100+ datasets found
  1. List of National Geospatial Data Assets (NGDAs) Portfolio Datasets As...

    • catalog.data.gov
    • res1catalogd-o-tdatad-o-tgov.vcapture.xyz
    • +1more
    Updated Dec 14, 2022
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    Federal Geographic Data Committee (FGDC) National Geospatial Data Asset (NGDA) Portfolio Management Team (Custodian) (2022). List of National Geospatial Data Assets (NGDAs) Portfolio Datasets As Endorsed by the Federal Geospatial Data Committee (FGDC) [Dataset]. https://catalog.data.gov/dataset/list-of-national-geospatial-data-assets-ngdas-portfolio-datasets-as-endorsed-by-the-federal-geo2
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    Dataset updated
    Dec 14, 2022
    Dataset provided by
    National Geospatial-Intelligence Agencyhttp://www.nga.mil/
    Federal Geographic Data Committee
    Description

    An National Geospatial Data Asset (NGDA) is defined as a geospatial dataset that has been designated by the FGDC Steering Committee and meets at least one of the following criteria: used by multiple agencies or with agency partners such as State, Tribal and local governments; applied to achieve Presidential priorities as expressed by OMB; required to meet shared mission goals of multiple Federal agencies; or expressly required by statutory mandate. Together, these datasets comprise the NGDA Portfolio. This metadata points to a spreadsheet that contains the official list of NGDA with a link to specific NGDA metadata maintained by the dataset owners on Data.gov, GeoPlatform.gov, a link to their associated NGDA Theme, and the agency responsible for the NGDA.

  2. a

    Data from: GEOSPATIAL DATA Progress Needed on Identifying Expenditures,...

    • hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    Updated Jun 11, 2024
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    GeoPlatform ArcGIS Online (2024). GEOSPATIAL DATA Progress Needed on Identifying Expenditures, Building and Utilizing a Data Infrastructure, and Reducing Duplicative Efforts [Dataset]. https://hub.arcgis.com/documents/c0cef9e4901143cbb9f15ddbb49ca3b4
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    Dataset updated
    Jun 11, 2024
    Dataset authored and provided by
    GeoPlatform ArcGIS Online
    Description

    Progress Needed on Identifying Expenditures, Building and Utilizing a Data Infrastructure, and Reducing Duplicative Efforts The federal government collects, maintains, and uses geospatial information—data linked to specific geographic locations—to help support varied missions, including national security and natural resources conservation. To coordinate geospatial activities, in 1994 the President issued an executive order to develop a National Spatial Data Infrastructure—a framework for coordination that includes standards, data themes, and a clearinghouse. GAO was asked to review federal and state coordination of geospatial data. GAO’s objectives were to (1) describe the geospatial data that selected federal agencies and states use and how much is spent on geospatial data; (2) assess progress in establishing the National Spatial Data Infrastructure; and (3) determine whether selected federal agencies and states invest in duplicative geospatial data. To do so, GAO identified federal and state uses of geospatial data; evaluated available cost data from 2013 to 2015; assessed FGDC’s and selected agencies’ efforts to establish the infrastructure; and analyzed federal and state datasets to identify duplication. What GAO Found Federal agencies and state governments use a variety of geospatial datasets to support their missions. For example, after Hurricane Sandy in 2012, the Federal Emergency Management Agency used geospatial data to identify 44,000 households that were damaged and inaccessible and reported that, as a result, it was able to provide expedited assistance to area residents. Federal agencies report spending billions of dollars on geospatial investments; however, the estimates are understated because agencies do not always track geospatial investments. For example, these estimates do not include billions of dollars spent on earth-observing satellites that produce volumes of geospatial data. The Federal Geographic Data Committee (FGDC) and the Office of Management and Budget (OMB) have started an initiative to have agencies identify and report annually on geospatial-related investments as part of the fiscal year 2017 budget process. FGDC and selected federal agencies have made progress in implementing their responsibilities for the National Spatial Data Infrastructure as outlined in OMB guidance; however, critical items remain incomplete. For example, the committee established a clearinghouse for records on geospatial data, but the clearinghouse lacks an effective search capability and performance monitoring. FGDC also initiated plans and activities for coordinating with state governments on the collection of geospatial data; however, state officials GAO contacted are generally not satisfied with the committee’s efforts to coordinate with them. Among other reasons, they feel that the committee is focused on a federal perspective rather than a national one, and that state recommendations are often ignored. In addition, selected agencies have made limited progress in their own strategic planning efforts and in using the clearinghouse to register their data to ensure they do not invest in duplicative data. For example, 8 of the committee’s 32 member agencies have begun to register their data on the clearinghouse, and they have registered 59 percent of the geospatial data they deemed critical. Part of the reason that agencies are not fulfilling their responsibilities is that OMB has not made it a priority to oversee these efforts. Until OMB ensures that FGDC and federal agencies fully implement their responsibilities, the vision of improving the coordination of geospatial information and reducing duplicative investments will not be fully realized. OMB guidance calls for agencies to eliminate duplication, avoid redundant expenditures, and improve the efficiency and effectiveness of the sharing and dissemination of geospatial data. However, some data are collected multiple times by federal, state, and local entities, resulting in duplication in effort and resources. A new initiative to create a national address database could potentially result in significant savings for federal, state, and local governments. However, agencies face challenges in effectively coordinating address data collection efforts, including statutory restrictions on sharing certain federal address data. Until there is effective coordination across the National Spatial Data Infrastructure, there will continue to be duplicative efforts to obtain and maintain these data at every level of government.https://www.gao.gov/assets/d15193.pdfWhat GAO Recommends GAO suggests that Congress consider assessing statutory limitations on address data to foster progress toward a national address database. GAO also recommends that OMB improve its oversight of FGDC and federal agency initiatives, and that FGDC and selected agencies fully implement initiatives. The agencies generally agreed with the recommendations and identified plans to implement them.

