100+ datasets found
  1. Geodatabase for the Baltimore Ecosystem Study Spatial Data

    • search.dataone.org
    • portal.edirepository.org
    Updated Apr 1, 2020
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    Spatial Analysis Lab; Jarlath O'Neal-Dunne; Morgan Grove (2020). Geodatabase for the Baltimore Ecosystem Study Spatial Data [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F3120%2F150
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    Dataset updated
    Apr 1, 2020
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Spatial Analysis Lab; Jarlath O'Neal-Dunne; Morgan Grove
    Time period covered
    Jan 1, 1999 - Jun 1, 2014
    Area covered
    Description

    The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

  2. a

    Parks (File Geodatabase)

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • data-mcplanning.hub.arcgis.com
    Updated May 10, 2023
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    Montgomery Maps (2023). Parks (File Geodatabase) [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/e9a1b98043c441d6a941c775264edd72
    Explore at:
    Dataset updated
    May 10, 2023
    Dataset authored and provided by
    Montgomery Maps
    License

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

    Description
  3. a

    Damage Lines SDE

    • data-napsg.opendata.arcgis.com
    • prep-response-portal.napsgfoundation.org
    • +4more
    Updated May 29, 2019
    + more versions
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    NAPSG Foundation (2019). Damage Lines SDE [Dataset]. https://data-napsg.opendata.arcgis.com/maps/damage-lines-sde
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    Dataset updated
    May 29, 2019
    Dataset authored and provided by
    NAPSG Foundation
    Area covered
    Description

    Currently filtered for Storm Date is after 12/1/2023Purpose: This is a feature layer of tornado swaths for the NWS Damage Assessment Toolkit.The National Weather Service (NWS) Damage Assessment Toolkit (DAT) has been utilized experimentally since 2009 to assess damage following tornadoes and convective wind events. The DAT is a GIS-based framework for collecting, storing, and analyzing damage survey data, utilizing the Enhanced Fujita (EF) scale for the classification of damage. Data collected from individual locations via mobile device are transmitted to a central geospatial database where they are quality controlled and analyzed to assign the official EF rating. In addition to the individual point, the data are analyzed to generate track centerlines and damage swaths. High resolution satellite imagery and radar data, through partnership with the NASA Short-term Prediction Research and Transition Center, are also available to aid in the analysis. The subsequent dataset is then made available through a web-based graphical interface and GIS services.Here is the full REST service: https://services.dat.noaa.gov/arcgis/rest/services/nws_damageassessmenttoolkitGeoplatform website: https://communities.geoplatform.gov/disasters/noaa-damage-assessment-toolkit-dat/More InformationWelcome to the National Weather Service Damage Assessment Toolkit. Data on this interface is collected during NWS Post-Event Damage Assessments. While the data has been quality controlled, it is still considered preliminary. Official statistics for severe weather events can be found in the Storm Data publication, available from the National Centers for Environmental Information (NCEI) at: https://www.ncdc.noaa.gov/IPS/sd/sd.html Questions regarding this data can be addressed to: parks.camp@noaa.gov.

  4. u

    Utah Soils Deprecated

    • opendata.gis.utah.gov
    Updated Jan 14, 2020
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    Utah Automated Geographic Reference Center (AGRC) (2020). Utah Soils Deprecated [Dataset]. https://opendata.gis.utah.gov/datasets/dfcc74caa2e94584b6fff271754c1473
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    Dataset updated
    Jan 14, 2020
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    NOTE: This dataset is an older dataset that we have removed from the SGID and 'shelved' in ArcGIS Online. There may (or may not) be a newer vintage of this dataset in the SGID.NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.

  5. u

    Utah Soils

    • opendata.gis.utah.gov
    Updated Jan 14, 2020
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    Utah Automated Geographic Reference Center (AGRC) (2020). Utah Soils [Dataset]. https://opendata.gis.utah.gov/datasets/utah-soils
    Explore at:
    Dataset updated
    Jan 14, 2020
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.

