100+ datasets found
  1. d

    Data and Results for GIS-Based Identification of Areas that have Resource...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Oct 2, 2025
    + more versions
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    U.S. Geological Survey (2025). Data and Results for GIS-Based Identification of Areas that have Resource Potential for Lode Gold in Alaska [Dataset]. https://catalog.data.gov/dataset/data-and-results-for-gis-based-identification-of-areas-that-have-resource-potential-for-lo
    Explore at:
    Dataset updated
    Oct 2, 2025
    Dataset provided by
    U.S. Geological Survey
    Description

    This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

  2. M

    Geodatabase to Shapefile Warning Tool

    • gisdata.mn.gov
    esri_toolbox
    Updated Apr 1, 2025
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    University of Minnesota (2025). Geodatabase to Shapefile Warning Tool [Dataset]. https://gisdata.mn.gov/dataset/gdb-to-shp-warning-tool
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    esri_toolboxAvailable download formats
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    University of Minnesota
    Description

    The Geodatabase to Shapefile Warning Tool examines feature classes in input file geodatabases for characteristics and data that would be lost or altered if it were transformed into a shapefile. Checks include:
    1) large files (feature classes with more than 255 fields or over 2GB), 2) field names longer than 10 characters
    string fields longer than 254 characters, 3) date fields with time values 4) NULL values, 5) BLOB, guid, global id, and raster field types, 6) attribute domains or subtypes, and 7) annotation or topology

    The results of this inspection are written to a text file ("warning_report_[geodatabase_name]") in the directory where the geodatabase is located. A section at the top provides a list of feature classes and information about the geodatabase as a whole. The report has a section for each valid feature class that returned a warning, with a summary of possible warnings and then more details about issues found.

    The tool can process multiple file geodatabases at once. A separate text file report will be created for each geodatabase. The toolbox was created using ArcGIS Pro 3.7.11.

    For more information about this and other related tools, explore the Geospatial Data Curation toolkit

  3. c

    2019 Annual Land Use (Download in file-GDB format only)

    • hub.scag.ca.gov
    • hub.arcgis.com
    Updated Feb 10, 2022
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    rdpgisadmin (2022). 2019 Annual Land Use (Download in file-GDB format only) [Dataset]. https://hub.scag.ca.gov/datasets/ea9fda878c1947d2afac5142fd5cb658
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    Dataset updated
    Feb 10, 2022
    Dataset authored and provided by
    rdpgisadmin
    License

