23 datasets found
  1. T

    Municipal Boundaries

    • opendata.utah.gov
    • hub.arcgis.com
    application/rdfxml +5
    Updated Aug 20, 2022
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    (2022). Municipal Boundaries [Dataset]. https://opendata.utah.gov/dataset/Municipal-Boundaries/e8k6-zy9k
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    xml, json, application/rdfxml, application/rssxml, csv, tsvAvailable download formats
    Dataset updated
    Aug 20, 2022
    Description

    Salt Lake County Municipal Boundaries, including Cities, Metro Townships and Unincorporated areas.


    Source:
    Salt Lake County Surveyor's Office

  2. TIGER/Line Shapefile, 2020, County, Salt Lake County, UT, All Roads

    • catalog.data.gov
    Updated Oct 13, 2021
    + more versions
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch (Publisher) (2021). TIGER/Line Shapefile, 2020, County, Salt Lake County, UT, All Roads [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-2020-county-salt-lake-county-ut-all-roads
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    Dataset updated
    Oct 13, 2021
    Dataset provided by
    United States Department of Commercehttp://www.commerce.gov/
    United States Census Bureauhttp://census.gov/
    Area covered
    Salt Lake County, Utah
    Description

    The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, stairways, and winter trails.

  3. n

    Contours for Utah

    • cmr.earthdata.nasa.gov
    Updated Apr 24, 2017
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    (2017). Contours for Utah [Dataset]. https://cmr.earthdata.nasa.gov/search/concepts/C1214613874-SCIOPS
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    Dataset updated
    Apr 24, 2017
    Time period covered
    Jan 1, 2001 - Present
    Area covered
    Description

    This datasets contain a statewide coverage of contours.

    • 500 foot contours derived from the 10 meter National Elevation Dataset’s (NED) Digital Elevation Model (DEM).

    • 2 meter contours derived from 2 meter LiDAR for Salt Lake County. No smoothing has been applied to the contour data but a ‘Smooth Line’ in ArcMap with a ’50 meter Smoothing Tolerance’ cleans the data up nicely. Although the data is refered to as 2 meter, the contour intervals are .5 meter. SLCo_2m_Index is a shapefile that can be used to determine what files to download. These contour shapefiles cover 4,000 X 4,000 meter blocks. This data has a UTM NAD83 meters projection.

  4. S

    State of Utah Acquired Lidar Data - Wasatch Front

    • portal.opentopography.org
    • otportal.sdsc.edu
    • +4more
    raster
    Updated Mar 25, 2015
    + more versions
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    OpenTopography (2015). State of Utah Acquired Lidar Data - Wasatch Front [Dataset]. http://doi.org/10.5069/G9TH8JNQ
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    rasterAvailable download formats
    Dataset updated
    Mar 25, 2015
    Dataset provided by
    OpenTopography
    Time period covered
    Oct 18, 2013 - May 31, 2014
    Area covered
    Variables measured
    Area, Unit, RasterResolution
    Dataset funded by
    Utah Division of Emergency Management
    Federal Emergency Management Agency
    U.S. Geological Survey
    Salt Lake County Surveyors Office and partner cities
    Utah Geological Survey
    Description

    The State of Utah, including the Utah Automated Geographic Reference Center, Utah Geological Survey, and the Utah Division of Emergency Management, along with local and federal partners, including Salt Lake County and local cities, the Federal Emergency Management Agency, the U.S. Geological Survey, and the U.S. Environmental Protection Agency, have funded and collected over 8380 km2 (3236 mi2) of high-resolution (0.5 or 1 meter) Lidar data across the state since 2011, in support of a diverse set of flood mapping, geologic, transportation, infrastructure, solar energy, and vegetation projects. The datasets include point cloud, first return digital surface model (DSM), and bare-earth digital terrain/elevation model (DEM) data, along with appropriate metadata (XML, project tile indexes, and area completion reports).

    This 0.5-meter 2013-2014 Wasatch Front dataset includes most of the Salt Lake and Utah Valleys (Utah), and the Wasatch (Utah and Idaho), and West Valley fault zones (Utah).

