23 datasets found
  1. d

    Seattle Parks and Recreation GIS Map Layer Web Services URL - Golf Courses

    • catalog.data.gov
    • data.amerigeoss.org
    Updated Jan 31, 2025
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    data.seattle.gov (2025). Seattle Parks and Recreation GIS Map Layer Web Services URL - Golf Courses [Dataset]. https://catalog.data.gov/dataset/seattle-parks-and-recreation-gis-map-layer-web-services-url-golf-courses-5cda6
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    Dataset updated
    Jan 31, 2025
    Dataset provided by
    data.seattle.gov
    Area covered
    Seattle
    Description

    Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Golf Courses dataset.

  2. C

    Parks Boundaries

    • phoenixopendata.com
    • mapping-phoenix.opendata.arcgis.com
    • +2more
    Updated Apr 30, 2025
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    Enterprise (2025). Parks Boundaries [Dataset]. https://www.phoenixopendata.com/dataset/parks-boundaries
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    arcgis geoservices rest api, kml, csv, html, geojson, zipAvailable download formats
    Dataset updated
    Apr 30, 2025
    Dataset provided by
    City of Phoenix
    Authors
    Enterprise
    License

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

    Description

    ​Parks and Recreation

    Phoenix provides residents and visitors more than 41,000 acres of desert parks and mountain preserve land with more than 200 miles of trails; 185 parks; 32 community and recreation centers; eight golf courses; 29 pools where thousands cool off and learn to swim each summer; and classes, programs and sports leagues where kids and adults learn, stay active and have fun.

  3. T

    GIS dataset

    • dataverse-training.tdl.org
    • dataverse-dev.tdl.org
    bin, tiff
    Updated Oct 18, 2016
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    Kristi Park; Kristi Park (2016). GIS dataset [Dataset]. https://dataverse-training.tdl.org/dataset.xhtml?persistentId=doi:10.5072/FK2/AZCLXJ
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    tiff(13275561), bin(216), bin(387)Available download formats
    Dataset updated
    Oct 18, 2016
    Dataset provided by
    Texas Data Repository ***TRAINING*** Dataverse
    Authors
    Kristi Park; Kristi Park
    License

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

    Description

    GIS dataset with TIFFs and tsw files

  4. a

    Golf Courses

    • data-seattlecitygis.opendata.arcgis.com
    • s.cnmilf.com
    • +1more
    Updated Oct 2, 2023
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    City of Seattle ArcGIS Online (2023). Golf Courses [Dataset]. https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::golf-courses
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    Dataset updated
    Oct 2, 2023
    Dataset authored and provided by
    City of Seattle ArcGIS Online
    Area covered
    Description

    Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

  5. f

    Parks and Landmarks, Detroit

    • data.ferndalemi.gov
    • datasets.ai
    • +5more
    Updated Feb 12, 2014
    + more versions
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    Data Driven Detroit (2014). Parks and Landmarks, Detroit [Dataset]. https://data.ferndalemi.gov/datasets/D3::parks-and-landmarks-detroit/api
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    Dataset updated
    Feb 12, 2014
    Dataset authored and provided by
    Data Driven Detroit
    License

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

    Area covered
    Description

    Municipal Parks and Landmark features for the City of Detroit. Includes golf courses, City Airport and civic plazas. Does not include all school parks, unless they are maintained by Detroit Parks and Rec. This layer was developed by Data Driven Detroit by adding to a GIS file that was provided by the City in 2010. Some parks in the file may have been "abandoned" and are not maintained any longer.

  6. s

    Park Golf Course Rounds 2017 2018

    • opendata.suffolkcountyny.gov
    • hub.arcgis.com
    • +1more
    Updated Aug 12, 2022
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    Suffolk County GIS (2022). Park Golf Course Rounds 2017 2018 [Dataset]. https://opendata.suffolkcountyny.gov/datasets/park-golf-course-rounds-2017-2018/api
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    Dataset updated
    Aug 12, 2022
    Dataset authored and provided by
    Suffolk County GIS
    License

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

    Description

    Parks 2017 - 2018 Golf Rounds

  7. a

    Open Space By Type

    • data-cuyahoga.opendata.arcgis.com
    • geospatial.gis.cuyahogacounty.gov
    • +1more
    Updated Dec 27, 2019
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    Cuyahoga County (2019). Open Space By Type [Dataset]. https://data-cuyahoga.opendata.arcgis.com/datasets/cuyahoga::open-space-by-type/about
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    Dataset updated
    Dec 27, 2019
    Dataset authored and provided by
    Cuyahoga County
    Area covered
    Description

    Park: Publicly accessible park land. Compiled from various sources, including Cleveland Metroparks, Cuyahoga Valley National Park, and other local parks departments.

