21 datasets found
  1. B

    Residential Schools Locations Dataset (Geodatabase)

    • borealisdata.ca
    • search.dataone.org
    Updated May 31, 2019
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    Rosa Orlandini (2019). Residential Schools Locations Dataset (Geodatabase) [Dataset]. http://doi.org/10.5683/SP2/JFQ1SZ
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 31, 2019
    Dataset provided by
    Borealis
    Authors
    Rosa Orlandini
    License

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

    Time period covered
    Jan 1, 1863 - Jun 30, 1998
    Area covered
    Canada
    Description

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

  2. a

    IRWIN Data Service User's Guide ( 20190325)

    • gis-fema.hub.arcgis.com
    • prep-response-portal.napsgfoundation.org
    • +2more
    Updated Nov 5, 2019
    + more versions
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    NAPSG Foundation (2019). IRWIN Data Service User's Guide ( 20190325) [Dataset]. https://gis-fema.hub.arcgis.com/documents/29d7b53aecc1491b9d42fd559368b22f
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    Dataset updated
    Nov 5, 2019
    Dataset authored and provided by
    NAPSG Foundation
    Description

    IntroductionIRWIN ArcGIS Online GeoPlatform Services The Integrated Reporting of Wildland-Fire Information (IRWIN) Production data is replicated every 60 seconds to the ArcGIS Online GeoPlatform organization so that read-only views can be provided for consumers. This replicated view is called the hosted datastore. The “IRWIN Data” group is a set of Feature Layer views based on the replicated IRWIN layers. These feature layers provide a near real-time feed of all valid IRWIN data. All incidents that have been shared through the integration service since May 20, 2014 are available through this service. The incident data provides the location of existing fires, size, conditions and several other attributes that help classify fires. The IRWIN Data service allows users to create a web map, share it with their organization, or pull it into ArcMap or ArcGIS Pro for more in-depth analysis.InstructionsTo allow the emergency management GIS staff to join the IRWIN Data group, they will need to set up an ArcGIS Online account through our account manager. Please send the response to Samantha Gibbes (Samantha.C.Gibbes@saic.com) and Kayloni Ahtong (kayloni_ahtong@ios.doi.gov). Use the below template and fill in each part as best as possible, where the point of contact (POC) is the person responsible for the account.Reply Email Body: The (name of application) application requests the following user account and access to the IRWIN Data group.POC Name: First name Last name and titlePOC Email: Username: <>_irwin (choose a username, something short, followed by _irwin)Business Justification: Once you are set up with the account, I will coordinate a call to go over any questions.

  3. l

    Place Vulnerability Analysis Solution for ArcGIS Pro (BETA)

    • visionzero.geohub.lacity.org
    • opendata.rcmrd.org
    Updated Feb 12, 2019
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    NAPSG Foundation (2019). Place Vulnerability Analysis Solution for ArcGIS Pro (BETA) [Dataset]. https://visionzero.geohub.lacity.org/content/ee44dd7cd11c4017a67d43fcbb1cb467
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    Dataset updated
    Feb 12, 2019
    Dataset authored and provided by
    NAPSG Foundation
    License

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

    Area covered
    Description

    Purpose: This is an ArcGIS Pro template that GIS Specialists can use to identify vulnerable populations and special needs infrastructure most at risk to flooding events.How does it work?Determine and understand the Place Vulnerability (based on Cutter et al. 1997) and the Special Needs Infrastructure for an area of interest based on Special Flood Hazard Zones, Social Vulnerability Index, and the distribution of its Population and Housing units. The final product will be charts of the data distribution and a Hosted Feature Layer. See this Story Map example for a more detailed explanation.This uses the FEMA National Flood Hazard Layer as an input (although you can substitute your own flood hazard data), check availability for your County before beginning the Task: FEMA NFHL ViewerThe solution consists of several tasks that allow you to:Select an area of interest for your Place Vulnerability Analysis. Select a Hazard that may occur within your area of interest.Select the Social Vulnerability Index (SVI) features contained within your area of interest using the CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI layer at the census tract level in the map.Determine and understand the Social Vulnerability Index for the hazard zones identified within you area of interest.Identify the Special Needs Infrastructure features located within the hazard zones identified within you area of interest.Share your data to ArcGIS Online as a Hosted Feature Layer.FIRST STEPS:Create a folder C:\GIS\ if you do not already have this folder created. (This is a suggested step as the ArcGIS Pro Tasks does not appear to keep relative paths)Download the ZIP file.Extract the ZIP file and save it to the C:\GIS\ location on your computer. Open the PlaceVulnerabilityAnalysis.aprx file.Once the Project file (.aprx) opens, we suggest the following setup to easily view the Tasks instructions, the Map and its Contents, and the Databases (.gdb) from the Catalog pane.The following public web map is included as a Template in the ArcGIS Pro solution file: Place Vulnerability Template Web MapNote 1:As this is a beta version, please take note of some pain points:Data input and output locations may need to be manually populated from the related workspaces (.gdb) or the tools may fail to run. Make sure to unzip/extract the file to the C:\GIS\ location on your computer to avoid issues.Switching from one step to the next may not be totally seamless yet.If you are experiencing any issues with the Flood Hazard Zones service provided, or if the data is not available for your area of interest, you can also download your Flood Hazard Zones data from the FEMA Flood Map Service Center. In the search, use the FEMA ID. Once downloaded, save the data in your project folder and use it as an input.Note 2:In this task, the default hazard being used are the National Flood Hazard Zones. If you would like to use a different hazard, you will need to add the new hazard layer to the map and update all query expressions accordingly.For questions, bug reports, or new requirements contact pdoherty@publicsafetygis.org