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

    Area covered
    Description

    "Due to the size of this dataset, both Shapefile and Spreadsheet download options will not work as expected. The File Geodatabase is an alternative option for this data download"This is SCAG's 2019 Annual Land Use (ALU v. 2019.1) at the parcel-level, updated as of February 2021. This dataset has been modified to include additional attributes in order to feed SCAG's Housing Element Parcel Tool (HELPR), version 2.0. The dataset will be further reviewed and updated as additional information is released. Please refer to the tables below for data dictionary and SCAG’s land use classification.Field NameData TypeField DescriptionPID19Text2019 SCAG’s parcel unique IDAPN19Text2019 Assessor’s parcel numberCOUNTYTextCounty name (based on 2016 county boundary)COUNTY_IDDoubleCounty FIPS code (based on 2016 county boundary)CITYTextCity name (based on 2016 city boundary)CITY_IDDoubleCity FIPS code (based on 2016 city boundary)MULTIPARTShort IntegerMultipart feature (the number of multiple polygons; '1' = singlepart feature)STACKLong IntegerDuplicate geometry (the number of duplicate polygons; '0' = no duplicate polygons)ACRESDoubleParcel area (in acreage)GEOID20Text2020 Census Block Group GEOIDSLOPEShort IntegerSlope information1APN_DUPLong IntegerDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIODoubleRatio of improvement assessed value to land assessed valueLU19Text2019 existing land useLU19_SRCTextSource of 2019 existing land use2SCAGUID16Text2016 SCAG’s parcel unique IDAPNText2016 Assessor’s parcel numberCITY_GP_COText2016 Jurisdiction’s general plan land use designationSCAG_GP_COText2016 SCAG general plan land use codeSP_INDEXShort IntegerSpecific plan index ('0' = outside specific plan area; '1' = inside specific plan area)CITY_SP_COText2016 Jurisdiction’s specific plan land use designationSCAG_SP_COText2016 SCAG specific plan land use codeCITY_ZN_COText2016 Jurisdiction’s zoning codeSCAG_ZN_COText2016 SCAG zoning codeLU16Text2016 existing land useYEARLong IntegerDataset yearPUB_OWNShort IntegerPublic-owned land index ('1' = owned by public agency)PUB_NAMETextName of public agencyPUB_TYPETextType of public agency3BF_SQFTDoubleBuilding footprint area (in square feet)4BSF_NAMETextName of brownfield/superfund site5BSF_TYPETextType of brownfield/superfund site5FIREShort IntegerParcel intersects CalFire Very High Hazard Local Responsibility Areas or State Responsibility Areas (November 2020 version) (CalFIRE)SEARISE36Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)1 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)SEARISE72Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)2 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)FLOODShort IntegerParcel intersects with a FEMA 100 Year Flood Plain data from the Digital Flood Insurance Rate Map (DFIRM), obtained from Federal Emergency Management Agency (FEMA) in August 10, 2017EQUAKEShort IntegerParcel intersects with an Alquist-Priolo Earthquake Fault Zone (California Geological Survey; 2018)LIQUAFAShort IntegerParcel intersects with a Liquefaction Susceptibility Zone (California Geological Survey; 2016)LANDSLIDEShort IntegerParcel intersects with a Landslide Hazard Zone (California Geological Survey; 2016)CPADShort IntegerParcel intersects with a protected area from the California Protected Areas Database(CPAD) – www.calands.org (accessed April 2021)RIPARIANShort IntegerParcel centroid falls within Active River Areas(2010)or parcel intersects with a Wetland Area in the National Wetland Inventory(Version 2)WILDLIFEShort IntegerParcel intersects with wildlife habitat (US Fish & Wildlife ServiceCritical Habitat, Southern California Missing Linkages, Natural Lands & Habitat Corridors from Connect SoCal, CEHC Essential Connectivity Areas,Critical Coastal Habitats)CNDDBShort IntegerThe California Natural Diversity Database (CNDDB)includes the status and locations of rare plants and animals in California. Parcels that overlap locations of rare plants and animals in California from the California Natural Diversity Database (CNDDB)have a greater likelihood of encountering special status plants and animals on the property, potentially leading to further legal requirements to allow development (California Department of Fish and Wildlife). Data accessed in October 2020.HCPRAShort IntegerParcel intersects Natural Community & Habitat Conservation Plans Reserve Designs from the Western Riverside MHSCP, Coachella Valley MHSCP, and the Orange County Central Coastal NCCP/HCP, as accessed in October 2020WETLANDShort IntegerParcel intersects a wetland or deepwater habitat as defined by the US Fish & Wildlife Service National Wetlands Inventory, Version 2.UAZShort IntegerParcel centroid lies within a Caltrans Adjusted Urbanized AreasUNBUILT_SFDoubleDifference between parcel area and building footprint area expressed in square feet.6GRCRY_1MIShort IntegerThe number of grocery stores within a 1-mile drive7HEALTH_1MIShort IntegerThe number of healthcare facilities within a 1-mile drive7OPENSP_1MIShort IntegerQuantity of open space (roughly corresponding to city blocks’ worth) within a 1-mile drive7TCAC_2021TextThe opportunity level based on the 2021 CA HCD/TCAC opportunity scores.HQTA45Short IntegerField takes a value of 1 if parcel centroid lies within a 2045 High-Quality Transit Area (HQTA)JOB_CTRShort IntegerField takes a value of 1 if parcel centroid lies within a job centerNMAShort IntegerField takes a value of 1 if parcel centroid lies within a neighborhood mobility area.ABS_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within an absolute constraint area. See the Sustainable Communities Strategy Technical Reportfor details.VAR_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within a variable constraint area. See the Sustainable Communities Strategy Technical Reportfor details.EJAShort IntegerField takes a value of 1 if parcel centroid lies within an Environmental Justice Area. See the Environmental Justice Technical Reportfor details.SB535Short IntegerField takes a value of 1 if parcel centroid lies within an SB535 Disadvantaged Community area. See the Environmental Justice Technical Reportfor details.COCShort IntegerField takes a value of 1 if parcel centroid lies within a Community of Concern See the Environmental Justice Technical Reportfor details.STATEShort IntegerThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)8.SBShort IntegerIndex of ADU eligibility according to the setback reduction policy scenario (from 4 to 2 feet) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SMShort IntegerIndex of ADU eligibility according to the small ADU policy scenario (from 800 to 600 square feet ADU) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)PKShort IntegerIndex of ADU eligibility according to parking space exemption (200 square feet) policy scenario (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SMShort IntegerIndex of ADU eligibility according to both the setback reduction and small ADU policy scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_PKShort IntegerIndex of ADU eligibility according to both the setback reduction and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SM_PKShort IntegerIndex of ADU eligibility according to both the small ADU policy and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SM_PKShort IntegerIndex of ADU eligibility according to the setback reduction, small ADU, and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. Source of 2019 existing land use: SCAG_REF- SCAG's regional geospatial datasets;ASSESSOR- Assessor's 2019 tax roll records; CPAD- California Protected Areas Database (version 2020a; accessed in September 2020); CSCD- California School Campus Database (version 2018; accessed in September 2020); FMMP- Farmland Mapping and Monitoring Program's Important Farmland GIS data (accessed in September 2020); MIRTA- U.S. Department of Defense's Military Installations, Ranges, and Training Areas GIS data (accessed in September 2020)3. Type of public agency includes federal, state, county, city, special district, school district, college/university, military.4. Based on 2019 building footprint data obtained from BuildingFootprintUSA (except that 2014 building footprint data was used for Imperial County). Please note that 2019 building footprint data does not cover the entire SCAG region (overlapped with 83% of parcels in the SCAG Region).5. Includes brownfield/superfund site whose address information are matched by SCAG rooftop address locator. Brownfield data was obtained from EPA's Assessment, Cleanup and Redevelopment Exchange System (ACRES) database, Cleanups in my community (CIMC), DTSC brownfield Memorandum of Agreement (MOA). Superfund site data was obtained from EPA's Superfund Enterprise Management System (SEMS) database.6. Parcels with a zero value for building footprint area are marked as NULL to indicate this field is not reliable.7. These values are intended as a rudimentary indicator of accessibility developed by SCAG using 2016 InfoUSA business establishment data and 2017 California Protected Areas data. See documentation for details.8. A detailed study conducted by Cal Poly Pomona (CPP) and available hereconducted an extensive review of state and local requirements and development trends for ADUs in the SCAG region and developed a baseline set of assumptions for estimating how many of a jurisdiction’s parcels

