100+ datasets found
  1. Geospatial data for the Vegetation Mapping Inventory Project of Crater Lake...

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Jun 4, 2024
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    National Park Service (2024). Geospatial data for the Vegetation Mapping Inventory Project of Crater Lake National Park [Dataset]. https://catalog.data.gov/dataset/geospatial-data-for-the-vegetation-mapping-inventory-project-of-crater-lake-national-park
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    Dataset updated
    Jun 4, 2024
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Area covered
    Crater Lake
    Description

    The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

  2. d

    Geospatial Data | Global Map data | Postal/Zip code boundaries | Polygon...

    • datarade.ai
    .json, .xml
    Updated Dec 15, 2024
    + more versions
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    GeoPostcodes (2024). Geospatial Data | Global Map data | Postal/Zip code boundaries | Polygon data [Dataset]. https://datarade.ai/data-products/geopostcodes-boundary-data-global-coverage-880k-polygons-geopostcodes
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    .json, .xmlAvailable download formats
    Dataset updated
    Dec 15, 2024
    Dataset authored and provided by
    GeoPostcodes
    Area covered
    Comoros, Macedonia (the former Yugoslav Republic of), Togo, Åland Islands, Argentina, San Marino, Colombia, Saudi Arabia, Senegal, Egypt
    Description

    Overview

    Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

    Our self-hosted geospatial data cover postal divisions for the whole world. The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

    Use cases for the Global Boundaries Database (Geospatial data, Map data, Polygon daa)

    • In-depth spatial analysis

    • Clustering

    • Geofencing

    • Reverse Geocoding

    • Reporting and Business Intelligence (BI)

    Product Features

    • Coherence and precision at every level

    • Edge-matched polygons

    • High-precision shapes for spatial analysis

    • Fast-loading polygons for reporting and BI

    • Multi-language support

    For additional insights, you can combine the map data with:

    • Population data: Historical and future trends

    • UNLOCODE and IATA codes

    • Time zones and Daylight Saving Time (DST)

    Data export methodology

    Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

    All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

    Why companies choose our map data

    • Precision at every level

    • Coverage of difficult geographies

    • No gaps, nor overlaps

    Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

  3. G

    Geospatial Analytics Market Report

    • marketresearchforecast.com
    doc, pdf, ppt
    Updated Jan 10, 2025
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    Market Research Forecast (2025). Geospatial Analytics Market Report [Dataset]. https://www.marketresearchforecast.com/reports/geospatial-analytics-market-1650
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    doc, ppt, pdfAvailable download formats
    Dataset updated
    Jan 10, 2025
    Dataset authored and provided by
    Market Research Forecast
    License

    https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Geospatial Analytics Market size was valued at USD 79.06 USD billion in 2023 and is projected to reach USD 202.74 USD billion by 2032, exhibiting a CAGR of 14.4 % during the forecast period. The growing adoption of location-based technologies and the increasing need for data-driven decision-making in various industries are key factors driving market growth. Geospatial analytics captures, produces and displays GIS (geographic information system)-maps and pictures that may be weather maps, GPS or satellite photos. The geospatial analysis as a tool works with state of art technology in every formats namely; the GPS, sensors that locates, social media, mobile devices, multi of the satellite imagery to produce data visualizations that are facilitating trend-finding in complex relations between people and places as well are the situations' understanding. Visualizations are depicted through the use of maps, graphs, figures, and cartograms that illustrate the entire historical picture as well as a current changing trend. This is why the forecast becomes more confident and the situation is anticipated better. Recent developments include: February 2024: Placer.ai and Esri, a Geographic Information System (GIS) technology provider, partnered to empower customers with enhanced analytics capabilities, integrating consumer behavior analysis. Additionally, the agreement will foster collaborations to unlock further features by synergizing our respective product offerings., December 2023: CKS and Esri India Technologies Pvt Ltd teamed up to introduce the 'MMGEIS' program, focusing on students from 8th grade to undergraduates, to position India as a global leader in geospatial technology through skill development and innovation., December 2023: In collaboration with Bayanat, the UAE Space Agency revealed the initiation of the operational phase of the Geospatial Analytics Platform during its participation in organizing the Space at COP28 initiatives., November 2023: USAID unveiled its inaugural Geospatial Strategy, designed to harness geospatial data and technology for more targeted international program delivery. The strategy foresees a future where geographic methods enhance the effectiveness of USAID's efforts by pinpointing development needs, monitoring program implementation, and evaluating outcomes based on location., May 2023: TomTom International BV, a geolocation technology specialist, expanded its partnership with Alteryx, Inc. Through this partnership, Alteryx will use TomTom’s Maps APIs and location data to integrate spatial data into Alteryx’s products and location insights packages, such as Alteryx Designer., May 2023: Oracle Corporation announced the launch of Oracle Spatial Studio 23.1, available in the Oracle Cloud Infrastructure (OCI) marketplace and for on-premises deployment. Users can browse, explore, and analyze geographic data stored in and managed by Oracle using a no-code mapping tool., May 2023: CAPE Analytics, a property intelligence company, announced an enhanced insurance offering by leveraging Google geospatial data. Google’s geospatial data can help CAPE create appropriate solutions for insurance carriers., February 2023: HERE Global B.V. announced a collaboration with Cognizant, an information technology, services, and consulting company, to offer digital customer experience using location data. In this partnership, Cognizant will utilize the HERE location platform’s real-time traffic data, weather, and road attribute data to develop spatial intelligent solutions for its customers., July 2022: Athenium Analytics, a climate risk analytics company, launched a comprehensive tornado data set on the Esri ArcGIS Marketplace. This offering, which included the last 25 years of tornado insights from Athenium Analytics, would extend its Bronze partner relationship with Esri. . Key drivers for this market are: Advancements in Technologies to Fuel Market Growth. Potential restraints include: Lack of Standardization Coupled with Shortage of Skilled Workforce to Limit Market Growth. Notable trends are: Rise of Web-based GIS Platforms Will Transform Market.