  4. n

    ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) Project...

    • nbam.ntia.gov
    Updated Dec 20, 2024
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    NBAM_Org (2024). ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) Project Package [Dataset]. https://nbam.ntia.gov/content/37fa42c6313e4bdb9d8a9c05d2624891
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    Dataset updated
    Dec 20, 2024
    Dataset authored and provided by
    NBAM_Org
    Description

    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.

  5. l

    USAR ArcGIS Pro Template d617e4

    • visionzero.geohub.lacity.org
    • opendata.rcmrd.org
    Updated Apr 15, 2024
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    FEMA AGOL (2024). USAR ArcGIS Pro Template d617e4 [Dataset]. https://visionzero.geohub.lacity.org/content/082da9f1b1ba4053b2ec6d8b7cd617e4
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    Dataset updated
    Apr 15, 2024
    Dataset authored and provided by
    FEMA AGOL
    Description

    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.

  6. l

    USAR ArcGIS Pro Template - a47ec7

    • visionzero.geohub.lacity.org
    • opendata.rcmrd.org
    Updated Apr 11, 2025
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    SARGeo (2025). USAR ArcGIS Pro Template - a47ec7 [Dataset]. https://visionzero.geohub.lacity.org/content/949bda15254d4c58911843ca28a47ec7
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    Dataset updated
    Apr 11, 2025
    Dataset authored and provided by
    SARGeo
    Description

    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.

  7. Grocery Access Map Gallery

    • supply-chain-data-hub-nmcdc.hub.arcgis.com
    Updated Apr 20, 2021
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    Urban Observatory by Esri (2021). Grocery Access Map Gallery [Dataset]. https://supply-chain-data-hub-nmcdc.hub.arcgis.com/datasets/UrbanObservatory::grocery-access-map-gallery
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    Dataset updated
    Apr 20, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    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

  8. a

    North Complex

    • nifc.hub.arcgis.com
    Updated Sep 30, 2020
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    National Interagency Fire Center (2020). North Complex [Dataset]. https://nifc.hub.arcgis.com/maps/nifc::north-complex/about?path=
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    Dataset updated
    Sep 30, 2020
    Dataset authored and provided by
    National Interagency Fire Center
    Area covered
    Description

    Do not share this map Publicly!This template is for ACTIVE INCIDENTS only. For training, please use the Training template (found here). This workflow uses one template web map and contains all layers of the National Incident Feature Service in a single service (Unlike the standard template which splits features into Edit, View, and Repair services). It is for teams looking for a simple approach to ArcGIS Online implementation. All features are visible; editing is enabled for points, lines, and polygons and disabled for the IR layers [Workflow LINK]; contains the National Incident Feature Service layers: NWCG approved Event schema.This template web map is provided for quick deployment. Listed next are the steps to implement this Standard Workflow:1) Open this web map template in Map Viewer2) Do a Save As (Click Save and select Save As)3) Zoom to your fire area and add bookmarks4) Look for a red triangle polygon with your fire's attributes - do either of these: a. Use this polygon as a start for your incident and modify as needed b. Copy the attributes (most importantly, the IRWIN ID) into a new polygon and delete the triangle (delete in ArcMap or Pro)5) Create a display filter on features to only show features related to your incident (Optional).6) Create a new Photo Point Layer (Content > Create > Feature Layer > From Existing > #TEMPLATE - PhotoPoint). Add this to your web map and remove default PhotoPoint Layer7) Share with your Mobile Editing group8) Add necessary incident personnel to the Mobile Editing group9) Make available for Viewers:a. Save out a second version of this map and disable editing on all the layers except Photo Points.b. Share this version with the Viewing group.10) To track and manage suppression repair needs use the Suppression Repair Add-on

  9. TopoBathy

    • hub.arcgis.com
    • opendata.rcmrd.org
    • +2more
    Updated Apr 11, 2014
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    Esri (2014). TopoBathy [Dataset]. https://hub.arcgis.com/datasets/c753e5bfadb54d46b69c3e68922483bc
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    Dataset updated
    Apr 11, 2014
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This World Elevation TopoBathy service combines topography (land elevation) and bathymetry (water depths) from various authoritative sources from across the globe. Heights are orthometric (sea level = 0), and bathymetric values are negative downward from sea level. The source data of land elevation in this service is same as in the Terrain layer. When possible, the water areas are represented by the best available bathymetry. Height/Depth units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select additional functions, applied on the server, that return rendered data. For visualizations such as hillshade or elevation tinted hillshade, consider using the appropriate server-side function defined on this service. Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns. NOTE: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the max extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

    Slope Degrees Slope Percentage Hillshade Multi-Directional Hillshade Elevation Tinted HillshadeSlope MapMosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 is included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks. Disclaimer: Bathymetry data sources are not to be used for navigation/safety at sea.