  6. d

    ScienceBase Item Summary Page

    • datadiscoverystudio.org
    Updated Jul 20, 2011
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    (2011). ScienceBase Item Summary Page [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/b9cc28590db64ac497490c735fefd988/html
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    Dataset updated
    Jul 20, 2011
    Area covered
    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  7. a

    Ditches

    • hub.arcgis.com
    • abstractorresources-starkcountyohio.hub.arcgis.com
    • +2more
    Updated Mar 20, 2024
    + more versions
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    Stark County Ohio (2024). Ditches [Dataset]. https://hub.arcgis.com/datasets/starkcountyohio::stark-countywide-stormwater-systems?layer=3
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    Dataset updated
    Mar 20, 2024
    Dataset authored and provided by
    Stark County Ohio
    Area covered
    Description

    A combination of stormwater system data throughout Stark County, Ohio. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.As this layer encompasses the entire county, source feature classes are consolidated into 4 layers to improve performance on ArcGIS Online. Discharge points are the point at which water exits part of the stormwater system, such as the outlet of a pipe or ditch. It includes outfalls defined under NPDES Phase II. Structures includes both inlets (catch basins, yard drains, etc.) and manholes. Pipes includes storm sewers, as well as culverts (pipes in which both ends are daylit). Finally, the ditches layer includes roadside ditches, as well as off-road ditches in some areas/instances.

  8. a

    arcgis.SDE.EABoundary2014

    • hub.arcgis.com
    Updated Mar 19, 2014
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    Kerry County Council (2014). arcgis.SDE.EABoundary2014 [Dataset]. https://hub.arcgis.com/maps/kerry::arcgis-sde-eaboundary2014
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    Dataset updated
    Mar 19, 2014
    Dataset authored and provided by
    Kerry County Council
    Area covered
    Description

    Local Electoral Areas 2014, for Local Area Elections. Also showing Electoral Divisions.

  9. a

    US.SDE.Mills

    • hub.arcgis.com
    Updated Oct 17, 2016
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    Kentucky Community and Technical College System (2016). US.SDE.Mills [Dataset]. https://hub.arcgis.com/maps/KCTCS::us-sde-mills
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    Dataset updated
    Oct 17, 2016
    Dataset authored and provided by
    Kentucky Community and Technical College System
    Area covered
    Description

    Map of Mills and Covered Bridges. The data is curated by Vince DiNoto and links to a Esri Collector application.

  10. T

    Utah Surrounding State Counties

    • opendata.utah.gov
    • opendata.gis.utah.gov
    application/rdfxml +5
    Updated Mar 20, 2020
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    (2020). Utah Surrounding State Counties [Dataset]. https://opendata.utah.gov/dataset/Utah-Surrounding-State-Counties/qd8e-e57a
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    application/rdfxml, xml, csv, application/rssxml, tsv, jsonAvailable download formats
    Dataset updated
    Mar 20, 2020
    Area covered
    Utah
    Description

    NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.

  11. d

    Moose Concentration Area

    • datadiscoverystudio.org
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    Moose Concentration Area [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/df069b3091f948bdb9c3e89919e9031b/html
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    Area covered
    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  12. u

    Utah Potential Liquefaction Wasatch Front

    • opendata.gis.utah.gov
    • gis-support-utah-em.hub.arcgis.com
    Updated Jan 14, 2020
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    Utah Automated Geographic Reference Center (AGRC) (2020). Utah Potential Liquefaction Wasatch Front [Dataset]. https://opendata.gis.utah.gov/datasets/utah-potential-liquefaction-wasatch-front
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    Dataset updated
    Jan 14, 2020
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.

  13. 25 Mile Buffer of MARTA Rail Stations

    • opendata.atlantaregional.com
    • gisdata.fultoncountyga.gov
    • +3more
    Updated Apr 23, 2020
    + more versions
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    Georgia Association of Regional Commissions (2020). 25 Mile Buffer of MARTA Rail Stations [Dataset]. https://opendata.atlantaregional.com/datasets/-25-mile-buffer-of-marta-rail-stations/data
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    Dataset updated
    Apr 23, 2020
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

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

    Area covered
    Description

    Feature layer generated from running the Buffer Features solution. Input from Transit Rail Stations - SDEPUB.SDE.Transit_Rail_Stations were buffered by [0.25] Miles

  14. s

    Stark Countywide Stormwater Pipes

    • opendata.starkcountyohio.gov
    • hub.arcgis.com
    • +2more
    Updated Mar 19, 2024
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    Stark County Ohio (2024). Stark Countywide Stormwater Pipes [Dataset]. https://opendata.starkcountyohio.gov/datasets/stark-countywide-stormwater-pipes
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    Dataset updated
    Mar 19, 2024
    Dataset authored and provided by
    Stark County Ohio
    Area covered
    Description

    A combination of stormwater pipe data throughout Stark County, Ohio. Pipes includes storm sewers, as well as culverts (pipes in which both ends are daylit). The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.