  4. ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating...

    • zenodo.org
    • data.niaid.nih.gov
    bin, zip
    Updated Jul 25, 2024
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    Andrew Gillreath-Brown; Andrew Gillreath-Brown; Lisa Nagaoka; Lisa Nagaoka; Steve Wolverton; Steve Wolverton (2024). ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019) [Dataset]. http://doi.org/10.5281/zenodo.2572018
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    bin, zipAvailable download formats
    Dataset updated
    Jul 25, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Andrew Gillreath-Brown; Andrew Gillreath-Brown; Lisa Nagaoka; Lisa Nagaoka; Steve Wolverton; Steve Wolverton
    License

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

    Description

    ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

    **When using the GIS data included in these map packages, please cite all of the following:

    Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

    Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

    OVERVIEW OF CONTENTS

    This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

    • Raw DEM and Soils data
      • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
        • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
        • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
      • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
        • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
        • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
    • ArcGIS Map Packages
      • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
      • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
      • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
      • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

    For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

    LICENSES

    Code: MIT year: 2019
    Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

    CONTACT

    Andrew Gillreath-Brown, PhD Candidate, RPA
    Department of Anthropology, Washington State University
    andrew.brown1234@gmail.com – Email
    andrewgillreathbrown.wordpress.com – Web

  5. Geospatial data for the Vegetation Mapping Inventory Project of Fort Larned...

    • catalog.data.gov
    Updated Jun 5, 2024
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    National Park Service (2024). Geospatial data for the Vegetation Mapping Inventory Project of Fort Larned National Historic Site [Dataset]. https://catalog.data.gov/dataset/geospatial-data-for-the-vegetation-mapping-inventory-project-of-fort-larned-national-histo
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    Dataset updated
    Jun 5, 2024
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Area covered
    Larned
    Description

    The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

  6. Indian Geospatial Dataset

    • kaggle.com
    Updated Jun 8, 2024
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    RITIK SHARMA (2024). Indian Geospatial Dataset [Dataset]. https://www.kaggle.com/datasets/ritiksharma07/indian-gis
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 8, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    RITIK SHARMA
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset contains comprehensive geospatial data detailing the geographical features and boundaries of India. It includes information on various geographic elements such as terrain, water bodies, administrative boundaries, and infrastructure, providing valuable insights for spatial analysis and mapping projects.

  7. d

    Global Geospatial & GIS Data | 230M+ POIs with Location Coordinates, Mapping...