  10. Windows and Doors Extraction

    • hub.arcgis.com
    • sdiinnovation-geoplatform.hub.arcgis.com
    Updated Nov 10, 2020
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    Esri (2020). Windows and Doors Extraction [Dataset]. https://hub.arcgis.com/content/8c0078cc7e314e31b20001d94daace5e
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    Dataset updated
    Nov 10, 2020
    Dataset authored and provided by
    Esrihttp://esri.com/
    Description

    This deep learning model is used for extracting windows and doors in textured building data displayed in 3D views. Manually digitizing windows/doors from 3D building data can be a slow process. This model automates the extraction of these objects from a 3D view and can help in speeding up 3D editing and analysis workflows. Using this model, existing building data can be enhanced with additional information on location, size and orientation of windows and doors. The extracted windows and doors can be further used to perform 3D visibility analysis using existing 3D geoprocessing tools in ArcGIS.This model can be useful in many industries and workflows. National Government and state-level law enforcement could use this model in security analysis scenarios. Local governments could use windows and door locations to help with tax assessments with CAMA (computer aided mass appraisal) plus impact-studies for urban planning. Public safety users might be interested in regards to physical or visual access to restricted areas, or the ability to build evacuation plans. The commercial sector, with everyone from real-estate agents to advertisers to office/interior designers, would be able to benefit from knowing where windows and doors are located. Even utilities, especially mobile phone providers, could take advantage of knowing window sizes and positions. To be clear, this model doesn't solve these problems, but it does allow users to extract and collate some of the data they will need to do it.Using the modelThis model is generic and is expected to work well with a variety of building styles and shapes. To use this model, you need to install supported deep learning frameworks packages first. See Install deep learning frameworks for ArcGIS for more information. The model can be used with the Interactive Object Detection tool.A blog on the ArcGIS Pro tool that leverages this model is published on Esri Blogs. We've also published steps on how to retrain this model further using your data.InputThe model is expected to work with any textured building data displayed in 3D views. Example data sources include textured multipatches, 3D object scene layers, and integrated mesh layers. OutputFeature class with polygons representing the detected windows and doors in the input imagery. Model architectureThe model uses the FasterRCNN model architecture implemented using ArcGIS API for Python.Training dataThis model was trained using images from the Open Images Dataset.Sample resultsBelow, are sample results of the windows detected with this model in ArcGIS Pro using the Interactive Object Detection tool, which outputs the detected objects as a symbolized point feature class with size and orientation attributes.

  11. Terrain

    • opendata.rcmrd.org
    • data.catchmentbasedapproach.org
    • +4more
    Updated Jul 5, 2013
    + more versions
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    Esri (2013). Terrain [Dataset]. https://opendata.rcmrd.org/datasets/58a541efc59545e6b7137f961d7de883
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    Dataset updated
    Jul 5, 2013
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

    Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

  12. a

    HC Dashboards - Equity - Food template - food desert and health

    • chi-phi-nmcdc.opendata.arcgis.com
    Updated Oct 21, 2021
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    New Mexico Community Data Collaborative (2021). HC Dashboards - Equity - Food template - food desert and health [Dataset]. https://chi-phi-nmcdc.opendata.arcgis.com/items/22e8ee4cbea743c99765b26dde4aaa49
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    Dataset updated
    Oct 21, 2021
    Dataset authored and provided by
    New Mexico Community Data Collaborative
    Area covered
    Description

    Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhoodHow do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store.Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access.As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying.The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access.Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.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. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.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.MethodologyEvery 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

  13. a

    NLW_v3_Divisions

    • hub.arcgis.com
    Updated Jun 10, 2025
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    Living Atlas – Landscape Content (2025). NLW_v3_Divisions [Dataset]. https://hub.arcgis.com/datasets/LandscapeTeam::named-landforms-of-the-world-v3-all-layers?layer=1&uiVersion=content-views
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    Dataset updated
    Jun 10, 2025
    Dataset authored and provided by
    Living Atlas – Landscape Content
    License