  15. a

    Recreational Trails

    • hub.arcgis.com
    • data.sunshinecoast.qld.gov.au
    • +1more
    Updated Apr 1, 2021
    + more versions
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    Sunshine Coast Council Public Access Hub (2021). Recreational Trails [Dataset]. https://hub.arcgis.com/datasets/scrcpublic::transportation-scrc?layer=13
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    Dataset updated
    Apr 1, 2021
    Dataset authored and provided by
    Sunshine Coast Council Public Access Hub
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Area covered
    Description

    This dataset represents SCC's Trails asset class, and thus the approximate location and basic attribution of known, recorded, SCC-owned or maintained trails within the Sunshine Coast LGA. Eligible path uses include walking/hiking, multi-purpose, horse-riding and mountain biking. This feature class does NOT include 'Bridge' asset category items (eg. boardwalks, footbridges, attached vehicle bridge paths), or 'Pedestrian Tunnel' asset class items. This dataset was generated by various parties and methodologies. Ongoing data collection is imported by SCC AIS staff, and managed within ESRI ArcGIS SDE database architecture. This dataset is to be considered a standalone layer.

  16. u

    Utah Alluvial Fans

    • opendata.gis.utah.gov
    • sgid-utah.opendata.arcgis.com
    Updated Nov 21, 2019
    + more versions
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    Utah Automated Geographic Reference Center (AGRC) (2019). Utah Alluvial Fans [Dataset]. https://opendata.gis.utah.gov/datasets/utah-alluvial-fans
    Explore at:
    Dataset updated
    Nov 21, 2019
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.This data represents active alluvial fans in Utah as identified by several datasets and observed through field investigationsLast update 2017

  17. a

    Stark Countywide Stormwater Discharge Points

    • portal-starkcountyohio.opendata.arcgis.com
    • opendata.starkcountyohio.gov
    • +1more
    Updated Mar 19, 2024
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    Stark County Ohio (2024). Stark Countywide Stormwater Discharge Points [Dataset]. https://portal-starkcountyohio.opendata.arcgis.com/datasets/stark-countywide-stormwater-discharge-points
    Explore at:
    Dataset updated
    Mar 19, 2024
    Dataset authored and provided by
    Stark County Ohio
    Area covered
    Description

    A combination of stormwater discharge point data throughout Stark County, Ohio. Discharge points are the point at which water exits part of the stormwater system, such as the outlet of a pipe or ditch. It includes outfalls defined under NPDES Phase II. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.

  18. d

    ScienceBase Item Summary Page

    • datadiscoverystudio.org
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    ScienceBase Item Summary Page [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/c411080a84034fbda913727107ac4d56/html
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    Area covered
    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  19. Number of WNV outbreaks and strains stored in the DMD database since 1994.

    • plos.figshare.com
    xls
    Updated Jun 18, 2023
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    Lara Savini; Susanna Tora; Alessio Di Lorenzo; Daniela Cioci; Federica Monaco; Andrea Polci; Massimiliano Orsini; Paolo Calistri; Annamaria Conte (2023). Number of WNV outbreaks and strains stored in the DMD database since 1994. [Dataset]. http://doi.org/10.1371/journal.pone.0196429.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 18, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Lara Savini; Susanna Tora; Alessio Di Lorenzo; Daniela Cioci; Federica Monaco; Andrea Polci; Massimiliano Orsini; Paolo Calistri; Annamaria Conte
    License

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

    Description

    Number of WNV outbreaks and strains stored in the DMD database since 1994.

  20. a

    sde SDE Hardeeville Crashes

    • hub.arcgis.com
    • data-hardeeville.opendata.arcgis.com
    Updated Mar 29, 2018
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    City of Hardeeville (2018). sde SDE Hardeeville Crashes [Dataset]. https://hub.arcgis.com/maps/Hardeeville::sde-sde-hardeeville-crashes
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    Dataset updated
    Mar 29, 2018
    Dataset authored and provided by
    City of Hardeeville
    Area covered
    Description

    Crashes within the City of Hardeeville, via SCDPH.

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Spatial Analysis Lab; Jarlath O'Neal-Dunne; Morgan Grove (2020). Geodatabase for the Baltimore Ecosystem Study Spatial Data [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F3120%2F150
Organization logo

Geodatabase for the Baltimore Ecosystem Study Spatial Data

Explore at:
Dataset updated
Apr 1, 2020
Dataset provided by
Long Term Ecological Research Networkhttp://www.lternet.edu/
Authors
Spatial Analysis Lab; Jarlath O'Neal-Dunne; Morgan Grove
Time period covered
Jan 1, 1999 - Jun 1, 2014
Area covered
Description

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

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