  4. d

    Texas-Harvey Basemap - Addresses and Boundaries

    • search.dataone.org
    • hydroshare.org
    • +1more
    Updated Dec 30, 2023
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    David Arctur; David Maidment (2023). Texas-Harvey Basemap - Addresses and Boundaries [Dataset]. http://doi.org/10.4211/hs.3e251d7d70884abd928d7023e050cbdc
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    Dataset updated
    Dec 30, 2023
    Dataset provided by
    Hydroshare
    Authors
    David Arctur; David Maidment
    Area covered
    Description

    This site provides access to download an ArcGIS geodatabase or shapefiles for the 2017 Texas Address Database, compiled by the Center for Water and the Environment (CWE) at the University of Texas at Austin, with guidance and funding from the Texas Division of Emergency Management (TDEM). These addresses are used by TDEM to help anticipate potential impacts of serious weather and flooding events statewide. This is part of the Texas Water Model (TWM), a project to adapt the NOAA National Water Model [1] for use in Texas public safety. This database was compiled over the period from June 2016 to December 2017. A number of gaps remain (towns and cities missing address points), see Address Database Gaps spreadsheet below [4]. Additional datasets include administrative boundaries for Texas counties (including Federal and State disaster-declarations), Councils of Government, and Texas Dept of Public Safety Regions. An Esri ArcGIS Story Map [5] web app provides an interactive map-based portal to explore and access these data layers for download.

    The address points in this database include their "height above nearest drainage" (HAND) as attributes in meters and feet. HAND is an elevation model developed through processing by the TauDEM method [2], built on USGS National Elevation Data (NED) with 10m horizontal resolution. The HAND elevation data and 10m NED for the continental United States are available for download from the Texas Advanced Computational Center (TACC) [3].

    The complete statewide dataset contains about 9.28 million address points representing a population of about 28 million. The total file size is about 5GB in shapefile format. For better download performance, the shapefile version of this data is divided into 5 regions, based on groupings of major watersheds identified by their hydrologic unit codes (HUC). These are zipped by region, with no zipfile greater than 120mb: - North Tx: HUC1108-1114 (0.52 million address points) - DFW-East Tx: HUC1201-1203 (3.06 million address points) - Houston-SE Tx: HUC1204 (1.84 million address points) - Central Tx: HUC1205-1210 (2.96 million address points) - Rio Grande-SW Tx: HUC2111-1309 (2.96 million address points)

    Additional state and county boundaries are included (Louisiana, Mississippi, Arkansas), as well as disaster-declaration status.

    Compilation notes: The Texas Commission for State Emergency Communications (CSEC) provided the first 3 million address points received, in a single batch representing 213 of Texas' 254 counties. The remaining 41 counties were primarily urban areas comprising about 6.28 million addresses (totaling about 9.28 million addresses statewide). We reached the GIS data providers for these areas (see Contributors list below) through these emergency communications networks: Texas 9-1-1 Alliance, the Texas Emergency GIS Response Team (EGRT), and the Texas GIS 9-1-1 User Group. The address data was typically organized in groupings of counties called Councils of Governments (COG) or Regional Planning Commissions (RPC) or Development Councils (DC). Every county in Texas belongs to a COG, RPC or DC. We reconciled all counties' addresses to a common, very simple schema, and merged into a single geodatabase.

    November 2023 updates: In 2019, TNRIS took over maintenance of the Texas Address Database, which is now a StratMap program updated annually [6]. In 2023, TNRIS also changed its name to the Texas Geographic Information Office (TxGIO). The datasets available for download below are not being updated, but are current as of the time of Hurricane Harvey.