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

    Area covered
    Description

    Version 3 of the Named Landforms of the World (NLWv3) is an update of version 2 of the Named Landforms of the World (NLWv2). NLWv2 will remain available as the compilation that best matches the work of E.M. Bridges and Richard E. Murphy. In NLWv3, we added attributes that describe each landform's volcanism based on data from the Smithsonian Institution's Global Volcanism Program (GVP). We designed NLWv3 layers for two purposes:Label maps with broadly accepted names for physiographic features. Use the polygons as a basis to add fields (attributes) to observation data or other small features to facilitate rich and relevant descriptions that indicate how other features relate to named physiographic features. Three workflows are recommended: (1) For point features, Identity and then Join Field; (2) Zonal Statistics as Table and then Join Field, and when many such attributes are being produced, (3) when adding multiple different attributes, the recently added Zonal Characterization tool and then Join Field. While we gained ability to estimate the area of Earth"s volcanic landforms, we also learned that volcanoes are relatively short-lived as landforms. The GVP provided two inventories, one for the Holocene Epoch, which is the most recent 11,700 years (since the last ice age), and for the Pleistocene Epoch, which precedes the Holocene, and lasted about 2.6 million years. There were only 7.8% more volcanoes included for the Pleistocene, even though the Pleistocene is 222 times longer. That means most older volcanoes have disappeared through natural erosional and depositional processes. In the NLWv3, we consider volcanic landforms as being one of many types of landforms, including calderas, clusters and complexes, shields, stratovolcanoes, or minor volcanic features such as lava domes and fissure vents. Not all of the GVP features, particularly fissure vents and remnants of calderas, are large enough to be mapped as polygons in the NLWv3. Similarly, complexes and volcanic fields typically had greater areas and included many individual cinder cones and calderas. ContinentCount of Volcanic LandformsArea km2 of Volcanic Landforms (% of land area)Europe7822,888 (0.23%)Antarctica4234,035 (0.27%)Australia14757,422 (0.65%)South America37081,475 (0.46%)Small Volcanic Islands559124,310 (8.52%)Africa282147,116 (0.50%)Asia698227,486 (0.53%)North America622295,340 (1.23%)Global Totals2,7981,000,073 (0.67%) Overview of UpdatesCorresponding landform polygons were assigned attributes for the GVP"s ID, name, province, and region. See details in the volcanic attributes section below. Additionally, an describing volcanism for each GVP feature was derived from these and several other GVP attributes to provide a reader-friendly characterization of each feature.Landforms of Antarctica. Given recent analysis of Antarctica and the GVP data it became possible to provide rudimentary landform features for Antarctica. See details in the Antarctica section below.Refined the definition of Murphy"s Isolated Volcanics classification. If the volcanic landform occurred outside of a orogenic, rifting, or subducting zone, it could not be considered isolated, as this is where volcanos are expected to occur. Only volcanoes occurring in areas with no tectonic activity are considered Isolated Volcanics, and these typically occur in mid-continent or mid-tectonic plate. See details in the Isolated Volcanic Areas section below.Edits to tectonic process attributes in selected areas. The Global Volcanism Program point locations for volcanoes includes an attribute for the underlying tectonic process. The concept matched the existing tectonic process in the NLWv2 and we compared the values. When the values differed, we reviewed research and made changes. See details in the Tectonic Process section below.Minor boundary changes at the province and lower levels in the western mountains of North and South America. See details in the Boundary Change Locations section below.Technical CharacteristicsThe NLWv2 and NLWv3 are derived the same raster datasets used to produce the 2018 version of the World Terrestrial Ecosystems (WTEs), which when combined have a lowest-common-denominator resolution, a.k.a. minimum-mapping-unit of 1-km. This means that some features, such as small islands are not included and complex coastlines are simplified and only included as land if the 1-km cell contains at least 50% land. Because the coastlines included in the original datasets varied by as much as 3-km from the actual coastline, nearly always due to missing land, we manually corrected many of the worst cases in NLWv2 using the 12 to 30-meter resolution World Hillshade layer as a guide. In NLWv3, we continued this work by adding 247 volcanic islands, some of which were smaller than 1-km in area. We estimate these islands to have been about one percent of the smaller islands of the world. In NLWv3, we also refined the coastlines of volcanic coastal areas, particularly in Oceania and Japan. For NLWv4, we plan to continue this refinement work intending that future versions of NLW will have a progressively refined, medium resolution coastline, though we do not intend to capture the full detail of the Global Islands dataset produced from 30-m Landsat. Detailed Description of Updates Volcanic AttributesWe combined the Holocene and Pleistocene spreadsheets containing the coordinates and attributes for each volcano, then added a column for the geologic age before exporting as a .CSV file and importing into ArcGIS Pro. We used the XY Table to Points tool to create point features. We ultimately found that nearly ten percent of the point locations lacked sufficient precision to fall within the correct landform polygon, so we manually reviewed each point and assigned the Volcano ID to each polygon.We were able to assign 2,394 of 2,662 GVP volcanic features to landform polygons. 198 GVP features were not used because they represented undersea features and 75 GVP features did not have apparent landforms; either being very small or indistinguishable from surrounding topography. Of the 2,394 assigned GVP features, 48% are Holocene age features and 52% are Pleistocene age features. We found that 225 GVP features were not located within a landform feature that topographically represented a volcanic landform feature, e.g., a caldera or stratovolcano. This was usually due to insufficient precision of the coordinates provided, which sometimes were rounded to the nearest integer of latitude and longitude and could be over 50-km distant from the landform"s location. AttributeDescriptionVolcano ID (SI)The six-digit unique ID for the Global Volcanism Program features.Volcano Name (SI)The Name of the volcanic feature as provided by the Global Volcanism Program. Volcanic Region (SI)The Name of the volcanic region as provided by the Global Volcanism Program. Volcanic Province (SI)The Name of the volcanic province as provided by the Global Volcanism Program. VolcanismA consistently formatted description volcanism for the landform feature based on the age, last eruption, landform type, and type of material. This information was not consistently available from the Global Volcanism Program, and we used a Python script to determine the condition of the Global Volcanism Program"s data and then include whatever information was available. AntarcticaSeveral recent analyses of Antarctica complemented the GVP point features. In particular, the British Antarctic Survey"s 2019 Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet shows sufficiently detailed land surface elevation beneath the ice sheets to support identifying topographic landform classes. We georeferenced the elevation image and combined that with Bridge"s geomorphological divisions and provinces to divide the continent into landforms. More work needs to be done to make these landform polygons as rich and accurately defined as those in NLWv2. Isolated Volcanic AreasNLWv2 has 333 Isolated Volcanic landforms. We intentionally expanded on Murphy"s map which could not show many of the smaller landforms and areas due to the 1:50,000,000 scale (poster sized map of the world). Murphy"s map only included isolated volcanic areas in three locations: north-central Africa, Hawaii, and Iceland. In NLWv2, we used the Global Lithological Map to identify several areas on each continent and used the example of Hawaii to include many other known volcanic islands. In most ways, Isolated Volcanics denoted geographic isolation from other mountain systems. NLWv3 contains 2,798 volcanic landform features, and 185 have Murphy"s Isolated Volcanic structure class because they do not occur within a region with the tectonic process of orogenic, subduction, or rifting. These Isolated Volcanic landform features are located mostly in mid-tectonic plate regions of Africa, the Arabian Peninsula, and on islands, particularly in the southern hemisphere, with a few in North America and Asia. NLWv3 contains 2,603 volcanic landform features, occurring on all continents and islands within all oceans. Tectonic ProcessThe GVP data included a tectonic setting attribute that was compiled independently of the NLWv2 tectonic setting variable. When these differed, we reviewed and if needed update the tectonic setting variable in the NLWv3. This also exposed several regions of landforms requiring updates to the Structure class. These areas included Japan, northeast Asia, the Aleutian Islands, and Alaska to either Orogenic or None. We independently verified these regions using Orogeny and Mantle Dynamics: role of tectonic erosion and second continent in the mantle transition zone which indicated specific orogenic and subducting areas, disagreeing with our original assessment and the GVP attribution for tectonic setting. Tectonic ProcessHolocene Volcanic Features Pleistocene Volcanic FeaturesNone (Isolated)7797Orogenic329497Subduction