    Other recently acquired State of Utah data include the 2011 Utah Geological Survey Lidar dataset covering Cedar and Parowan Valleys, the east shore/wetlands of Great Salt Lake, the Hurricane fault zone, the west half of Ogden Valley, North Ogden, and part of the Wasatch Plateau in Utah.

  5. a

    Utah Great Salt Lake Shoreline Flooding

    • hub.arcgis.com
    • opendata.gis.utah.gov
    • +1more
    Updated Nov 22, 2019
    + more versions
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    Utah Automated Geographic Reference Center (AGRC) (2019). Utah Great Salt Lake Shoreline Flooding [Dataset]. https://hub.arcgis.com/datasets/275cc3a094d142bb979f23ff7b036843
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    Dataset updated
    Nov 22, 2019
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    This dataset represents the Flood Plain Management Services Study (FPMS) ares, 100-Year Flood for the Great Salt Lake. The area included Salt Lake City, Davis, Weber, tooele and box elder County The information was collected by digitzing the quad maps (Salt Lake, Tooele, boxelder county) and plate maps (weber and Davis county). The digital data contain the zone boundary and shoreline boundary. The FPMS study was limited to the general area along the Salt Lake County shoreline of the Great Salt Lake Only the 100-year flood elevation was evaluated and included wind and wave action for the Great Salt Lake. This dataset is the most current digital information available.

  6. u

    Utah Salt Lake County Parcels LIR

    • opendata.gis.utah.gov
    • hub.arcgis.com
    Updated Nov 20, 2019
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    Utah Automated Geographic Reference Center (AGRC) (2019). Utah Salt Lake County Parcels LIR [Dataset]. https://opendata.gis.utah.gov/maps/utah-salt-lake-county-parcels-lir?appid=84e1fcc2b93041348ef4329f532ab846&edit=true
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    Dataset updated
    Nov 20, 2019
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpageunder LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/ It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherSalt Lake County Tax Exempt codes below:AE - Airport - ExemptCC - Commercial Common AreaCE - Conservation EasementCM - CemeteryEC - Exempt CharitableEE - Exempt EducationER - Exempt ReligiousGB - GreenbeltHE - Homeowners Assoc ExemptIL - In LieuIR - Irrigation CompanyMC - Master CardOE - Owner ExemptPE - Part ExemptPR - Pro-RatedPT - Privilege TaxPY - Privilege Tax on a YieldSA - State AssessedSC - State and Cnty AssessedSE - Special - ExemptSU - Salt Lake - Utah CntyTD - Divided Tax DistrictUI - Undivided_Interest TAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialSalt Lake County Property Class codes below:R - Residential / CondoC - CommercialI - IndustrialRE - RecreationalA - AgriculturalMH - Multi HousingMore information about the PROP_CLASS and PROP_TYPE for Salt Lake County can be found at http://slco.org/assessor/new/queryproptyp.cfmPROP_TYPE (expected) Text 100 - Single Family Res.,Townhome, CondoPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