    References: [1] NOAA National Water Model [https://water.noaa.gov/map] [2] TauDEM Downloads [https://hydrology.usu.edu/taudem/taudem5/downloads.html] [3] NFIE Continental Flood Inundation Mapping - Data Repository [https://web.corral.tacc.utexas.edu/nfiedata/] [4] Address Database Gaps, Dec 2017 (download spreadsheet below) [5] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/] [6] TNRIS/TxGIO StratMap Address Points data downloads [https://tnris.org/stratmap/address-points/]

  5. Wetlands - Forests Practices Regulation

    • data-wadnr.opendata.arcgis.com
    • geo.wa.gov
    Updated Jan 31, 2017
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    Washington State Department of Natural Resources (2017). Wetlands - Forests Practices Regulation [Dataset]. https://data-wadnr.opendata.arcgis.com/items/02b250843e44485ea7d736b34fa80998
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    Dataset updated
    Jan 31, 2017
    Dataset authored and provided by
    Washington State Department of Natural Resourceshttps://dnr.wa.gov/
    Area covered
    Description

    Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

    The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
    L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

    • indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

    DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

    WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

    • NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.
  6. Surface Drinking Water Importance (Feature Layer)

    • agdatacommons.nal.usda.gov
    bin
    Updated Oct 31, 2024
    + more versions
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    U.S. Forest Service (2024). Surface Drinking Water Importance (Feature Layer) [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Surface_Drinking_Water_Importance_Feature_Layer_/25973593
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    binAvailable download formats
    Dataset updated
    Oct 31, 2024
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    U.S. Forest Service
    License

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

    Description

    Note: This is a large dataset. To download, go to�ArcGIS Open Data Set�and click the download button, and under additional resources select the shapefile or geodatabase option. This dataset provides a watershed index of surface drinking water importance, a watershed index of forest importance to surface drinking water, and a watershed index to highlight the extent to which development, fire, and insects and disease threaten forests important for surface drinking water. This tabular dataset is meant to be joined with the NRCS Watershed Boundary Dataset HUC-12.�MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService Geodatabase Download Shapefile Download CSV GeoJSON Shapefile KML For complete information, please visit https://data.gov.

  7. Statewide Crop Mapping

    • data.cnra.ca.gov
    data, gdb, html +3
    Updated Mar 3, 2025
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    California Department of Water Resources (2025). Statewide Crop Mapping [Dataset]. https://data.cnra.ca.gov/dataset/statewide-crop-mapping
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    rest service, data, zip(179113742), gdb(86655350), shp(126828193), shp(126548912), gdb(86886429), gdb(85891531), gdb(76631083), zip(144060723), shp(107610538), zip(140021333), zip(88308707), zip(189880202), html, zip(94630663), zip(159870566), zip(169400976), zip(98690638)Available download formats
    Dataset updated
    Mar 3, 2025
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    Description

    NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

    Thank you for your interest in DWR land use datasets.

    The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

    Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

    For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

    For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

    For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

    Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

  8. d

    Primary model outputs (packaged datasets) - A landscape connectivity...

    • catalog.data.gov
    Updated Oct 5, 2025
    + more versions
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    U.S. Fish and Wildlife Service (2025). Primary model outputs (packaged datasets) - A landscape connectivity analysis for the coastal marten (Martes caurina humboldtensis) [Dataset]. https://catalog.data.gov/dataset/primary-model-outputs-packaged-datasets-a-landscape-connectivity-analysis-for-the-coastal-
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    Dataset updated
    Oct 5, 2025
    Dataset provided by
    U.S. Fish and Wildlife Service
    Description

    This packaged data collection contains all of the outputs from our primary model, including the following data layers: Habitat Cores (vector polygons) Least-cost Paths (vector lines) Least-cost Corridors (raster) Least-cost Corridors (vector polygon interpretation) Modeling Extent (vector polygon) Please refer to the embedded spatial metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in shapefile (.shp) or raster GeoTIFF (.tif) formats.

  9. M

    National Wetland Inventory for Minnesota

    • gisdata.mn.gov
    fgdb, gpkg, html +1
    Updated Mar 29, 2024
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    Natural Resources Department (2024). National Wetland Inventory for Minnesota [Dataset]. https://gisdata.mn.gov/dataset/water-nat-wetlands-inv-2009-2014
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    gpkg, fgdb, jpeg, htmlAvailable download formats
    Dataset updated
    Mar 29, 2024
    Dataset provided by
    Natural Resources Department
    Area covered
    Minnesota
    Description

    National Wetland Inventory (NWI) data for Minnesota provide information on the location, extent, and type of Minnesota wetlands. Natural resource managers use NWI data to improve the management, protection, and restoration of wetlands. Wetlands provide many ecological benefits including habitat for fish and wildlife, reducing floods, recharging, improving water quality, and supporting recreation.

