project. Users may only input a single project area or line at a time; multiple projects or project segments will need to be screened separately. For project route lines, users are required to specify a buffer distance. The buffer distance that is used for broadband projects should be determined by the area of anticipated impact and should generally not exceed 500 feet. For example, the State of Maryland recommends a 100-foot buffer for broadband permitting. The tool restricts buffers to two miles to ensure relevant results.

Disclaimer

This document is intended solely to assist federal broadband grant recipients and subgrantees in better understanding Infrastructure Investment and Jobs Act (IIJA) broadband grant programs and the requirements set forth in the Notice of Funding Opportunity (NOFO) for this program. This document does not and is not intended to supersede, modify, or otherwise alter applicable statutory or regulatory requirements, the terms and conditions of the award, or the specific application requirements set forth in the NOFO. In all cases, statutory and regulatory mandates, the terms and conditions of the award, the requirements set forth in the NOFO, and follow-on policies and guidance, shall prevail over any inconsistencies contained in this document.

NTIA’s ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) should be used for informational purposes only and is intended solely to assist users with preliminary identification of broadband deployments that may require permits or planning to avoid potentially significant impacts to environmental resources subject to the National Environmental Policy Act (NEPA) and other statutory requirements.

The tool is not an exhaustive or complete resource and does not and is not intended to substitute for, supersede, modify, or otherwise alter any applicable statutory or regulatory requirements, or the specific application requirements set forth in any NTIA NOFO, Terms and Conditions, or Special Award Condition. In all cases, statutory and regulatory mandates, and the requirements set forth in NTIA grant documents, shall prevail over any inconsistencies contained in these templates.

The tool relies on publicly available data available on the websites of other federal, state, local, and Tribal agencies, and in some instances, private organizations and research institutions. Layers identified with a double asterisk include information relevant to determining if an “extraordinary circumstance” may warrant more detailed environmental review when a categorical exclusion may otherwise apply. While NTIA continues to make amendments to its websites to comply with Section 508, NTIA cannot ensure Section 508 compliance of federal and non-federal websites or resources users may access from links on NTIA websites.

All data is presented “as is,” “as available” for informational purposes. NTIA does not warrant the accuracy, adequacy, or completeness of this information and expressly disclaims liability for any errors or omissions.

Please e-mail NTIAanalytics@ntia.gov with any questions.

