Expands the use of internal data for creating Geographic Information System (GIS) maps. SSA's Database Systems division developed a map users guide for GIS data object publishing and was made available in an internal Sharepoint site for access throughout the agency. The guide acts as the reference for publishers of GIS objects across the life-cycle in our single, central geodatabase implementation.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

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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?

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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, gover

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

The U.S. Army Corps of Engineers Geospatial Open Data provides shared and trusted USACE geospatial data, services and applications for use by our partner agencies and the public.

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the census 2022 county boundary file in a GIS system to produce maps for 40 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

Geographic & Location Intelligence Data: ✅ Latitude & longitude coordinates for mapping and navigation applications. ✅ Geographic classification, including country, state, city, and postal code. ✅ Business status tracking – Open, temporarily closed, or permanently closed.

Continuous Discovery & Regular Updates: ✅ New POIs continuously added through discovery processes. ✅ Regular updates ensure data accuracy, reflecting new openings and closures.

Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

Ideal for Mapping & Location Analytics: ✅ Supports geospatial analysis & GIS applications. ✅ Enhances mapping & navigation solutions with structured POI data. ✅ Provides location intelligence for site selection & business expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured format (.json) for seamless integration.

🏆Primary Use Cases:

Mapping & Geographic Analysis: 🔹 Power GIS platforms & navigation systems with precise POI data. 🔹 Enhance digital maps with accurate business locations & categories.

Retail Expansion & Market Research: 🔹 Identify key business locations & competitors for market analysis. 🔹 Assess brand presence across different industries & geographies.

Business Intelligence & Competitive Analysis: 🔹 Benchmark competitor locations & regional business density. 🔹 Analyze market trends through POI growth & closure tracking.

Smart City & Urban Planning: 🔹 Support public infrastructure projects with accurate POI data. 🔹 Improve accessibility & zoning decisions for government & businesses.

💡 Why Choose Xverum’s POI Data?

• 230M+ Verified POI Records – One of the largest & most detailed location datasets available.
• Global Coverage – POI data from 249+ countries, covering all major business sectors.
• Regular Updates – Ensuring accurate tracking of business openings & closures.
• Comprehensive Geographic & Business Data – Coordinates, addresses, categories, and more.
• Bulk Dataset Delivery – S3 Bucket & cloud storage delivery for full dataset access.
• 100% Compliant – Ethically sourced, privacy-compliant data.

Access Xverum’s 230M+ POI dataset for mapping, geographic analysis, and location intelligence. Request a free sample or contact us to customize your dataset today!

The statewide dataset contains a combination of land cover mapping from 2016 aerial imagery and land use derived from standardized assessor parcel information for Massachusetts. The data layer is the result of a cooperative project between MassGIS and the National Oceanic and Atmospheric Administration’s (NOAA) Office of Coastal Management (OCM). Funding was provided by the Mass. Executive Office of Energy and Environmental Affairs.

This land cover/land use dataset does not conform to the classification schemes or polygon delineation of previous land use data from MassGIS (1951-1999; 2005).In this map service layer hosted at MassGIS' ArcGIS Server, all impervious polygons are symbolized by their generalized use code; all non-impervious land cover polygons are symbolized by their land cover category. The idea behind this method is to use both cover and use codes to provide a truer picture of how land is being used: parcel use codes may indicate allowed or assessed, not actual use; land cover alone (especially impervious) does not indicate actual use.

See the full datalayer description for more details.This map service is best displayed at large (zoomed in) scales. Also available are a Feature Service and a Tile Service (cache). The tile cache will display very quickly in in ArcGIS Online, ArcGIS Desktop, and other applications that can consume tile services.

Unlock precise, high-quality GIS data covering 4.5M+ verified locations across Italy. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

Unlock precise, high-quality GIS data covering 43M+ verified locations across the USA. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

DescriptionThis dataset is available for download from: Parcelization (File Geodatabase)Parcelization, a measure of size and density of parcels in a localized area, is a development feasibility factor that is used in evaluating substations’ ability to support new utility-scale resources in long-term energy planning. A statewide dataset of parcel boundaries are used to develop this index. The parcels are converted into a 90-meter raster, containing values of a unique identifier reflective of Parcel APN. A focal statistics tool is used to count the number of unique parcels within a 0.5 mile radius of each parcel. This output is provided here and is an intermediate output to the final parcelization map. Users who wish to use this information to produce the final map should overlay parcel boundary data and extract the mean raster value within each parcel. The map is limited to the area considered with solar technical resource potential after a minimum set of land-use screens (referred to as the Base Exclusions) has been applied. More information on the methods developing this dataset as well as the main use of this dataset in state electric system planning processes can be found in a recent CEC staff report and workshops supporting the resource-to-busbar mapping methodology for the 2024-2025 Transmission Planning Process.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

These data show sample locations for various abiotic data collected on Konza Prairie (rain gauges, soil moisture, and stream data). Included in these data are the locations for 12rain gauges (GIS300) on Konza Prairie. The Konza headquarters weather station formerly consisted of two gauges which were operated year-round. The Konza headquarters weather station currently consists of one Otto-Pluvio2 gauge which is operated year-round. The remaining Konza-operated gauges run from April 1 to November 1. These data are to be used in conjunction with the APT01 (precipitation) dataset. GIS305 defines the locations where measurements of soil moisture (%volume) are taken on Konza Prairie. These data are to be used in conjunction with the ASM01 (soil moisture) dataset. GIS310 defines the locations within watershed N4D where samples are taken for analyzing the belowground water chemistry of the watershed. These data are to be used in conjunction with the AGW01 dataset. GIS311 defines the locations of 14 wells at two sites along Kings Creek. Depth and nutrient content of groundwater is measured at these sites. These data are to be used in conjunction with the AGW02 dataset. GIS315 defines the locations of stream gauging stations (including one operated by the USGS*) within multiple Konza watersheds. These data are to be used in conjunction with the APT, NWC, ASS, and ASW datasets. (*http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650) GIS320 defines the locations of the rainfall collectors used to collect the samples analyzed as a part of the National Atmospheric Deposition Program. These data are to be used in conjunction with the ANA01 dataset.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

World Countries Generalized represents generalized boundaries for the countries of the world. It has fields for official names and country codes. The generalized political boundaries improve draw performance and effectiveness at a global or continental level.This layer is best viewed out beyond a scale of 1:5,000,000.This layer's geography was developed by Esri, Garmin International, Inc., the U.S. Central Intelligence Agency (The World Factbook), and the National Geographic Society for use as a world basemap. It is updated annually as country names or significant borders change.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.