    These data were updated through a decade-long, multi-agency collaborative effort under leadership of the Minnesota Department of Natural Resources (MNDNR). Major funding was provided by the Environmental and Natural Resources Trust Fund.

    This is the first statewide update of the NWI for Minnesota since the original inventory in the mid-1980s. The work was completed in phases by dividing the state into five project areas. Those project areas have all been edgematched into a final seamless statewide dataset.

    Ducks Unlimited (Ann Arbor, MI) and St. Mary’s University Geospatial Services (Winona, MN) conducted the wetland mapping and classification under contract to the MNDNR. The Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota provided support for methods development and field validation. The DNR Resource Assessment Office provided additional support for data processing, field checking, and quality control review.

    The updated NWI data delineate and classify wetlands according to the system developed by Cowardin et al. (1979), which is consistent with the original NWI. The updated data also contain a simplified plant community classification (SPCC) and a simplified hydrogeomorphic (HGM) classification. Quality assurance of the data included visual inspection, automated checks for attribute validity and topologic consistency, as well as a formal accuracy assessment based on an independent field verified data set. Further details on the methods employed can be found in the technical procedures document for this project located on the project website (http://www.dnr.state.mn.us/eco/wetlands/nwi_proj.html ).

    DOWNLOAD NOTE: NWI data are only provided in either ESRI File Geodatabase or OGC GeoPackage formats. A Shapefile is not available because the size of the NWI dataset exceeds the limit for that format. If you are unable to use the File Geodatabase or GeoPackage, you can view data through Wetland Finder, an interactive mapping application on the DNR’s website (https://arcgis.dnr.state.mn.us/ewr/wetlandfinder ).

    SYMBOLOGY NOTE: The ESRI File Geodatabase download includes four layer files that symbolize the data using four different wetland classification systems. The symbology layer files for the Cowardin class and the simplified HGM class are grouped into a smaller number of classes than the full elaborated classifications. Detail is available in the Minnesota Wetland Inventory User Guide and Summary Statistics report (https://files.dnr.state.mn.us/eco/wetlands/nwi-user-guide.pdf ). The layer files for these data have been set up to restrict drawing of the data when zoomed out beyond 1:250,000 scale. This is, in part, to prevent problems with slow performance with this large dataset.

  10. Public Land Survey Corner (Feature Layer)

    • agdatacommons.nal.usda.gov
    • catalog.data.gov
    • +3more
    bin
    Updated Jun 21, 2025
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    U.S. Forest Service (2025). Public Land Survey Corner (Feature Layer) [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Public_Land_Survey_Corner_Feature_Layer_/25974256
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    binAvailable download formats
    Dataset updated
    Jun 21, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    U.S. Forest Service
    License

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

    Description

    Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. A land survey point from a GCDB LX file, survey plat, or captured from a CFF land net coverage. Includes points generated by calculating an aliquot breakdown of a section.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML Geodatabase Download Shapefile Download For complete information, please visit https://data.gov.

  11. Activity FACTS Common Attributes (Feature Layer)

    • agdatacommons.nal.usda.gov
    • catalog.data.gov
    • +6more
    bin
    Updated Sep 22, 2025
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    U.S. Forest Service (2025). Activity FACTS Common Attributes (Feature Layer) [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Activity_FACTS_Common_Attributes_Feature_Layer_/25974223
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    binAvailable download formats
    Dataset updated
    Sep 22, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    U.S. Forest Service
    License

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

    Description

    The data in this map service is updated every weekend.Note: This data includes all activities regardless of whether there is a spatial feature attached.Note: This is a large dataset. Metadata and Downloads are available at: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FACTS+common+attributesTo download FACTS activities layers, search for the activity types you want, such as timber harvest or hazardous fuels treatments. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. This feature class contains the FACTS attributes most commonly needed to describe FACTS activities.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityFactsCommonAttributes_01/MapServer/0 Geodatabase Download Shapefile Download For complete information, please visit https://data.gov.

  12. v

    Vermont National Wetlands Inventory Download

    • geodata.vermont.gov
    • anrgeodata.vermont.gov
    • +3more
    Updated Apr 12, 2023
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    Vermont Agency of Natural Resources (2023). Vermont National Wetlands Inventory Download [Dataset]. https://geodata.vermont.gov/documents/6e1bd6ce370e4704a63f98dda7fcb913
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    Dataset updated
    Apr 12, 2023
    Dataset authored and provided by
    Vermont Agency of Natural Resources
    Area covered
    Vermont
    Description

    Please note that NWI data is continuously being improved and new data is added on a biannual basis. Those updates are reflected on the Wetlands Mapper and in the data downloads in October and May of each year. To ensure that you have the most up to date information, please refer to the published date in the metadata, the location of new data on the Projects Mapper and download new data regularly.Each State data download is available as either a compressed file Geodatabase or a Shapefile (PDF). Both files are compressed by using the .zip format and contain the following layers:Wetlands polygon data - Version 2Wetlands Project Metadata - Version 2 (includes image dates and project information)Wetlands Historic Map Information*Riparian polygon data*Riparian Project Metadata (includes image dates and project information)*Historic Wetlands*Historic Wetlands Project Metadata (includes image dates and project information)*Watershed Boundary Dataset (WBD) HUC8 modified*** If available at the requested location.** Not include in State downloads.The state downloads include a Wetlands Project Metadata layer that identifies where and when wetlands were mapped within the state.NOTE: Due to the variation in use and analysis of this data by the end user, each of states wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.