  3. w

    Geospatial Data Repository (list of resources) - Dataset - waterdata

    • wbwaterdata.org
    Updated Feb 16, 2021
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    (2021). Geospatial Data Repository (list of resources) - Dataset - waterdata [Dataset]. https://wbwaterdata.org/dataset/geospatial-data-repository-list-of-resources
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    Dataset updated
    Feb 16, 2021
    Description

    This is a geospatial data repository for agricultural economists interested in climate, production or soil data. Everything listed here is openly accessible. Most of it is global or quasi-global, with a few Africa-specific products. For development economists with interest in other open source data sets, I would refer you to a major and comprehensive data set collection effort on DEVECONDATA.

  4. EPA Geospatial Data Download: Facility and Site Information

    • catalog.data.gov
    • res1catalogd-o-tdatad-o-tgov.vcapture.xyz
    • +2more
    Updated Feb 24, 2021
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    U.S. EPA Office of Environmental Information (OEI) - Office of Information Collection (OIC) (2021). EPA Geospatial Data Download: Facility and Site Information [Dataset]. https://catalog.data.gov/dataset/epa-geospatial-data-download-facility-and-site-information
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    Dataset updated
    Feb 24, 2021
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    Contains information about facilities or sites subject to environmental regulation, including key facility information along with associated environmental interests for use in mapping and reporting applications.

  5. Dataset relating a study on Geospatial Open Data usage and metadata quality

    • zenodo.org
    • data.niaid.nih.gov
    Updated Jun 19, 2023
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    Alfonso Quarati; Alfonso Quarati; Monica De Martino; Monica De Martino (2023). Dataset relating a study on Geospatial Open Data usage and metadata quality [Dataset]. http://doi.org/10.5281/zenodo.4280594
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    Dataset updated
    Jun 19, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Alfonso Quarati; Alfonso Quarati; Monica De Martino; Monica De Martino
    Description

    The Open Government Data portals (OGD) thanks to the presence of thousands of geo-referenced datasets, containing spatial information, are of extreme interest for any analysis or process relating to the territory. For this to happen, users must be enabled to access these datasets and reuse them. An element often considered hindering the full dissemination of OGD data is the quality of their metadata. Starting from an experimental investigation conducted on over 160,000 geospatial datasets belonging to six national and international OGD portals, this work has as its first objective to provide an overview of the usage of these portals measured in terms of datasets views and downloads. Furthermore, to assess the possible influence of the quality of the metadata on the use of geospatial datasets, an assessment of the metadata for each dataset was carried out, and the correlation between these two variables was measured. The results obtained showed a significant underutilization of geospatial datasets and a generally poor quality of their metadata. Besides, a weak correlation was found between the use and quality of the metadata, not such as to assert with certainty that the latter is a determining factor of the former.