    -Golf Course -Cemetery -Other Public/Institutional

  8. GIS Shapefile - Soil, Sampling locations, Baltimore City

    • search.datacite.org
    • search.dataone.org
    • +1more
    Updated 2018
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    Cary Institute Of Ecosystem Studies; Jarlath O'Neil-Dunne; Morgan Grove (2018). GIS Shapefile - Soil, Sampling locations, Baltimore City [Dataset]. http://doi.org/10.6073/pasta/73499577fa03ee745fbcf56f98f0a73c
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    Dataset updated
    2018
    Dataset provided by
    DataCitehttps://www.datacite.org/
    Environmental Data Initiative
    Authors
    Cary Institute Of Ecosystem Studies; Jarlath O'Neil-Dunne; Morgan Grove
    Description

    Soil_Samples_BACI Available only by request on a case by case basis. Contact rthe author, David Nowak, at dnowak@fs.fed.us Tags Biophysical Resources, Land, Social Institutions, Health, BES, Soil, Lead, Sample, UFORE Summary Samples were taken to relate soil data to vegetation data obtained for the Urban Forestry Effects Model (UFORE). Description The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces. Credits Rich Pouyat, USDA Forest Service Use limitations Not for profit use only Extent West -76.711030 East -76.530612 North 39.371355 South 39.200686 Scale Range There is no scale range for this item. The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.

  9. Supplementary material 6 from: Seltmann K, Lafia S, Paul D, James S, Bloom...

    • zenodo.org
    • data.niaid.nih.gov
    bin
    Updated Jan 21, 2020
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    Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg; Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg (2020). Supplementary material 6 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449 [Dataset]. http://doi.org/10.3897/rio.4.e32449.suppl6
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 21, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg; Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg
    License

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

    Description

    Summary of topics to be covered in an ideal workshop as identified by workshop applicants in the workshop call for participation. We incorporated as many as possible that also fit our scope.

  10. Supplementary material 7 from: Seltmann K, Lafia S, Paul D, James S, Bloom...

    • zenodo.org
    • data.niaid.nih.gov
    bin
    Updated Jan 21, 2020
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    Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg; Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg (2020). Supplementary material 7 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449 [Dataset]. http://doi.org/10.3897/rio.4.e32449.suppl7
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    binAvailable download formats
    Dataset updated
    Jan 21, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg; Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg
    License

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

    Description

    This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.

  11. Supplementary material 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom...

    • zenodo.org
    pdf
    Updated Jul 25, 2024
    + more versions
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    Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg; Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg (2024). Supplementary material 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449 [Dataset]. http://doi.org/10.3897/rio.4.e32449.suppl3
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Jul 25, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg; Katja Seltmann; Sara Lafia; Deborah Paul; Shelley James; David Bloom; Nelson Rios; Shari Ellis; Una Farrell; Jessica Utrup; Michael Yost; Edward Davis; Rob Emery; Gary Motz; Julien Kimmig; Vaughn Shirey; Emily Sandall; Daniel Park; Christopher Tyrrell; R. Sean Thackurdeen; Matthew Collins; Vincent O'Leary; Heather Prestridge; Christopher Evelyn; Ben Nyberg
    License

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

    Description

    The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.

  12. a

    Golf Courses

    • opendata2-pbcgov.opendata.arcgis.com
    Updated Feb 6, 2018
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    Palm Beach County GIS (2018). Golf Courses [Dataset]. https://opendata2-pbcgov.opendata.arcgis.com/maps/golf-courses
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    Dataset updated
    Feb 6, 2018
    Dataset authored and provided by
    Palm Beach County GIS
    License

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

    Area covered
    Description

    This dataset identifies golf courses within Palm Beach County, FL, including both private and public facilities. It provides detailed information on their locations, boundaries, and attributes. The dataset is designed to support planning, analysis, and recreational management by offering a comprehensive inventory of golf course facilities throughout the county.

  13. n

    MapSAR Template Feature Layer

    • prep-response-portal.napsgfoundation.org
    • hub.arcgis.com
    Updated Oct 21, 2017
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    NAPSG Foundation (2017). MapSAR Template Feature Layer [Dataset]. https://prep-response-portal.napsgfoundation.org/maps/f412081560ec4074ac16e2161f7d5def
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    Dataset updated
    Oct 21, 2017
    Dataset authored and provided by
    NAPSG Foundation
    License