The goal of this project is to create a map of the planet Mars, by using ESRI software. For this, a 3D project was developed using ArcGIS Pro, considering a global scene, to be published in an online platform. All the various data from Mars will be available in a single website, where everyone can visualize and interact. The Red Planet has been studied for many decades and this year marks the launch of a new rover, Mars2020, which will happen on the 17th of July. This new rover will be continuing the on-going work of the Curiosity Rover, launched in 2012. The main objective for these rovers is to determine if Mars could have supported life, by studying its water, climate and geology. Currently, the only operational rover in Mars is Curiosity and with that in mind, this project will have a strong focus on the path taken by this rover, during almost 8 years of exploration. In the web application, the user will be able to see the course taken by Curiosity in Mars’ Gale Crater, from its landing until January 2020. The map highlights several points of interest, such as the location after each year passed on MarsEarth year and every kilometer, which can be interacted with as well as browse through photos taken at each of the locations, through a pop-up window. Additionally, the application also supports global data of Mars. The two main pieces, used as basemaps, are the global imagery, with a pixel size of 925 meters and the Digital Elevation Model (DEM), with 200 meters per pixel. The DEM represents the topography of Mars and was also used to develop Relief and Slope Maps. Furthermore, the application also includes data regarding the geology of the planet and nomenclature to identify regions, areas of interest and craters of Mars. This project wouldn’t have been possible without NASA’s open-source philosophy, working alongside other entities, such as the European Space Agency, the International Astronomical Union and the Working Group for Planetary System Nomenclature. All the data related to Imagery, DEM raster files, Mars geology and nomenclature was obtained on USGS Astrogeology Science Center database. Finally, the data related to the Curiosity Rover was obtained on the portal of The Planetary Society. Working with global datasets means working with very large files, so selecting the right approach is crucial and there isn’t much margin for experiments. In fact, a wrong step means losing several hours of computing time. All the data that was downloaded came in Mars Coordinate Reference Systems (CRS) and luckily, ESRI handles that format well. This not only allowed the development of accurate analysis of the planet, but also modelling the data around a globe. One limitation, however, is that ESRI only has the celestial body for planet Earth, so this meant that the Mars imagery and elevation was wrapped around Earth. ArcGIS Pro allows CRS transformation on the fly, but rendering times were not efficient, so the workaround was to project all data into WGS84. The slope map and respective reclassification and hillshading was developed in the original CRS. This process was done twice: one globally and another considering the Gale Crater. The results show that the crater’s slope characteristics are quite different from the global panorama of Mars. The crater has a depression that is approximately 5000 meters deep, but at the top it’s possible to identify an elevation of 750 meters, according to the altitude system of Mars. These discrepancies in a relatively small area result in very high slope values. Globally, 88% of the area has slopes less than 2 degrees, while in the Gale Crater this value is only 36%. Slopes between 2 and 10 degrees represent almost 60% of the area of the crater. On the other hand, they only represent 10% of the area globally. A considerable area with more than 10 degrees of slope can also be found within the crater, but globally the value is less than 1%. By combining Curiosity’s track path with the DEM, a profile graph of the path was obtained. It is possible to observe that Curiosity landed in a flat area and has been exploring in a “steady path”. However, in the last few years (since the 12th km), the rover has been more adventurous and is starting to climb the crater. In the last 10 km of its journey, Curiosity “climbed” around 300 meters, whereas in the first 11 km it never went above 100 meters. With the data processed in the WGS84 system, all was ready to start modelling Mars, which was firstly done in ArcGIS Pro. When the data was loaded, symbology and pop-ups configured, the project was exported to ArcGIS Online. Both the imagery and elevation layer were exported as “hosted tile service”. This was a key step, since keeping the same level of detail online and offline would have a steep increase in imagery size, to hundreds of Terabytes, thus a lot of work was put into balancing tile cache size and the intended quality of imagery. For the remaining data, it was a straight-forward step, exporting these files as vectors. Once all the data was in the Online Portal, a Global Web Scene was developed. This is an on-going project with an outlook to develop the global scene into an application with ESRI’s AppBuilder, allowing the addition of more information. In the future, there is also interest to increment the displayed data, like adding the paths taken by other rovers in the past, alongside detailed imagery of other areas beyond the Gale Crater. Finally, with 2021 being the year when the new rover Mars2020 will land on the Red Planet, we might be looking into adding it to this project.https://arcg.is/KuS4r

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.

This .zip file contains pre-configured files for members of the public to interact with Kendall County's public GIS layers in a desktop environment. Included are:An ArcGIS Pro PackageA QGIS Project FIleArcGIS Pro requires an ESRI license to use. See the ArcGIS Pro product page for more information.QGIS is free, open-source software that is available for a variety of computing environments. See the QGIS Downloads page to select the appropriate installation method.With the appropriate software installed, users can simply open the corresponding file. It may take a minute or two to load, due to the number of layers that need to load. Once loaded, users will have read-only access to all of the major public layers, and can adjust how they are displayed. In a desktop environment, users can also create and interact with other data sources, such as private site plans, annotations, and other public data layers from non-County entities.Please note that the layers included in these packages are the same live data sources found in the web maps. An internet connection is required for these files to function properly.