    • datarade.ai
    .json
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    Xverum, Global Geospatial & GIS Data | 230M+ POIs with Location Coordinates, Mapping Metadata & 5000 Categories [Dataset]. https://datarade.ai/data-products/xverum-geospatial-data-100-verified-locations-230m-poi-xverum
    Explore at:
    .jsonAvailable download formats
    Dataset authored and provided by
    Xverum
    Area covered
    United States
    Description

    Xverum’s Global GIS & Geospatial Data is a high-precision dataset featuring 230M+ verified points of interest across 249 countries. With rich metadata, structured geographic attributes, and continuous updates, our dataset empowers businesses, researchers, and governments to extract location intelligence and conduct advanced geospatial analysis.

    Perfectly suited for GIS systems, mapping tools, and location intelligence platforms, this dataset covers everything from businesses and landmarks to public infrastructure, all classified into over 5000 categories. Whether you're planning urban developments, analyzing territories, or building location-based products, our data delivers unmatched coverage and accuracy.

    Key Features: ✅ 230M+ Global POIs Includes commercial, governmental, industrial, and service locations - updated regularly for accurate relevance.

    ✅ Comprehensive Geographic Coverage Worldwide dataset covering 249 countries, with attributes including latitude, longitude, city, country code, postal code, etc.

    ✅ Detailed Mapping Metadata Get structured address data, place names, categories, and location, which are ideal for map visualization and geospatial modeling.

    ✅ Bulk Delivery for GIS Platforms Available in .json - delivered via S3 Bucket or cloud storage for easy integration into ArcGIS, QGIS, Mapbox, and similar systems.

    ✅ Continuous Discovery & Refresh New POIs added and existing ones refreshed on a regular refresh cycle, ensuring reliable, up-to-date insights.

    ✅ Compliance & Scalability 100% compliant with global data regulations and scalable for enterprise use across mapping, urban planning, and retail analytics.

    Use Cases: 📍 Location Intelligence & Market Analysis Identify high-density commercial zones, assess regional activity, and understand spatial relationships between locations.

    🏙️ Urban Planning & Smart City Development Design infrastructure, zoning plans, and accessibility strategies using accurate location-based data.

    🗺️ Mapping & Navigation Enrich digital maps with verified business listings, categories, and address-level geographic attributes.

    📊 Retail Site Selection & Expansion Analyze proximity to key POIs for smarter retail or franchise placement.

    📌 Risk & Catchment Area Assessment Evaluate location clusters for insurance, logistics, or regional outreach strategies.

    Why Xverum? ✅ Global Coverage: One of the largest POI geospatial databases on the market ✅ Location Intelligence Ready: Built for GIS platforms and spatial analysis use ✅ Continuously Updated: New POIs discovered and refreshed regularly ✅ Enterprise-Friendly: Scalable, compliant, and customizable ✅ Flexible Delivery: Structured format for smooth data onboarding

    Request a free sample and discover how Xverum’s geospatial data can power your mapping, planning, and spatial analysis projects.

  8. M

    Status of Free and Open Public Geospatial Data from Minnesota Counties

    • gisdata.mn.gov
    • data.wu.ac.at
    fgdb, gpkg, html +3
    Updated Apr 24, 2025
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    Geospatial Information Office (2025). Status of Free and Open Public Geospatial Data from Minnesota Counties [Dataset]. https://gisdata.mn.gov/dataset/bdry-mn-county-open-data-status
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    printable_map, jpeg, fgdb, html, shp, gpkgAvailable download formats
    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Geospatial Information Office
    Area covered
    Minnesota
    Description

    This map shows the free and open data status of county public geospatial (GIS) data across Minnesota. The accompanying data set can be used to make similar maps using GIS software.

    Counties shown in this dataset as having free and open public geospatial data (with or without a policy) are: Aitkin, Anoka, Becker, Beltrami, Benton, Big Stone, Carlton, Carver, Cass, Chippewa, Chisago, Clay, Clearwater, Cook, Crow Wing, Dakota, Douglas, Grant, Hennepin, Hubbard, Isanti, Itasca, Kittson, Koochiching, Lac qui Parle, Lake, Lyon, Marshall, McLeod, Meeker, Mille Lacs, Morrison, Mower, Norman, Olmsted, Otter Tail, Pipestone, Polk, Pope, Ramsey, Renville, Rice, Scott, Sherburne, Stearns, Steele, Stevens, St. Louis, Traverse, Waseca, Washington, Wilkin, Winona, Wright and Yellow Medicine.