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

    Area covered
    Description

    IMPORTANT: This is the source of the feature layer template in the LearnArcGIS Lesson: Prepare for SAR Incidents and for the MapSAR Solution. If this layer is cloned or copied, the owner of the items needs to update the item details to reflect this. Purpose: This is a feature layer template for use in missing person search operations. It is based on the MapSAR (ArcGIS Desktop) Data Model but simplified for use in web maps and apps. Please see MapSAR GitHub for more information on this project.Maps are at the core of any Search and Rescue (SAR) operation. Geographic information system (GIS) software allows rescue personnel to quickly generate maps that depict specific aspects of the operation and show what is happening on the ground over time. The maps and operations data can be shared over a network to supply an enhanced common operating picture throughout the Incident Command Post (ICP). A team of GIS and SAR professionals from Sierra Madre Search and Rescue Team, Esri, Sequoia and Kings Canyon National Park, Yosemite National Park, Grand Canyon National Park, and the Mountaineer Rescue Group came together to develop the tools and instructions to fit established SAR workflows. The goal is to meet the critical need to provide standards, documents, and training to the international SAR community and establish more widespread and effective integration of GIS into operations.See Comments below for updates to the data model.

  14. d

    NREL GIS Data: Filtered Direct Normal Solar Resource for the Southwest...

    • datadiscoverystudio.org
    zip
    Updated Aug 29, 2017
    + more versions
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    (2017). NREL GIS Data: Filtered Direct Normal Solar Resource for the Southwest United States (1 Percent Slope and Less). [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/a5a453f80b45438794554d821fd98165/html
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 29, 2017
    Area covered
    United States
    Description

    description: Filtered (1% slope and less) direct normal solar resource data for the Southwest United States This data provides filtered solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. Exclusions: Contiguous Area 1km squared; US Census Urban Areas; MRLC Multi Resolution Land Characteristics Consortium http://www.mrlc.gov/; Argone National Lab ACEC Lands (Areas of Critical Environmental Concern) Federally Protected lands (FS - IRA (Inventoried Roadless Areas, FS - National Monument, FS - National Scenic Area, FS - Wilderness, FS - Wilderness Study Area, BLM - Wilderness, BLM - National Recreation Area, BLM - Forest Reserve, BLM - Wilderness Study Area, BLM - National Monument, BLM - National Conservation Area, FWS - National Wildlife Refuge, FWS - Waterfowl Production Area, FWS - Wildlife Management Area, FWS - Wilderness, FWS - Wilderness Study Area, FWS - Fish Hatchery, NPS - National Battlefield, NPS - National Battlefield Park, NPS - National Capital Park, NPS - National Historic Park, NPS - National Historic Site, NPS - National Lakeshore, NPS - National Mall, NPS - National Memorial, NPS - National Military Park, NPS - National Monument, NPS - National Park, NPS - National Parkway, NPS - National Preserve, NPS - National Recreation Area, NPS - National Reserve, NPS - National River, NPS - National Seashore, NPS - Wilderness, NPS - Wilderness Study Area, NPS - National Wild and Scenic River). ### License Info DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.; abstract: Filtered (1% slope and less) direct normal solar resource data for the Southwest United States This data provides filtered solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. Exclusions: Contiguous Area 1km squared; US Census Urban Areas; MRLC Multi Resolution Land Characteristics Consortium http://www.mrlc.gov/; Argone National Lab ACEC Lands (Areas of Critical Environmental Concern) Federally Protected lands (FS - IRA (Inventoried Roadless Areas, FS - National Monument, FS - National Scenic Area, FS - Wilderness, FS - Wilderness Study Area, BLM - Wilderness, BLM - National Recreation Area, BLM - Forest Reserve, BLM - Wilderness Study Area, BLM - National Monument, BLM - National Conservation Area, FWS - National Wildlife Refuge, FWS - Waterfowl Production Area, FWS - Wildlife Management Area, FWS - Wilderness, FWS - Wilderness Study Area, FWS - Fish Hatchery, NPS - National Battlefield, NPS - National Battlefield Park, NPS - National Capital Park, NPS - National Historic Park, NPS - National Historic Site, NPS - National Lakeshore, NPS - National Mall, NPS - National Memorial, NPS - National Military Park, NPS - National Monument, NPS - National Park, NPS - National Parkway, NPS - National Preserve, NPS - National Recreation Area, NPS - National Reserve, NPS - National River, NPS - National Seashore, NPS - Wilderness, NPS - Wilderness Study Area, NPS - National Wild and Scenic River). ### License Info DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

  15. a

    Parks

    • sedgwick-county-gis-sedgwickcounty.hub.arcgis.com
    Updated Aug 29, 2023
    + more versions
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    SedgwickCounty-GIS (2023). Parks [Dataset]. https://sedgwick-county-gis-sedgwickcounty.hub.arcgis.com/datasets/86d704c66dfe41649226181e2e53a1a0
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    Dataset updated
    Aug 29, 2023
    Dataset authored and provided by
    SedgwickCounty-GIS
    Area covered
    Description

    Golf Courses and Parks with in Sedgwick CountyThe golf course layer contains polygon features representing County, City, and privately owned golf courses existing throughout Sedgwick County and it's constituent jurisdictions.The park layer contains polygon features representing County and City parks throughout Sedgwick County and it's constituent jurisdictions.