    The dataset consists of six zipped CSV files, containing the collected datasets' usage data, full metadata, and computed quality values, for about 160,000 geospatial datasets belonging to the three national and three international portals considered in the study, i.e. US (catalog.data.gov), Colombia (datos.gov.co), Ireland (data.gov.ie), HDX (data.humdata.org), EUODP (data.europa.eu), and NASA (data.nasa.gov).

    Data collection occurred in the period: 2019-12-19 -- 2019-12-23.

    The header for each CSV file is:

    [ ,portalid,id,downloaddate,metadata,overallq,qvalues,assessdate,dviews,downloads,engine,admindomain]

    where for each row (a portal's dataset) the following fields are defined as follows:

    • portalid: portal identifier
    • id: dataset identifier
    • downloaddate: date of data collection
    • metadata: the overall dataset's metadata downloaded via API from the portal according to the supporting platform schema
    • overallq: overall quality values computed by applying the methodology presented in [1]
    • qvalues: json object containing the quality values computed for the 17 metrics presented in [1]
    • assessdate: date of quality assessment
    • dviews: number of total views for the dataset
    • downloads: number of total downloads for the dataset (made available only by the Colombia, HDX, and NASA portals)
    • engine: identifier of the supporting portal platform: 1(CKAN), 2 (Socrata)
    • admindomain: 1 (national), 2 (international)

    [1] Neumaier, S.; Umbrich, J.; Polleres, A. Automated Quality Assessment of Metadata Across Open Data Portals.J. Data and Information Quality2016,8, 2:1–2:29. doi:10.1145/2964909

  6. GIS Data Object Publishing instructions

    • catalog.data.gov
    • data.amerigeoss.org
    Updated Jul 4, 2025
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    Social Security Administration (2025). GIS Data Object Publishing instructions [Dataset]. https://catalog.data.gov/dataset/gis-data-object-publishing-instructions
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    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Social Security Administrationhttp://ssa.gov/
    Description

    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.

  7. Synthetic geospatial data for performance analysis of geospatial database...

    • data.europa.eu
    unknown
    Updated Nov 8, 2017
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    Zenodo (2017). Synthetic geospatial data for performance analysis of geospatial database systems [Dataset]. https://data.europa.eu/data/datasets/oai-zenodo-org-1043822?locale=de
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    unknown(71180886)Available download formats
    Dataset updated
    Nov 8, 2017
    Dataset authored and provided by
    Zenodohttp://zenodo.org/
    License

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

    Description

    This dataset contains a set of synthetic data that can be used to evaluate the efficiency of geosaptial datasbases. The datasets is composed of four json file, characterized by different size. They can be used to analyze the scalability of geospatial datasets with respect to the database size. Each json file contains a set of "points", each one characterized by a set of random attributes (description, url of a picture linked to the point, creation date, delete date, update date, identifier, partition identifier). The synthetically generated points are uniformly distributed among the world.

  8. Southwestern Region (Region 3) Geospatial Data

    • agdatacommons.nal.usda.gov
    bin
    Updated Feb 13, 2024
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    USDA Forest Service (2024). Southwestern Region (Region 3) Geospatial Data [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Southwestern_Region_Region_3_Geospatial_Data/24661962
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    binAvailable download formats
    Dataset updated
    Feb 13, 2024
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    USDA Forest Service
    License

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

    Area covered
    Southwestern United States
    Description

    The Southwestern Region is 20.6 million acres. There are six national forests in Arizona, five national forests and a national grassland in New Mexico, and one national grassland each in Oklahoma and the Texas panhandle.The region ranges in elevation from 1,600 feet above sea level and an annual rain fall of 8 inches in Arizona's lower Sonoran Desert to 13,171-foot high Wheeler Peak and over 35 inches of precipitation a year in northern New Mexico. Geographic Information Systems or GIS are computer systems, software and data used to analyze and display spatial or locational data about surface features. One of the strengths of GIS is the capability to overlay or compare multiple feature layers. A user can then analyze the relationship between the layers. Data, reports and maps produced through GIS are used by managers and resource specialists to make decisions about land management activities on National Forests. The National Forests of the Southwestern Region maintain and utilize GIS data for various features on the ground. Some of these datasets are made available for download through this page. Resources in this dataset:Resource Title: GIS Datasets. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=STELPRDB5202474 Selected GIS datasets for the Southwestern Region are available for download from this page.Resource Software Recommended: ArcExplorer,url: http://www.esri.com/software/arcexplorer/index.html