    To see if a county's data is distributed via the Minnesota Geospatial Commons, check the Commons organizations page: https://gisdata.mn.gov/organization

    To see if a county distributes data via its website, check the link(s) on the Minnesota County GIS Contacts webpage: https://www.mngeo.state.mn.us/county_contacts.html

  9. U

    Geospatial data for bedrock elevation and overburden thickness maps of the...

    • data.usgs.gov
    • catalog.data.gov
    Updated May 25, 2023
    + more versions
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    Laura Demott; Frederick Stumm; Michael Como; Jason Finkelstein (2023). Geospatial data for bedrock elevation and overburden thickness maps of the Five Boroughs, New York City, New York [Dataset]. http://doi.org/10.5066/P911CSI3
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    Dataset updated
    May 25, 2023
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Laura Demott; Frederick Stumm; Michael Como; Jason Finkelstein
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Time period covered
    1905 - 2021
    Area covered
    New York
    Description

    Using a combination of public and proprietary historical construction test borings, recent exploration drilling, USGS observation wells, outcrops, and seismic measurements, a series of geospatial overlays for bedrock elevation and overburden thickness were created for the Five Boroughs of New York City, New York. Rasters were interpolated from a point elevation data set and refined using published and interpretive bedrock contours, and interpreted glacial valleys and faults. Contours for bedrock elevation were generated at 100-ft contour intervals and smoothed. This data release includes shapefiles containing the input point elevation features and output contours, and rasters of interpolated bedrock elevation and overburden thickness surfaces.

  10. Geospatial Data Gateway

    • agdatacommons.nal.usda.gov
    bin
    Updated Nov 30, 2023
    + more versions
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    USDA, Natural Resources Conservation Service (NRCS); USDA, Farm Service Agency (FSA); USDA, Rural Development (RD) (2023). Geospatial Data Gateway [Dataset]. http://doi.org/10.15482/USDA.ADC/1241880
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    binAvailable download formats
    Dataset updated
    Nov 30, 2023
    Dataset provided by
    United States Department of Agriculturehttp://usda.gov/
    Natural Resources Conservation Servicehttp://www.nrcs.usda.gov/
    Authors
    USDA, Natural Resources Conservation Service (NRCS); USDA, Farm Service Agency (FSA); USDA, Rural Development (RD)
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The Geospatial Data Gateway (GDG) provides access to a map library of over 100 high resolution vector and raster layers in the Geospatial Data Warehouse. It is the one stop source for environmental and natural resource data, available anytime, from anywhere. It allows a user to choose an area of interest, browse and select data, customize the format, then download or have it shipped on media. The map layers include data on: Public Land Survey System (PLSS), Census data, demographic statistics, precipitation, temperature, disaster events, conservation easements, elevation, geographic names, geology, government units, hydrography, hydrologic units, land use and land cover, map indexes, ortho imagery, soils, topographic images, and streets and roads. This service is made available through a close partnership between the three Service Center Agencies (SCA): Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA), and Rural Development (RD). Resources in this dataset:Resource Title: Geospatial Data Gateway. File Name: Web Page, url: https://gdg.sc.egov.usda.gov This is the main page for the GDG that includes several links to view, download, or order various datasets. Find additional status maps that indicate the location of data available for each map layer in the Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGHome_StatusMaps.aspx

  11. Geographic Information System Analytics Market Analysis, Size, and Forecast...

    • technavio.com
    Updated Jul 15, 2024
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    Technavio (2024). Geographic Information System Analytics Market Analysis, Size, and Forecast 2024-2028: North America (US and Canada), Europe (France, Germany, UK), APAC (China, India, South Korea), Middle East and Africa , and South America [Dataset]. https://www.technavio.com/report/geographic-information-system-analytics-market-industry-analysis
    Explore at:
    Dataset updated
    Jul 15, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global, France, United States, Germany, Canada, United Kingdom
    Description

    Snapshot img

    Geographic Information System Analytics Market Size 2024-2028

    The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

    The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
    Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.
    

    What will be the Size of the GIS Analytics Market during the forecast period?

    Request Free Sample

    The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
    GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.
    

    How is this Geographic Information System Analytics Industry segmented?