  16. K

    Prince William County, Virginia Golf Courses

    • koordinates.com
    csv, dwg, geodatabase +6
    Updated Feb 21, 2019
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    Prince William County, Virginia (2019). Prince William County, Virginia Golf Courses [Dataset]. https://koordinates.com/layer/99573-prince-william-county-virginia-golf-courses/
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    mapinfo tab, mapinfo mif, pdf, csv, shapefile, geodatabase, geopackage / sqlite, dwg, kmlAvailable download formats
    Dataset updated
    Feb 21, 2019
    Dataset authored and provided by
    Prince William County, Virginia
    Area covered
    Description

    Geospatial data about Prince William County, Virginia Golf Courses. Export to CAD, GIS, PDF, CSV and access via API.

  17. B

    Toronto Land Use Spatial Data - parcel-level - (2019-2021)

    • borealisdata.ca
    • dataone.org
    Updated Feb 23, 2023
    + more versions
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    Marcel Fortin (2023). Toronto Land Use Spatial Data - parcel-level - (2019-2021) [Dataset]. http://doi.org/10.5683/SP3/1VMJAG
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 23, 2023
    Dataset provided by
    Borealis
    Authors
    Marcel Fortin
    License

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

    Area covered
    Toronto
    Description

    Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

  18. a

    Gypsy Hill Park Golf Course

    • gis-data-stauntonva.hub.arcgis.com
    Updated Dec 13, 2022
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    StauntonGIS (2022). Gypsy Hill Park Golf Course [Dataset]. https://gis-data-stauntonva.hub.arcgis.com/items/ecc108349b95445fa173857a2cf8c1e9
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    Dataset updated
    Dec 13, 2022
    Dataset authored and provided by
    StauntonGIS
    Area covered
    Description

    City of Staunton Gypsy Hill Park Golf Course.

  19. a

    Park Locations

    • hub.arcgis.com
    Updated Apr 6, 2021
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    City of West Palm Beach (2021). Park Locations [Dataset]. https://hub.arcgis.com/maps/31d9578eb97546a0998ef49d14a3abd8
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    Dataset updated
    Apr 6, 2021
    Dataset authored and provided by
    City of West Palm Beach
    License

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

    Area covered
    Description

    Purpose: West Palm Beach Parks enhance the quality of life for our family of citizens within our available means by providing and facilitating safe, quality services, programs and facilities, while preserving our history and protecting our natural resources. Our Parks are classified as metropolitan, community, or neighborhood parks. Display locations of West Palm Beach Parks and Public Golf coursesIntended Use: Currently used for locational purpose in Park Locator and West Palm Beach Parks StorymapDepartment: Parks and It-GISData Source: SDE Layer in the Parks datasetHow was the Data Manipulated: Geocoded by address aligned with aerial photography.How the Data is Modified: Input from Parks Department and edits preformed by GIS staffUpdate Frequency: As-NeededNote: Source data for Golf Courses is Palm Beach County GOLFCOURSE layer. The data was geoprocessed from polygon to point is staging db and a queried was applied for loading private and public courses into the hosted feature service in March 2020. The data should be updated as needed as of March 2020 there are 106 courses.

  20. a

    Midvale City Disc Golf Course

    • midvale-city-gis-department-midvale.opendata.arcgis.com
    Updated Jul 22, 2024
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    Midvale City (2024). Midvale City Disc Golf Course [Dataset]. https://midvale-city-gis-department-midvale.opendata.arcgis.com/datasets/midvale-city-disc-golf-course-1
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    Dataset updated
    Jul 22, 2024
    Dataset authored and provided by
    Midvale City
    Area covered
    Midvale
    Description

    Right to left draw - 260 feet

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data.seattle.gov (2025). Seattle Parks and Recreation GIS Map Layer Web Services URL - Golf Courses [Dataset]. https://catalog.data.gov/dataset/seattle-parks-and-recreation-gis-map-layer-web-services-url-golf-courses-5cda6

Seattle Parks and Recreation GIS Map Layer Web Services URL - Golf Courses

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Dataset updated
Jan 31, 2025
Dataset provided by
data.seattle.gov
Area covered
Seattle
Description

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Golf Courses dataset.

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