Last Update: 06/18/2025 with v10 launch and Reverse Geocode HotfixRequires ArcGIS Pro 3.3.xThis is a file structure with ArcGIS Pro project and layout templates for supporting Urban Search and Rescue Teams in 2024. It points to the latest feature layers and is based on the NWCG Wildfire GIS templates.Updates to this project can be found in the Read Me text document in the root folder of the template after downloading. Some patch notes can also be found below in the comments.Special thanks to NIFC and the Wildfire GIS Community for the starting template. For more documentation see NWCG Standards for Geospatial Operations, PMS 936 | NWCGYOU WILL NOT BE ABLE TO ACCESS any incident data unless you are a member of the NSARGC Group.If the template brings you to a screen saying "Invalid Token", you may need to try downloading it again. How to deploy templateThis template is not a traditional ArcGIS Pro template. When you download this template, you are downloading the full folder structure, pre-made map projects, layouts, databases, and tools that have been designed to work alongside SARCOP. This "template" does not use the "Create a project from a template" workflow within Pro, rather you are downloading the full project, and it can be modified as you see fit from there. Below are the recommended steps to take to deploy the template.Download the template anywhere on your PC by clicking the Download button on the top right below Sign In and Overview. This will download as a Zipped folder, likely to your Downloads folder.Go to the C drive of your computer and create a new folder called "Incidents", then create another folder within that Incidents folder with the name of the incident you are using the template for. For example, if the incident name is "Hurricane Lisa", the folder path should look something like "C:\Incidents\2024xxxx_HurricaneLisa".Extract the zipped folder contents from step 1 to that new incident folder you created in step 2. In the Hurricane Lisa example, data would be extracted to C:\Incidents\2024xxxx_HurricaneLisa.Go to the newly extracted folders and find the Projects folder. Open that and double click on any APRX file to begin work.

The Inventory from Model (White)Model Reservoirs (Red)Mary and Co's reservoirs (Blue)NHD water (Orange)CDL water (Yellow)The Inventory from Model map (1) represents all the water found within the three areas identified the 2023 project; (2) is where the model indicates a reservoir based on the elevation difference based on the Yeager et al. (2017) reservoirs; (3) is based on the Yeager et al. (2017) reservoir boundaries which were plugged into the fifth step of the model (the water identification steps were skipped). Then the model was used to evaluate and classify them into elevation classes. NHD Water map (4) is every waterbody polygon from the National Hydrography Dataset that falls within the three study area areas. The CDL Water map (5) is every open water and aquaculture polygon vectorized from the Cropland Data Layer raster within the three study areas.This Data is a zipped, ArcGIS Pro (V3.1.2) project. The project geodatabase contains the model. In this project there are five maps representing different datasets.NOTE (2024-05-13): ISO xml metadata files added to the root of the zipped folder, describing specific items in the project, saved in ArcGIS Pro V.3.2.2.

The Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_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 map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) 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 10.1 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.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_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 (smis_geology_metadata_faq.pdf). Please read the chis_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: 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: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_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 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 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, 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).

This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. Methodology Every census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point. Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) trees data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

The Digital Geologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sahi_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 map file (.mapx) file (sahi_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sahi_geology.mxd) and individual 10.1 layer (.lyr) 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 10.1 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 GIS readme file (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_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 (sahi_geology_metadata_faq.pdf). Please read the sahi_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: 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 (sahi_geology_metadata.txt or sahi_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 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 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, 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).

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) trees data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

The Digital Geologic-GIS Map of Santa Rosa Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sris_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 map file (.mapx) file (sris_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sris_geology.mxd) and individual 10.1 layer (.lyr) 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 10.1 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.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_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 (sris_geology_metadata_faq.pdf). Please read the chis_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: 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: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sris_geology_metadata.txt or sris_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 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 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, 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).

Last Update: 9/5/2024, Requires ArcGIS Pro 3.2.xThis is a file structure with ArcGIS Pro project and layout templates for supporting Urban Search and Rescue Teams in 2024. It points to the latest feature layers and is based on the NWCG Wildfire GIS templates.Updates to this project can be found in the Read Me text document in the root folder of the template after downloading. Some patch notes can also be found below in the comments.Special thanks to NIFC and the Wildfire GIS Community for the starting template. For more documentation see NWCG Standards for Geospatial Operations, PMS 936 | NWCGYOU WILL NOT BE ABLE TO ACCESS any incident data unless you are a member of the NSARGC Group.If the template brings you to a screen saying "Invalid Token", you may need to try downloading it again. How to deploy templateThis template is not a traditional ArcGIS Pro template. When you download this template, you are downloading the full folder structure, pre-made map projects, layouts, databases, and tools that have been designed to work alongside SARCOP. This "template" does not use the "Create a project from a template" workflow within Pro, rather you are downloading the full project, and it can be modified as you see fit from there. Below are the recommended steps to take to deploy the template.Download the template anywhere on your PC by clicking the Download button on the top right below Sign In and Overview. This will download as a Zipped folder, likely to your Downloads folder.Go to the C drive of your computer and create a new folder called "Incidents", then create another folder within that Incidents folder with the name of the incident you are using the template for. For example, if the incident name is "Hurricane Lisa", the folder path should look something like "C:\Incidents\2024xxxx_HurricaneLisa".Extract the zipped folder contents from step 1 to that new incident folder you created in step 2. In the Hurricane Lisa example, data would be extracted to C:\Incidents\2024xxxx_HurricaneLisa.Go to the newly extracted folders and find the Projects folder. Open that and double click on any APRX file to begin work.