  9. d

    Geospatial Data: Places Data | Global | Location Data on 56M+ Places

    • datarade.ai
    .csv
    Updated Feb 25, 2022
    + more versions
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    SafeGraph (2022). Geospatial Data: Places Data | Global | Location Data on 56M+ Places [Dataset]. https://datarade.ai/data-products/geospatial-data-places-data-usa-uk-ca-location-data-on-safegraph
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    .csvAvailable download formats
    Dataset updated
    Feb 25, 2022
    Dataset authored and provided by
    SafeGraph
    Area covered
    United States
    Description

    SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

    SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

    SafeGraph provides clean and accurate geospatial datasets on 52M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

  10. Vector datasets for workshop "Introduction to Geospatial Raster and Vector...

    • figshare.com
    Updated Oct 5, 2022
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    Ryan Avery (2022). Vector datasets for workshop "Introduction to Geospatial Raster and Vector Data with Python" [Dataset]. http://doi.org/10.6084/m9.figshare.21273837.v1
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    application/x-sqlite3Available download formats
    Dataset updated
    Oct 5, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Ryan Avery
    License

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

    Description

    Cadaster data from PDOK used to illustrate the use of geopandas and shapely, geospatial python packages for manipulating vector data. The brpgewaspercelen_definitief_2020.gpkg file has been subsetted in order to make the download manageable for workshops. Other datasets are copies of those available from PDOK.

  11. a

    CT Geodata Portal

    • ct-geospatial-data-portal-ctmaps.hub.arcgis.com
    • data.ct.gov
    • +2more
    Updated Oct 14, 2022
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    State of Connecticut (2022). CT Geodata Portal [Dataset]. https://ct-geospatial-data-portal-ctmaps.hub.arcgis.com/datasets/ct-geodata-portal
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    Dataset updated
    Oct 14, 2022
    Dataset authored and provided by
    State of Connecticut
    Area covered
    Description

    The CT Geodata Portal is an open data site for all geospatial data in Connecticut. Users can find spatial datasets directly administered by the GIS Office as well as those shared by the Department of Transportation, the Department of Energy and Environmental Protection, CT ECO, and other partners. The Geodata portal aims to provide residents, policymakers, and researchers easy access to foundational geospatial datasets and promote open data principles.

  12. m

    Geospatial Datasets for Assessing Vulnerability of Bangladesh to Climate...

    • data.mendeley.com
    • narcis.nl
    Updated Jan 12, 2021
    + more versions
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    MD GOLAM AZAM (2021). Geospatial Datasets for Assessing Vulnerability of Bangladesh to Climate Change and Extremes [Dataset]. http://doi.org/10.17632/cv6cyfgmcd.3
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    Dataset updated
    Jan 12, 2021
    Authors
    MD GOLAM AZAM
    License

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

    Area covered
    Bangladesh
    Description

    The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

  13. v

    Data from: Low-Temperature Geothermal Geospatial Datasets: An Example from...

    • res1catalogd-o-tdatad-o-tgov.vcapture.xyz
    • gdr.openei.org
    • +3more
    Updated May 29, 2025
    + more versions
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    National Renewable Energy Laboratory (2025). Low-Temperature Geothermal Geospatial Datasets: An Example from Alaska [Dataset]. https://res1catalogd-o-tdatad-o-tgov.vcapture.xyz/dataset/low-temperature-geothermal-geospatial-datasets-an-example-from-alaska-17a53
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    Dataset updated
    May 29, 2025
    Dataset provided by
    National Renewable Energy Laboratory
    Area covered
    Alaska
    Description