  13. Land Use/Land Cover of New Jersey 2012 Generalized (Download)

    • njogis-newjersey.opendata.arcgis.com
    Updated Feb 17, 2015
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    NJDEP Bureau of GIS (2015). Land Use/Land Cover of New Jersey 2012 Generalized (Download) [Dataset]. https://njogis-newjersey.opendata.arcgis.com/documents/d7b358c0ea384cdab8040bc25a9bf59c
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    Dataset updated
    Feb 17, 2015
    Dataset provided by
    New Jersey Department of Environmental Protectionhttp://www.nj.gov/dep/
    Authors
    NJDEP Bureau of GIS
    Area covered
    New Jersey
    Description

    Please note that this file is large, ~550 MB, and may take a substantial amount of time to download especially on slower internet connections.Shapefile (NJ State Plane NAD 1983) download: Click "Open" or Click hereFile Geodatabase (NJ State Plane NAD 1983) download: Click hereThis data represents a "generalized" version of the 2012 LULC. To improve the performance of the web applications displaying the 2012 land use data, it was necessary to create a new simplified layer that included only the minimum number of polygons and attributes needed to represent the 2012 land use conditions. The 2012 LU/LC data set is the fifth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007 and 2012. This present 2012 update was created by comparing the 2007 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2012 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2012 land use directly to the base data layer. All 2007 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2007-2012 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2007. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2012 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

  14. Digital Geologic-GIS Map of Santa Rosa Island, California (NPS, GRD, GRI,...

    • catalog.data.gov
    • s.cnmilf.com
    Updated Oct 5, 2025
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    National Park Service (2025). Digital Geologic-GIS Map of Santa Rosa Island, California (NPS, GRD, GRI, CHIS, SRIS digital map) adapted from a American Association of Petroleum Geologists Field Trip Guidebook map by Sonneman, as modified and extend by Weaver, Doerner, Avila and others (1969) [Dataset]. https://catalog.data.gov/dataset/digital-geologic-gis-map-of-santa-rosa-island-california-nps-grd-gri-chis-sris-digital-map
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    Dataset updated
    Oct 5, 2025
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Area covered
    Santa Rosa Island, California
    Description

    The Digital Geologic-GIS Map of Santa Rosa Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sris_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sris_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sris_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sris_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sris_geology_metadata.txt or sris_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

  15. Knutson-Vandenberg (Feature Layer)

    • agdatacommons.nal.usda.gov
    • datasets.ai
    • +6more
    bin
    Updated Sep 22, 2025
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    U.S. Forest Service (2025). Knutson-Vandenberg (Feature Layer) [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Knutson-Vandenberg_Feature_Layer_/25974211
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    binAvailable download formats
    Dataset updated
    Sep 22, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    U.S. Forest Service
    License

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

    Description

    Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. The Knutson-Vandenberg Act (K-V) of June 9, 1930 (16 U.S.C. 576-576b; 46 Stat. 527), as amended by the National Forest Management Act of October 22, 1976 (16 U.S.C. 1600 et seq.) authorized collection of deposits from federal timber purchasers for prompt and efficient use of funds to reestablish, protect, and improve the production of renewable resources on timber sale areas. This includes performing soil improvement and watershed restoration, wildlife habitat improvement, control of insects, disease, and noxious weeds, tree planting, seeding and other cultural treatments necessary to maintain and improve land productivity. Since its creation millions of acres of National Forest System lands (NFS) have been treated and restored to resilient conditions and terrestrial and aquatic habitat improved. Public Law 109-54 of August 2, 2005, Title IV General Provisions, Sec 412 further amended the K-V Act to allow the collection and use of CWKV funds for watershed restoration, wildlife habitat improvement, to prepare timber sales, control of insects, disease, and noxious weeds, fire community protection activities, and the maintenance of forest roads within the Forest Service region in which the timber sale occurred. Provided that such activities may be performed through the use of contracts, forest product sales, and cooperative agreements. Note that these activities are to be performed by contract and not Forest Service personnel. The Forest Service used this amendment to administratively create two K-V programs within the K-V fund; CWKV (Cooperative Work, Knutson-Vandenberg, Sale Area Projects) and CWK2 (Cooperative Work, Knutson-Vandenberg, Regional Projects). This layer shows the spatial representation where activities accomplished and funded with CWKV and CWK2 funds and reported through the Forest Service Activity Tracking System (FACTS) database. It is important to note that this layer may not contain all CWKV or CWK2 accomplished activities; the spatial portion of the activity description is not currently enforced by FACTS and at this time some are optionally reported by Forest Service units. As spatial data reporting is enforced by the application and acceptant of reporting both tabular and spatial we hope to improve the quality and comprehensiveness of the data used for this layer in coming years. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML Geodatabase Download Shapefile Download For complete information, please visit https://data.gov.