    The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

    End-user
    
      Retail and Real Estate
      Government
      Utilities
      Telecom
      Manufacturing and Automotive
      Agriculture
      Construction
      Mining
      Transportation
      Healthcare
      Defense and Intelligence
      Energy
      Education and Research
      BFSI
    
    
    Components
    
      Software
      Services
    
    
    Deployment Modes
    
      On-Premises
      Cloud-Based
    
    
    Applications
    
      Urban and Regional Planning
      Disaster Management
      Environmental Monitoring Asset Management
      Surveying and Mapping
      Location-Based Services
      Geospatial Business Intelligence
      Natural Resource Management
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        UK
    
    
      APAC
    
        China
        India
        South Korea
    
    
      Middle East and Africa
    
        UAE
    
    
      South America
    
        Brazil
    
    
      Rest of World
    

    By End-user Insights

    The retail and real estate segment is estimated to witness significant growth during the forecast period.

    The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

    The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

  12. g

    USGS Watershed Boundary Dataset (WBD) Overlay Map Service from The National...

    • data.globalchange.gov
    • data.amerigeoss.org
    • +1more
    Updated Dec 31, 2009
    + more versions
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    (2009). USGS Watershed Boundary Dataset (WBD) Overlay Map Service from The National Map - National Geospatial Data Asset (NGDA) Watershed Boundary Dataset (WBD) [Dataset]. https://data.globalchange.gov/dataset/usgs-watershed-boundary-dataset-wbd-overlay-map-service-from-the-national-map-national-geospat
    Explore at:
    Dataset updated
    Dec 31, 2009
    Description

    The Watershed Boundary Dataset (WBD) from The National Map (TNM) defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Upper Mississippi River, will be composed of multiple smaller drainage areas, such as the Wisconsin River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to http://nhd.usgs.gov/wbd.html. The USGS National Hydrography Dataset (NHD) service is a companion dataset to the WBD. The NHD is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. For more information on the NHD, go to http://nhd.usgs.gov/index.html. Hydrography data from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. Hydrography data is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain WBD and NHD data in either Esri File or Personal Geodatabase, or Shapefile formats.

  13. Geospatial Analytics Market Analysis, Size, and Forecast 2025-2029: North...

    • technavio.com
    Updated Apr 15, 2025
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    Technavio (2025). Geospatial Analytics Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, Italy, and UK), APAC (China, India, and Japan), South America (Brazil), and Rest of World (ROW) [Dataset]. https://www.technavio.com/report/geospatial-analytics-market-industry-analysis
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    Dataset updated
    Apr 15, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Canada, Germany, United States, Global
    Description

    Snapshot img

    Geospatial Analytics Market Size 2025-2029

    The geospatial analytics market size is forecast to increase by USD 178.6 billion, at a CAGR of 21.4% between 2024 and 2029.

    The market is experiencing significant growth, driven by the increasing adoption of geospatial analytics in sectors such as healthcare and insurance. This trend is fueled by the ability of geospatial analytics to provide valuable insights from location-based data, leading to improved operational efficiency and decision-making. Additionally, emerging methods in data collection and generation, including the use of drones and satellite imagery, are expanding the scope and potential of geospatial analytics. However, the market faces challenges, including data privacy and security concerns. With the vast amounts of sensitive location data being collected and analyzed, ensuring its protection is crucial for companies to maintain trust with their customers and avoid regulatory penalties. Navigating these challenges and capitalizing on the opportunities presented by the growing adoption of geospatial analytics requires a strategic approach from industry players. Companies must prioritize data security, invest in advanced analytics technologies, and collaborate with stakeholders to build trust and transparency. By addressing these challenges and leveraging the power of geospatial analytics, businesses can gain a competitive edge and unlock new opportunities in various industries.

    What will be the Size of the Geospatial Analytics Market during the forecast period?

    Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
    Request Free SampleThe market continues to evolve, driven by the increasing demand for location-specific insights across various sectors. Urban planning relies on geospatial optimization and data enrichment to enhance city designs and improve infrastructure. Cloud-based geospatial solutions facilitate real-time data access, enabling location intelligence for public safety and resource management. Spatial data standards ensure interoperability among different systems, while geospatial software and data visualization tools provide valuable insights from satellite imagery and aerial photography. Geospatial services offer data integration, spatial data accuracy, and advanced analytics capabilities, including 3D visualization, route optimization, and data cleansing. Precision agriculture and environmental monitoring leverage geospatial data to optimize resource usage and monitor ecosystem health. Infrastructure management and real estate industries rely on geospatial data for asset tracking and market analysis. Spatial statistics and disaster management applications help mitigate risks and respond effectively to crises. Geospatial data management and quality remain critical as the volume and complexity of data grow. Geospatial modeling and interoperability enable seamless data sharing and collaboration. Sensor networks and geospatial data acquisition technologies expand the reach of geospatial analytics, while AI-powered geospatial analytics offer new opportunities for predictive analysis and automation. The ongoing development of geospatial technologies and applications underscores the market's continuous dynamism, providing valuable insights and solutions for businesses and organizations worldwide.