The Digital Geologic-GIS Map of Vicksburg National Military Park, Mississippi and Louisiana is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (vick_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 map file (.mapx) file (vick_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (vick_geology.mxd) and individual 10.1 layer (.lyr) 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 10.1 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 GIS readme file (vick_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (vick_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 (vick_geology_metadata_faq.pdf). Please read the vick_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: 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: Mississippi State University, Department of Geosciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (vick_geology_metadata.txt or vick_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 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 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, 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).

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

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.

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 -

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.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 map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) 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 10.1 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 GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.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 (guis_geomorphology_metadata_faq.pdf). Please read the guis_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: 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 (guis_geomorphology_metadata.txt or guis_geomorphology_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 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 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, 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).

Stormwater Pipe/Conveyance Lines in Fuquay-Varina. Please note that many of the stormwater line features represent privately owned and maintained pipes, and these are essential for mapping and understanding the stormwater drainage network sub-systems at the neighborhood level. Please pay attention to the Subtype field to identify the different categories of public vs. private and culvert type stormwater lines. Directionality (start vs. end vertices) of these line features reflects real world flow direction. The GIS data in the area of Downtown Fuquay-Varina has a lot of old and erroneous stormwater features. A project is currently underway to correct much of this inaccurate stormwater data. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersWater Meter VaultsRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)

In the United States, the federal government manages approximately 28% of the land in the United States. Most federal lands are west of the Mississippi River, where almost half of the land by area is managed by the federal government. Federal lands include 193 million acres managed by the US Forest Service in 154 National Forests and 20 National Grasslands, Bureau of Land Management lands that cover 247 million acres in Alaska and the Western United States, 150 million acres managed for wildlife conservation by the US Fish and Wildlife Service, 84 million acres of National Parks and other lands managed by the National Park Service, and over 30 million acres managed by the Department of Defense. The Bureau of Reclamation manages a much smaller land base than the other agencies included in this layer but plays a critical role in managing the country's water resources. The agencies included in this layer are:Bureau of Land ManagementDepartment of DefenseNational Park ServiceUS Fish and Wildlife ServiceUS Forest ServiceDataset SummaryPhenomenon Mapped: United States federal lands managed by six federal agenciesGeographic Extent: 50 United States and the District of Columbia, Puerto Rico, US Virgin Islands, Guam, American Samoa, and Northern Mariana Islands. The layer also includes National Monuments and Wildlife Refuges in the Pacific Ocean, Atlantic Ocean, and the Caribbean Sea.Data Coordinate System: WGS 1984Visible Scale: The data is visible at all scales but draws best at scales greater than 1:2,000,000Source: BLM, DOD, USFS, USFWS, NPS, PADUS 3.0Publication Date: Various - Esri compiled and published this layer in May 2025. See individual agency views for data vintage.There are six layer views available that were created from this service. Each layer uses a filter to extract an individual agency from the service. For more information about the layer views or how to use them in your own project, follow these links:USA Bureau of Land Management LandsUSA Department of Defense LandsUSA National Park Service LandsUSA Fish and Wildlife Service LandsUSA Forest Service LandsWhat 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 "federal lands" 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 "federal lands" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in Pro.The data can be exported to a file geodatabase, a shapefile or other format and downloaded using the Export Data button on the top right of this webpage.This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.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.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.