  14. Grocery Access in the U.S. and Puerto Rico

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • supply-chain-data-hub-nmcdc.hub.arcgis.com
    Updated Feb 25, 2021
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    Urban Observatory by Esri (2021). Grocery Access in the U.S. and Puerto Rico [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/maps/5ed03e000eae4540b07c8ac4a1bc501d
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    Dataset updated
    Feb 25, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhood How do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app. Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state). On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access. As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car? How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access. Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. 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. MethodologyEvery 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

  15. a

    Named Landforms of the World v3 (All Layers)

    • hub.arcgis.com
    Updated Jun 11, 2025
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    Living Atlas – Landscape Content (2025). Named Landforms of the World v3 (All Layers) [Dataset]. https://hub.arcgis.com/datasets/882f24d8ea3244eaab0bc0050937374a
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    Dataset updated
    Jun 11, 2025
    Dataset authored and provided by
    Living Atlas – Landscape Content
    License

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

    Area covered
    Earth, World,
    Description

    Version 3 of the Named Landforms of the World (NLWv3) is an update of version 2 of the Named Landforms of the World (NLWv2). NLWv2 will remain available as the compilation that best matches the work of E.M. Bridges and Richard E. Murphy. In NLWv3, we added attributes that describe each landform's volcanism based on data from the Smithsonian Institution's Global Volcanism Program (GVP). We designed NLWv3 layers for two purposes:To label maps with broadly accepted names for physiographic features. To add landform attributes to other layers. For example, species observation data or other small features to enable rich and relevant descriptions for how those features relate to landforms. To accomplish this, typically, we use overlay tools such as Identity. For background, version 2 provided features with the physiographic and geomorphologic characteristics for the world's named landforms. This means it was more than just showing the land versus water or mountains versus plains; it also included the underlying structure and processes that created the landforms. We begin with the largest landform regions, which are continents, followed by tectonic plates, then divisions, provinces, sections, and finally, individual landforms. In adding the GVP volcanic landforms to NLWv3, we learned that volcanoes are relatively short-lived as landforms, with most not enduring for two million years. For context, the age of the rocks in most of the Earth's mountain ranges is in the tens to hundreds of millions of years. The full collection of layers and maps for NLWv3 are available in an ArcGIS Online Group named Named Landforms Of the World v3 (NLWv3) Layers and Maps. The GVP included two inventories--one for the Holocene Epoch, which are the volcanoes that formed during most recent 11,700 years (since the last ice age). The other is for the Pleistocene Epoch, which precedes the Holocene, and lasted about 2.6 million years. While the Pleistocene epoch is 222 times longer than the Holocene, it only has 7.8% more volcanoes. Most of the volcanoes that formed during the Pleistocene have disappeared through natural erosional and depositional processes. In NLWv3, volcanic landforms include calderas, clusters and complexes, shields, stratovolcanoes, and minor volcanic features such as cinder cones, lava domes, and fissure vents. Not all the GVP features, particularly fissure vents and remnants of calderas, are large enough to be mapped as polygons in NLWv3. Similarly, complexes and volcanic fields typically had greater areas and included many individual cinder cones and calderas. ContinentCount of Volcanic LandformsArea km2 of Volcanic Landforms (% of land area)Europe7822,888 (0.23%)Antarctica4234,035 (0.27%)Australia14757,422 (0.65%)South America37081,475 (0.46%)Small Volcanic Islands559124,310 (8.52%)Africa282147,116 (0.50%)Asia698227,486 (0.53%)North America622295,340 (1.23%)Global Totals2,7981,000,073 (0.67%)This table shows the distribution of volcanic landforms and their surface areas. Overview of UpdatesCorresponding landform polygons now include attributes for the GVP's ID, name, province, and region. Details are provided below in the volcanic attributes section. Additionally, a text description of volcanism for each GVP feature was derived from these attributes to provide a reader-friendly characterization of each volcanic landform.Landforms of Antarctica. Given recent analysis of Antarctica and the use of GVP data, rudimentary landform features for Antarctica have been added. See details in the Antarctica section below.Refined the definition of Murphy's Isolated Volcanics classification. If the volcanic landform occurred outside of an orogenic, rifting, or subducting zone, only then did we consider it isolated. The areas along tectonic plate boundaries are where volcanoes typically occur. Only volcanoes occurring in areas with no tectonic activity are considered isolated. These typically occur in mid-continent or mid-tectonic plate. See details in the Isolated Volcanic Areas section.Edits to tectonic process attributes in selected areas. The GVP point locations for volcanoes include an