  16. a

    2 Foot Contours

    • njogis-newjersey.opendata.arcgis.com
    • hub.arcgis.com
    Updated Oct 18, 2024
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    Monmouth County NJ GIS (2024). 2 Foot Contours [Dataset]. https://njogis-newjersey.opendata.arcgis.com/datasets/8c7faafbee524234bed84d1591503077
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    Dataset updated
    Oct 18, 2024
    Dataset authored and provided by
    Monmouth County NJ GIS
    Area covered
    Description

    This data set was originally created as part of the Monmouth County Contour Database (2003). It is a line-based feature class that comprised of modeled 2' contours. For fastest download choose File Geodatabase (fgdb) or Spreadsheet. Due to the dataset's large size, the entirety of the contour GIS layer cannot be downloaded as a single shapefile. If you require a shapefile, please use the Search/Filter Tools to create a subset of the data that will be small enough to download in shapefile format.

  17. Global map of tree density

    • figshare.com
    zip
    Updated May 31, 2023
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    Crowther, T. W.; Glick, H. B.; Covey, K. R.; Bettigole, C.; Maynard, D. S.; Thomas, S. M.; Smith, J. R.; Hintler, G.; Duguid, M. C.; Amatulli, G.; Tuanmu, M. N.; Jetz, W.; Salas, C.; Stam, C.; Piotto, D.; Tavani, R.; Green, S.; Bruce, G.; Williams, S. J.; Wiser, S. K.; Huber, M. O.; Hengeveld, G. M.; Nabuurs, G. J.; Tikhonova, E.; Borchardt, P.; Li, C. F.; Powrie, L. W.; Fischer, M.; Hemp, A.; Homeier, J.; Cho, P.; Vibrans, A. C.; Umunay, P. M.; Piao, S. L.; Rowe, C. W.; Ashton, M. S.; Crane, P. R.; Bradford, M. A. (2023). Global map of tree density [Dataset]. http://doi.org/10.6084/m9.figshare.3179986.v2
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    zipAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Crowther, T. W.; Glick, H. B.; Covey, K. R.; Bettigole, C.; Maynard, D. S.; Thomas, S. M.; Smith, J. R.; Hintler, G.; Duguid, M. C.; Amatulli, G.; Tuanmu, M. N.; Jetz, W.; Salas, C.; Stam, C.; Piotto, D.; Tavani, R.; Green, S.; Bruce, G.; Williams, S. J.; Wiser, S. K.; Huber, M. O.; Hengeveld, G. M.; Nabuurs, G. J.; Tikhonova, E.; Borchardt, P.; Li, C. F.; Powrie, L. W.; Fischer, M.; Hemp, A.; Homeier, J.; Cho, P.; Vibrans, A. C.; Umunay, P. M.; Piao, S. L.; Rowe, C. W.; Ashton, M. S.; Crane, P. R.; Bradford, M. A.
    License

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

    Description

    Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

    Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

    Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

    Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

    Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

    Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

    References:

    Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

  18. d

    Data from: Data and Results for GIS-based Identification of Areas that have...

    • datasets.ai
    55
    Updated Jun 1, 2023
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    Department of the Interior (2023). Data and Results for GIS-based Identification of Areas that have Resource Potential for Sediment-hosted Pb-Zn Deposits in Alaska [Dataset]. https://datasets.ai/datasets/data-and-results-for-gis-based-identification-of-areas-thathave-resource-potential-for-sed
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    55Available download formats
    Dataset updated
    Jun 1, 2023
    Dataset authored and provided by
    Department of the Interior
    Description