    How is this Geospatial Analytics Industry segmented?

    The geospatial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TechnologyGPSGISRemote sensingOthersEnd-userDefence and securityGovernmentEnvironmental monitoringMining and manufacturingOthersApplicationSurveyingMedicine and public safetyMilitary intelligenceDisaster risk reduction and managementOthersTypeSurface and field analyticsGeovisualizationNetwork and location analyticsOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

    By Technology Insights

    The gps segment is estimated to witness significant growth during the forecast period.The market encompasses various applications and technologies, including geospatial optimization, data enrichment, location-based services (LBS), spatial data standards, public safety, geospatial software, resource management, location intelligence, geospatial data visualization, geospatial services, data integration, 3D visualization, satellite imagery, remote sensing, GIS platforms, spatial data infrastructure, aerial photography, route optimization, data cleansing, precision agriculture, spatial interpolation, geospatial databases, transportation planning, spatial data accuracy, spatial analysis, map projections, interactive maps, marketing analytics, d

  14. g

    USGS Hydrography (NHD) Overlay Map Service from The National Map - National...

    • gimi9.com
    + more versions
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    USGS Hydrography (NHD) Overlay Map Service from The National Map - National Geospatial Data Asset (NGDA) National Hydrography Dataset (NHD) | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_1f407ec6b0e8f9e2268417dc6266ac8b8c1f7352
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    Description

    The USGS National Hydrography Dataset (NHD) service from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. The NHD from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on the NHD, go to https://nhd.usgs.gov/index.html. The Watershed Boundary Dataset (WBD) is a companion dataset to the NHD. It defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Upper Mississippi River, will be composed of multiple smaller drainage areas, such as the Wisconsin River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to https://nhd.usgs.gov/wbd.html. The National Map hydrography data is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain NHD and WBD data in either Esri File or Personal Geodatabase, or Shapefile formats.

  15. Geospatial Analytics Market Size, Insights, Trends & Share Report, 2035

    • rootsanalysis.com
    Updated Sep 10, 2024
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    Roots Analysis (2024). Geospatial Analytics Market Size, Insights, Trends & Share Report, 2035 [Dataset]. https://www.rootsanalysis.com/geospatial-analytics-market
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    Dataset updated
    Sep 10, 2024
    Dataset provided by
    Authors
    Roots Analysis
    License

    https://www.rootsanalysis.com/privacy.htmlhttps://www.rootsanalysis.com/privacy.html

    Time period covered
    2021 - 2031
    Area covered
    Global
    Description

    The geospatial analytics market size is predicted to rise from $93.49 billion in 2024 to $362.45 billion by 2035, growing at a CAGR of 13.1% from 2024 to 2035.

  16. Geospatial data for the Vegetation Mapping Inventory Project of Theodore...

    • catalog.data.gov
    Updated Jun 4, 2024
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    National Park Service (2024). Geospatial data for the Vegetation Mapping Inventory Project of Theodore Roosevelt National Park [Dataset]. https://catalog.data.gov/dataset/geospatial-data-for-the-vegetation-mapping-inventory-project-of-theodore-roosevelt-nationa
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    Dataset updated
    Jun 4, 2024
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Description

    The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. An ArcInfo (copyright ESRI) GIS database was designed for THRO using the National Park GIS Database Design, Layout, and Procedures created by RSGIG. This was created through Arc Macro Language (AML) scripts that helped automate the transfer process and ensure that all spatial and attribute data was consistent and stored properly. Actual transfer of information from the interpreted aerial photographs to a digital, geo-referenced format involved two techniques, scanning (for the vegetation classes) and on-screen digitizing (for the land-use classes). Transferred information used to create vegetation polygon coverages and linear coverages in ArcInfo were based on quarter-quad borders. Attribute information including vegetation map unit, location, and aerial photo number was subsequently entered for all polygons. In addition, the spatial database has an FGDC-compliant metadata file.