    This project is a component of a broader effort focused on geothermal heating and cooling (GHC) with the aim of illustrating the numerous benefits of incorporating GHC and geothermal heat exchange (GHX) into community energy planning and national decarbonization strategies. To better assist private sector investment, it is currently necessary to define and assess the potential of low-temperature geothermal resources. For shallow GHC/GHX fields, there is no formal compilation of subsurface characteristics shared among industry practitioners that can improve system design and operations. Alaska is specifically noted in this work, because heretofore, it has not received a similar focus in geothermal potential evaluations as the contiguous United States. The methodology consists of leveraging relevant data to generate a baseline geospatial dataset of low-temperature resources (less than 150 degrees C) to compare and analyze information accessible to anyone trying to understand the potential of GHC/GHX and small-scale low-temperature geothermal power in Alaska (e.g., energy modelers, communities, planners, and policymakers). Importantly, this project identifies data related to (1) the evaluation of GHC/GHX in the shallow subsurface, and (2) the evaluation of low-temperature geothermal resource availability. Additionally, data is being compiled to assess repurposing of oil and gas wells to contribute co-produced fluids toward the geothermal direct use and heating and cooling resource potential. In this work we identified new data from three different datasets of isolated geothermal systems in Alaska and bottom-hole temperature data from oil and gas wells that can be leveraged for evaluation of low-temperature geothermal resource potential. The goal of this project is to facilitate future deployment of GHC/GHX analysis and community-led programs and update the low-temperature geothermal resources assessment of Alaska. A better understanding of shallow potential for GHX will improve design and operations of highly efficient GHC systems. The deployment and impact that can be achieved for low-temperature geothermal resources will contribute to decarbonization goals and facilitate widespread electrification by shaving and shifting grid loads. Most of the data uses WGS84 coordinate system. However, each dataset come from different sources and has a metadata file with the original coordinate system.

  14. A

    Pattern-based GIS for understanding content of very large Earth Science...

    • data.amerigeoss.org
    • data.wu.ac.at
    html
    Updated Jan 29, 2020
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    United States (2020). Pattern-based GIS for understanding content of very large Earth Science datasets [Dataset]. https://data.amerigeoss.org/dataset/pattern-based-gis-for-understanding-content-of-very-large-earth-science-datasets1
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    htmlAvailable download formats
    Dataset updated
    Jan 29, 2020
    Dataset provided by
    United States
    Area covered
    Earth
    Description

    The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

    GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

    The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

  15. National Aggregates of Geospatial Data Collection: Population, Landscape,...

    • data.nasa.gov
    • datasets.ai
    • +6more
    Updated May 24, 2022
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    nasa.gov (2022). National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 4 (PLACE IV) [Dataset]. https://data.nasa.gov/dataset/national-aggregates-of-geospatial-data-collection-population-landscape-and-climate-estimat-6804e
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    Dataset updated
    May 24, 2022
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 4 (PLACE IV) provides measures of population (head counts) and land area (square kilometers) as totals and by urban and rural designation, within multiple biophysical themes for 248 statistical areas (countries and other territories recognized by the United Nations (UN)), UN geographic regions and subregions, and World Bank economic classifications. It improves upon previous versions by providing these estimates at both the national level, and where possible, at subnational administrative level 1 for the years 2000, 2005, 2010, 2015, and 2020, and by 5-year and broad age groups for the year 2010.

  16. W

    US EPA Geospatial Data Access

    • cloud.csiss.gmu.edu
    html
    Updated Mar 21, 2019
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    GEOSS CSR (2019). US EPA Geospatial Data Access [Dataset]. http://cloud.csiss.gmu.edu/uddi/lt/dataset/us-epa-geospatial-data-access
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    htmlAvailable download formats
    Dataset updated
    Mar 21, 2019
    Dataset provided by
    GEOSS CSR
    Description

    This site contains information on the full range of EPA geospatial data that is available from across the Agency's research and regulatory programs, as well as links to several download and access mechanisms.

  17. d

    National Aggregates of Geospatial Data Collection: Population, Landscape,...

    • catalog.data.gov
    • datasets.ai
    • +6more
    Updated Apr 24, 2025
    + more versions
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    SEDAC (2025). National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III) [Dataset]. https://catalog.data.gov/dataset/national-aggregates-of-geospatial-data-collection-population-landscape-and-climate-estimat
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    SEDAC
    Description

    The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III) data set contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

  18. d

    Riparian-Zone Boundaries for the U.S. Geological Survey Southeast Stream...