    This data release contains the analytical results and the evaluated source data files of a geospatial analysis for identifying areas in Alaska that may have potential for sediment-hosted Pb-Zn (lead-zinc) deposits. The spatial analysis is based on queries of statewide source datasets Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. The results in geodatabase format are in SedPbZn_Results_gdb.zip. The analytical results for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. The data is described by FGDC metadata. An mxd file, layer file, and cartographic feature classes are provided for display of the results in ArcMap. Files sedPbZn_scoring_tables.pdf (list of the scoring parameters for the analysis) and sedPbZn_Results_gdb_README.txt (description of the files in this download package) are included. 2. The results in shapefile format are in SedPbZn_Results_shape.zip. The analytical results for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. The results are also provided as a CSV file. The data is described by FGDC metadata. Files sedPbZn_scoring_tables.pdf (list of the scoring parameters for the analysis) and sedPbZn_Results_shape_README.txt (description of the files in this download package) are included. 3. The source data in geodatabase format are in SedPbZn_SourceData_gdb.zip. Data layers include AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds, with FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are two python scripts 1) to score the ARDF records based on the presence of certain keywords, and 2) to evaluate the ARDF, AGDB3, and lithology layers for the potential for sediment-hosted Pb-Zn deposits within subwatershed polygons. Users may modify the scripts to design their own analyses. Files sedPbZn_scoring_table.pdf (list of the scoring parameters for the analysis) and sedPbZn_sourcedata_gdb_README.txt (description of the files in this download package) are included. 4. The source data in shapefile and CSV format are in SedPbZn_SourceData_shape.zip. Data layers include ARDF and lithology from SIM3340, and HUC subwatersheds, with FGDC metadata. The ARDF keyword tables available in the geodatabase package are presented here as CSV files. All data files are described with the FGDC metadata. Files sedPb_Zn_scoring_table.pdf (list of the scoring parameters for the analysis) and sedPbZn_sourcedata_shapefile_README.txt (description of the files in this download package) are included. 5. Appendices 2, 3 and 4, which are cited by the larger work OFR2020-1147. Files are presented in XLSX and CSV formats.

  19. GIS Shapefile - Ordinance_parcels

    • search.dataone.org
    • portal.edirepository.org
    • +1more
    Updated Apr 5, 2019
    + more versions
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    Cary Institute Of Ecosystem Studies; Jarlath O'Neil-Dunne; Morgan Grove (2019). GIS Shapefile - Ordinance_parcels [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F120%2F600
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    Dataset updated
    Apr 5, 2019
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Cary Institute Of Ecosystem Studies; Jarlath O'Neil-Dunne; Morgan Grove
    Time period covered
    Jan 1, 2004 - Nov 17, 2011
    Area covered
    Description

    social system, socio-economic resources, justice, BES, Environmental disamentities, Environmental Justice, Zoning Board of Appeals Summary For use in the environmental injustices study of Baltimore relating to patterns of environmental disamenties in relation to low income/minority communities. Description This feature class layer is a point dataset of authorizing ordinances from the Baltimore City Council and Mayor from 1930 until 1999 concerning identified environmental disamentities. The data was gathered from records from the City Council since 1930 relating to decisions concerning land-uses considered to be environmental disamentities and is to be used to examine environmental injustices involving low income/minority communities in Baltimore. To examine if environmental injustices exist in Baltimore, this point layer will be overlayed with race/income data to determine if patterns of inequity exist. Points were placed manually using the associated addresses from the Ordinance_master dataset and using ISTAR 2004 data in conjunction with Baltimore parcel data. The Ordinance_ID number associated with each point relates to its appeal number from the City Council. Multiple points on the data layer have the same Ordinance_ID. This point layer can be joined with the Ordinance_master data layer based on the field "Ordinance_ID" and using the relationship "Ordinance_point_relationship". Credits UVM Spatial Analysis Lab Use limitations None. There are no restrictions on the use of this dataset. The authors of this dataset make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Extent West -76.707701 East -76.526991 North 39.371885 South 39.200794 This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

  20. Hazardous Fuel Treatment Reduction: Polygon (Feature Layer)

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    • +8more
    Updated Oct 2, 2025
    + more versions
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    U.S. Forest Service (2025). Hazardous Fuel Treatment Reduction: Polygon (Feature Layer) [Dataset]. https://catalog.data.gov/dataset/hazardous-fuel-treatment-reduction-polygon-feature-layer-9c557
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    Dataset updated
    Oct 2, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Description

    Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents activities of hazardous fuel treatment reduction that are polygons. All accomplishments toward the unified hazardous fuels reduction target must meet the following definition: Vegetative manipulation designed to create and maintain resilient and sustainable landscapes, including burning, mechanical treatments, and/or other methods that reduce the quantity or change the arrangement of living or dead fuel so that the intensity, severity, or effects of wildland fire are reduced within acceptable ecological parameters and consistent with land management plan objectives, or activities that maintain desired fuel conditions. These conditions should be measurable or predictable using fire behavior prediction models or fire effects models. Go to this url for full metadata description: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.Activity_HazFuelTrt_PL.xml

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U.S. Geological Survey (2025). Data and Results for GIS-Based Identification of Areas that have Resource Potential for Lode Gold in Alaska [Dataset]. https://catalog.data.gov/dataset/data-and-results-for-gis-based-identification-of-areas-that-have-resource-potential-for-lo

Data and Results for GIS-Based Identification of Areas that have Resource Potential for Lode Gold in Alaska

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Dataset updated
Oct 2, 2025
Dataset provided by
U.S. Geological Survey
Description

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

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