  17. w

    Kentucky Geological Survey: Geospatial Data Library

    • data.wu.ac.at
    html
    Updated Mar 23, 2015
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    (2015). Kentucky Geological Survey: Geospatial Data Library [Dataset]. https://data.wu.ac.at/odso/edx_netl_doe_gov/YjU5ODExODEtODljYi00YmY3LWE4NTUtOGQ5MzEwYzJiMzA2
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    htmlAvailable download formats
    Dataset updated
    Mar 23, 2015
    Area covered
    e6b005132ff975e458d3ea0a449441e7311b0d76
    Description

    Maps and GIS data provided by The Kentucky Geological Survey; includes maps, elevation, geology data, hydrology data, and transportation information.

  18. A

    USGS Structures Overlay Map Service from The National Map - National...

    • data.amerigeoss.org
    • data.wu.ac.at
    esri rest, pdf, wfs +1
    Updated Jul 28, 2019
    + more versions
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    United States[old] (2019). USGS Structures Overlay Map Service from The National Map - National Geospatial Data Asset (NGDA) USGS National Structures Dataset [Dataset]. https://data.amerigeoss.org/it/dataset/usgs-structures-overlay-map-service-from-the-national-map-national-geospatial-data-asset-ngda-0
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    wfs, esri rest, wms, pdfAvailable download formats
    Dataset updated
    Jul 28, 2019
    Dataset provided by
    United States[old]
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently being collected are: School, Technical/Trade School, College/University, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, State Capitol, Hospital/Medical Center, Ambulance Service, Cemetery, and Post Office. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map download client allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://nationalmap.gov/structures.html.

  19. A

    Geospatial data for the Vegetation Mapping Inventory Project of Great Basin...

    • data.amerigeoss.org
    • gimi9.com
    Updated Jul 27, 2019
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    United States[old] (2019). Geospatial data for the Vegetation Mapping Inventory Project of Great Basin National Park [Dataset]. https://data.amerigeoss.org/tr/dataset/geospatial-data-for-the-vegetation-mapping-inventory-project-of-great-basin-national-park
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    Dataset updated
    Jul 27, 2019
    Dataset provided by
    United States[old]
    Description

    The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

    GRBA’s spatial database and map layer was produced from high-resolution 2007 Digital Map, Inc. imagery provided to CTI by the NPS. By comparing the signatures on the imagery to field and ground data, 64 map units (48 vegetated, four barren geology and snow, and 12 land-use / land-cover) were developed and the vegetation map units were directly cross-walked or matched to their corresponding rUSNVC plant associations. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) spatial geodatabases and maps were printed, field tested, reviewed, and revised.

  20. d

    Geospatial mapping products derived from 2018, 2020, and 2022 NAIP aerial...

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 6, 2024
    + more versions
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    U.S. Geological Survey (2024). Geospatial mapping products derived from 2018, 2020, and 2022 NAIP aerial imagery for the Scotts Creek Watershed, Lake County, CA [Dataset]. https://catalog.data.gov/dataset/geospatial-mapping-products-derived-from-2018-2020-and-2022-naip-aerial-imagery-for-the-sc
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Lake County, California, Scotts Creek
    Description

    The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial mapping products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. The imagery was classified using Random Forest (RF) Modeling to produce land cover maps with three main classifications - bare, vegetation, and shadows. An updated roads and trails map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate road and trail densities in the watershed. Separate metadata records for each product (Land_Cover_Maps_Scotts_Creek_Watershed_CA_2018_2020_2022_metadata.xml, and Roads_and_Trails_Map_Upper_Scotts_Creek_Watershed_CA _2022_metadata.xml) are provided on the ScienceBase page for each child item. Users should be aware of the inherent errors in remote sensing products.

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National Park Service (2024). Geospatial data for the Vegetation Mapping Inventory Project of Crater Lake National Park [Dataset]. https://catalog.data.gov/dataset/geospatial-data-for-the-vegetation-mapping-inventory-project-of-crater-lake-national-park
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Geospatial data for the Vegetation Mapping Inventory Project of Crater Lake National Park

Explore at:
Dataset updated
Jun 4, 2024
Dataset provided by
National Park Servicehttp://www.nps.gov/
Area covered
Crater Lake
Description

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

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