attribute for the underlying tectonic process. The concept matched the existing tectonic process in the NLWv2, and we compared the values. When the values differed, we reviewed research and made changes. See details in the Tectonic Process section below.Minor boundary changes at the province, section, and landform level in the western mountains of North and South America. Details are provided below in the Boundary Change Locations section. Technical CharacteristicsThe NLWv2 and NLWv3 are derived from the same raster datasets used to produce the 2018 version of the World Terrestrial Ecosystems (WTEs), which, when combined, have a lowest-common-denominator resolution (minimum mapping unit) of 1 km. Some features, such as very small islands, were not included in NLWv3, and complex coastlines were simplified and were only included if the 1-km cell contained at least 50% land. Because the coastlines in the raster datasets varied by as much as 3 km from the actual coastline, nearly always due to missing land. Many of the worst such cases in NLWv2 were manually corrected using the 12-30-meter resolution World Hillshade layer as a guide. In NLWv3, we continued this work by adding 247 volcanic islands, some of which were smaller than 1 km in area. We estimate that these islands comprise about one percent of the world's smaller islands. In NLWv3, we also refined the coastlines of volcanic coastal areas, particularly in Oceania and Japan. For NLWv4, we plan to continue this refinement work, intending that future versions of NLW will have a progressively refined, medium-resolution coastline. However, we do not intend to capture the full detail of the Global Islands dataset, which was produced from 30-m Landsat data. Detailed Description of Updates Volcanic AttributesThe GVP Excel spreadsheets for the Holocene and Pleistocene epochs, which contained the coordinates and attributes for each volcano, were combined. A column for the geologic age was added before saving the spreadsheet as a .CSV file and importing into ArcGIS Pro. The XY Table to Points tool was used to create point features. Nearly ten percent of the point locations that lacked sufficient precision to fall within the correct landform polygon were revised manually in order to assign the correct Volcano ID to each polygon.2,394 of the 2,662 GVP volcanic features were assigned to landform polygons. 198 GVP features were not assigned because they represented undersea features, and 75 GVP features did not have apparent corresponding landform polygons because they were either too small or indistinguishable from surrounding topography. Of the 2,394 assigned GVP features, 48% are Holocene Age features and 52% are Pleistocene epoch features. 225 GVP features did not fall within within a landform feature that represented topographically a volcanic landform feature, such as a caldera or stratovolcano. This was usually due to insufficient precision of the GVP coordinates, which sometimes were rounded to the nearest integer of latitude and longitude and could therefore be over 50km away from the landform's location. AttributeDescriptionVolcano ID (SI)The six-digit unique ID for the Global Volcanism Program features.Volcano Name (SI)The Name of the volcanic feature as provided by the Global Volcanism Program. Volcanic Region (SI)The Name of the volcanic region as provided by the Global Volcanism Program. Volcanic Province (SI)The Name of the volcanic province as provided by the Global Volcanism Program. VolcanismA consistently formatted description volcanism for the landform feature based on the age, last eruption, landform type, and type of material. This information was not consistently available from the Global Volcanism Program, and we used a Python script to determine the condition of the Global Volcanism Program"s data and then include whatever information was available. AntarcticaSeveral recent analyses of Antarctica complemented the GVP point features. In particular, the British Antarctic Survey's 2019 Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet show sufficiently detailed land surface elevation beneath the ice sheets to support identifying topographic landform classes. We georeferenced the elevation image and combined it with Bridge's geomorphological divisions and provinces to divide the continent into different landform polygons. Additional work is needed to make these landform polygons as rich and accurately defined as those in NLWv2. Isolated Volcanic AreasThere are 333 Isolated Volcanic landforms in NLWv2. We intentionally expanded on Murphy"s map which could not show many of the smaller landforms and areas due to the 1:50,000,000 scale (poster sized map of the world). Murphy"s map only included isolated volcanic areas in three locations: north-central Africa, Hawaii, and Iceland. In NLWv2, we used the Global Lithological Map to identify several areas on each continent and used the example of Hawaii to include many other known volcanic islands. In most ways, Isolated Volcanics denoted geographic isolation from other mountain systems. NLWv3 includes 2,798 volcanic landform features, and 185 have been assigned Murphy's Isolated Volcanic structure class because they do not occur within a region with the tectonic process of orogenic, subduction, or rifting. These Isolated Volcanic landform features are located mostly in mid-tectonic plate regions of Africa, the Arabian Peninsula, and on islands, particularly in the southern hemisphere, with a few in North America and Asia. NLWv3 contains 2,603 volcanic landform features, occurring on all continents and on islands within all oceans. Tectonic ProcessThe GVP data included a tectonic setting attribute that was compiled independently of the NLWv2 tectonic setting variable. When these