    • search.dataone.org
    • gimi9.com
    • +4more
    Updated Sep 7, 2017
    + more versions
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    Sharon L. Qi; Naomi Nakagaki; Amanda H. Bell (2017). Riparian-Zone Boundaries for the U.S. Geological Survey Southeast Stream Quality Assessment [Dataset]. https://search.dataone.org/view/189373e2-5ee8-41ca-bfda-9074ff3934c7
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    Dataset updated
    Sep 7, 2017
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Sharon L. Qi; Naomi Nakagaki; Amanda H. Bell
    Area covered
    Variables measured
    SITE_NO, ECO_LAT83, ECO_LON83, DRreach_km, RpZone_km2, SHORT_NAME, STATION_NM
    Description

    In 2015, the second of several Regional Stream Quality Assessments (RSQA) was done in the southeastern United States. The Southeast Stream Quality Assessment (SESQA) was a study by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA) project. One of the objectives of the RSQA, and thus the SESQA, is to characterize the relationships between water-quality stressors and stream ecology and subsequently determine the relative effects of these stressors on aquatic biota within the streams (Van Metre and Journey, 2014). To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. This dataset represents the 115 water-chemistry sites sampled for the SESQA, and is one of the four fundamental geospatial data layers that were developed for the Southeast study.

  19. e

    Geospatial Data Catalogue: Ordnance Survey

    • data.europa.eu
    • cloud.csiss.gmu.edu
    • +1more
    csv
    Updated Sep 27, 2021
    + more versions
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    Ordnance Survey (2021). Geospatial Data Catalogue: Ordnance Survey [Dataset]. https://data.europa.eu/data/datasets/https-www-ordnancesurvey-co-uk-xml-datasets-gcdatacatalogue-ordnancesurvey-csv
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    csvAvailable download formats
    Dataset updated
    Sep 27, 2021
    Dataset authored and provided by
    Ordnance Surveyhttps://os.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Working with the Geospatial Commission, the Geo6 organisations have produced a simplified common data catalogue providing core information on the geospatial datasets that they hold and manage. This catalogue or index of data is made available under OGL however, the underlying datasets may be available under different licences.

    Geographic Coverage

    England, Wales, Scotland

    Additional Resources

    Link to CSV column definitions

    Frequency of Update

    Bi-annual or sooner, if required.

  20. m

    USA POI & Foot Traffic Enriched Geospatial Dataset by Predik Data-Driven

    • app.mobito.io
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    USA POI & Foot Traffic Enriched Geospatial Dataset by Predik Data-Driven [Dataset]. https://app.mobito.io/data-product/usa-enriched-geospatial-framework-dataset
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    Area covered
    United States
    Description

    Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

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Federal Geographic Data Committee (FGDC) National Geospatial Data Asset (NGDA) Portfolio Management Team (Custodian) (2022). List of National Geospatial Data Assets (NGDAs) Portfolio Datasets As Endorsed by the Federal Geospatial Data Committee (FGDC) [Dataset]. https://catalog.data.gov/dataset/list-of-national-geospatial-data-assets-ngdas-portfolio-datasets-as-endorsed-by-the-federal-geo2
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List of National Geospatial Data Assets (NGDAs) Portfolio Datasets As Endorsed by the Federal Geospatial Data Committee (FGDC)

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Dataset updated
Dec 14, 2022
Dataset provided by
National Geospatial-Intelligence Agencyhttp://www.nga.mil/
Federal Geographic Data Committee
Description

An National Geospatial Data Asset (NGDA) is defined as a geospatial dataset that has been designated by the FGDC Steering Committee and meets at least one of the following criteria: used by multiple agencies or with agency partners such as State, Tribal and local governments; applied to achieve Presidential priorities as expressed by OMB; required to meet shared mission goals of multiple Federal agencies; or expressly required by statutory mandate. Together, these datasets comprise the NGDA Portfolio. This metadata points to a spreadsheet that contains the official list of NGDA with a link to specific NGDA metadata maintained by the dataset owners on Data.gov, GeoPlatform.gov, a link to their associated NGDA Theme, and the agency responsible for the NGDA.

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