  16. Landsat Arctic Views

    • hub.arcgis.com
    • open-data-pittsylvania.hub.arcgis.com
    • +2more
    Updated Jun 24, 2016
    + more versions
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    Esri (2016). Landsat Arctic Views [Dataset]. https://hub.arcgis.com/datasets/6334dd0f09f04a3583a37233540d73c0
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    Dataset updated
    Jun 24, 2016
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This dynamic imagery layer features Landsat 8 and Landsat GLS imagery for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created. Available functions on this layer include:Agriculture with DRA – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with dynamic range adjustment applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.NDSI Colorized – Normalized difference Snow index (NDSI) with color map, computed as (b3-b6)/(b3+b6) on apparent reflectance. Dark blue represents dense snow, yellow and green areas represent clouds.Bathymetric with DRA – Bands red, green, coastal/aerosol (4, 3, 1) with dynamic range adjustment. Useful in bathymetric mapping applications.Color Infrared with DRA – Bands near-IR, red, green (5, 4, 3) with dynamic range adjustment. Healthy vegetation is bright red while stressed vegetation is dull red.Geology with DRA – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with dynamic range adjustment. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color with DRA – Natural Color bands red, green, blue (4, 3, 2) displayed with dynamic range adjustmentShort-wave Infrared with DRA – Bands shortwave IR-2, shortwave IR-1, red (7, 6, 4) with dynamic range adjustmentAgriculture – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with fixed stretch applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Bathymetry – Bands red, green, coastal/aerosol (4, 3, 1) with fixed stretch applied on apparent reflectance. Useful in bathymetric mapping applications.Color Infrared – Bands near-IR, red, green (5, 4, 3) with a fixed stretch. Healthy vegetation is bright red while stressed vegetation is dull red.Geology – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with a fixed stretch. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color – Natural Color bands red, green, blue (4, 3, 2) displayed with a fixed stretch.Short-wave Infrared – Bands shortwave IR-2, shortwave IR-1, red (7, 5, 4) with a fixed stretchNormalized Difference Moisture Index Colorized – Normalized Difference Moisture Index with color map, computed as (b5 - b6)/(b5 + b6). Wetlands and moist areas are blues, and dry areas in deep yellow and brownNDSI Raw – Normalized difference Snow index (NDSI) computed as (b3 - b6) / (b3 + b6)NDVI Raw – Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4)NBR Raw – Normalized Burn Ratio (NBR) computed as (b5 - b7) / (b5 + b7)Multispectral BandsThe table below lists all available multispectral OLI bands. Natural Color with DRA consumes bands 4,3,2

    Band

    Description

    Wavelength (µm)

    Spatial Resolution (m)

    1

    Coastal aerosol

    0.43 - 0.45

    30

    2

    Blue

    0.45 - 0.51

    30

    3

    Green

    0.53 - 0.59

    30

    4

    Red

    0.64 - 0.67

    30

    5

    Near Infrared (NIR)

    0.85 - 0.88

    30

    6

    SWIR 1

    1.57 - 1.65

    30

    7

    SWIR 2

    2.11 - 2.29

    30

    8

    Cirrus (in OLI this is band 9)

    1.36 - 1.38

    30

    9

    QA Band (available with Collection 1)*

    NA

    30

    *More about the Quality Assessment Band The layer also provides access to TIRS bands as follows: BandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

  17. Landsat Arctic Imagery: Short-wave Infrared with DRA

    • hub.arcgis.com
    • open-data-pittsylvania.hub.arcgis.com
    Updated Jun 23, 2016
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    Esri (2016). Landsat Arctic Imagery: Short-wave Infrared with DRA [Dataset]. https://hub.arcgis.com/datasets/fa5e07d865284f41ac5b548e7cec45b6
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    Dataset updated
    Jun 23, 2016
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This dynamic imagery layer features Landsat 8 and Landsat GLS imagery, rendered on-the-fly as Short-wave Infrared with DRA, for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.

    Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Short-wave Infrared (bands 7,6,4) with Dynamic Range Adjustment (DRA).Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Multispectral BandsThe table below lists all available multispectral OLI bands. Short-wave Infrared with DRA consumes bands 7,6,4.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic app is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.

    *The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

  18. 10 Minute Walk Access to Grocery Stores 2020

    • hub.arcgis.com
    Updated May 4, 2021
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    Urban Observatory by Esri (2021). 10 Minute Walk Access to Grocery Stores 2020 [Dataset]. https://hub.arcgis.com/maps/UrbanObservatory::safegraph-grocery-stores/about
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    Dataset updated
    May 4, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    This layer shows which parts of the United States and Puerto Rico fall within ten minutes" walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale. When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes" walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don"t own a car? How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards. The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population"s grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. 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

  19. Landsat Arctic Imagery: Normalized Difference Moisture Index Colorized

    • hub.arcgis.com
    • open-data-pittsylvania.hub.arcgis.com
    Updated Jun 24, 2016
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    Esri (2016). Landsat Arctic Imagery: Normalized Difference Moisture Index Colorized [Dataset]. https://hub.arcgis.com/datasets/3ea75c75105641ea91203280b57e9521
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    Dataset updated
    Jun 24, 2016
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This dynamic imagery layer features Landsat 8 and Landsat GLS imagery, rendered on-the-fly as Normalized Difference Moisture Index Colorized, for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.

    To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.

    Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Normalized Difference Moisture Index Colorized, calculated as (b5 - b6)/(b5 + b6) with a colormap applied. Wetlands and moist areas are blues, and dry areas in deep yellow and brown.Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Multispectral BandsThe table below lists all available multispectral OLI bands. Normalized Difference Moisture Index consumes band 5 and 6.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic app is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.

    *The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

  20. a

    2023 Council District Boundaries

    • egisdata-dallasgis.hub.arcgis.com
    Updated May 6, 2023
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    City of Dallas GIS Services (2023). 2023 Council District Boundaries [Dataset]. https://egisdata-dallasgis.hub.arcgis.com/datasets/1007324b43144989b2a0d24c96c9f4bd
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    Dataset updated
    May 6, 2023
    Dataset authored and provided by
    City of Dallas GIS Services
    Area covered
    Description

    Data Use: An enriched layer of City Council Districts (as of 2023) with metrics for supporting urban agriculture such as need (e.g, the SVI and dietary health), community assets (e.g., places of faith, meal assistance programs, public housing), land opportunities (e.g., vacant parcels), and existing urban agriculture projects. These metrics were aggregated from other features within the City Council Districts such as tract centroids and point locations. The pop-up for this layer provide a broad overview for urban agriculture potential within a District. The attribute table can be used to build District-level charts and tables. The layer was first downloaded by the provider from its source and enriched with spatial joins in ArcGISPro before being uploaded back into the AGOL environment. The layer is designed as a contextual layer in the webmap, COD Social Health UA. Data source: City of Dallas GIS Services, Current Council Districts - Polygon, COD_DistrictBoundariesFullDataYear: 2023Provider: FHEED, LLCRelated: COD Social Health UA, ID: ed08af809e9f4a968713fcb0e8cf8750

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Rosa Orlandini (2019). Residential Schools Locations Dataset (Geodatabase) [Dataset]. http://doi.org/10.5683/SP2/JFQ1SZ

Residential Schools Locations Dataset (Geodatabase)

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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
May 31, 2019
Dataset provided by
Borealis
Authors
Rosa Orlandini
License

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

Time period covered
Jan 1, 1863 - Jun 30, 1998
Area covered
Canada
Description

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

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