  7. u

    Utah Address System Quadrants

    • opendata.gis.utah.gov
    • hub.arcgis.com
    Updated Aug 10, 2016
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    Utah Automated Geographic Reference Center (AGRC) (2016). Utah Address System Quadrants [Dataset]. https://opendata.gis.utah.gov/datasets/utah-address-system-quadrants/api
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    Dataset updated
    Aug 10, 2016
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    Last update: April 4, 2023Added the Mammoth address system in Juab county. Additional minor edits to account for annexations in Utah (Springville, Lehi) and Box Elder (Willard, Garland) counties, April 2023.Added several address grids in Beaver county (Elk Meadows, Ponderosa, Greenville, Adamsville, Sulphurdale). Made major updates to grids in Utah, Cache, Tooele, and Box Elder Counties. Renamed 'NSL' to 'North Salt Lake' and 'East Carbon City' to 'East Carbon', December 2022. Minor adjustment to quadrants in Bluff.Added Rocky Ridge address grid in northern Juab county, August 2022.Updates were made near Elsinore/Central Valley/Monroe corners due to recent Elsinore annexation and inputs from Sevier County, September 2021.Improvements were made to Brigham City, Millville, Logan, and Providence, February 2016.Improvements were made to the Heber, Hyde Park, Logan, and Woodland address system boundaries; updated the American Fork, Fielding, Payson, and Saratoga Springs address system boundaries to reflect recent annexations, January 2016Improvements were made to the Hyde Park and Logan address system boundary, November 2015Improvements were made to the Hyrum and Logan address system boundary, November 2015Updated the American Fork address system boundary to reflect recent annexations, October 2015Improvements were made to the Brigham City, Fishlake, Fremont, Garland, Loa, Lyman, Mantua, Tremonton, and Willard address system boundaries; updated the Lehi and Santa Clara address system boundaries to reflect recent annexations, August 2015Improvements were made to the Price and Wellington address system boundaries; updated the Lehi and Provo address system boundaries to reflect recent annexations, July 2015Improvements were made to the Layton and HAFB address system boundaries; updated the Provo and Spanish Fork address system boundaries to reflect recent annexations, June 2015Updated address system boundaries to reflect annexations in Lehi, Lewiston, and Snowville, May 2015Improvements were made to the Orderville address system boundary to match the municipal boundary, February 2015Updated address system boundaries to match annexations in American Fork, Farmington, Elk Ridge, Grantsville, Lehi, Mendon, Mount Pleasant, Payson, Provo, Spanish Fork, and Washington, January 2015 Improvements were made to the Elmo and Cleveland address system boundaries, December 2014Improvements were made to the Wellington address system boundaries, July 2014Improvements were made to the NSL (North Salt Lake) and Bountiful address system boundaries, June 2014.Changed address system name East Carbon-Sunnyside to East Carbon City, May 2014Updated address system boundaries to match annexations in northern Utah County; misc improvements in Davis County; adjusted Laketown/Garden City boundary, April 2014Merged East Carbon and Sunnyside to create the East Carbon-Sunnyside address system, February 2014.Improvements were made to the Iron County address system quadrant boundaries and topological errors were corrected statewide, January 2014. Improvements were made to Garfield County and Washington County address system quadrant boundaries, August 2013.More information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/location/address-data/

  8. u

    Utah Address Points

    • opendata.gis.utah.gov
    • opendata.utah.gov
    • +3more
    Updated Jul 13, 2016
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    Utah Automated Geographic Reference Center (AGRC) (2016). Utah Address Points [Dataset]. https://opendata.gis.utah.gov/datasets/utah-address-points/geoservice
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    Dataset updated
    Jul 13, 2016
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

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

    Area covered
    Description

    The Address Points dataset shows Utah address points for all twenty-nine Utah counties. An address point represents a geographic location that has been assigned a US Postal Service (USPS) address by the local address authority (i.e., county or municipality) but does not necessarily receive mail. Address points may include several pieces of information about the structure or location that’s being mapped, such as:the full address (i.e., the USPS mailing address, if the address is for a physical location [rather than a PO box]);the landmark name; whether the location is a building;the type of unit;the city and ZIP code; unique code identifiers of the specific geographic location, including the Federal Information Processing Standard Publication (FIPS) county code and the US National Grid (USNG) spatial address;the address source; andthe date that the address point was loaded into the map layer.This dataset is mapping grade; it is a framework layer that receives regular updates. As with all our datasets, the Utah Geospatial Resource Center (UGRC) works to ensure the quality and accuracy of our data to the best of our abilities. Maintaining the dataset is now an ongoing effort between UGRC, counties, and municipalities. Specifically, UGRC works with each county or municipality’s Master Address List (MAL) authority to continually improve the address point data. Counties have been placed on an update schedule depending on the rate of new development and change within them. Populous counties, such as Weber, Davis, Salt Lake, Utah, and Washington, are more complete and are updated monthly, while rural or less populous counties may be updated quarterly or every six months.The information in the Address Points dataset was originally compiled by Utah counties and municipalities and was aggregated by UGRC for the MAL grant initiative in 2012. The purpose of this initiative was to make sure that all state entities were using the same verified, accurate county and municipal address information. Since 2012, more data has been added to the Address Points GIS data and is used for geocoding, 911 response, and analysis and planning purposes. The Address Point data is also used as reference data for the api.mapserv.utah.gov geocoding endpoint, and you can find the address points in many web mapping applications. This dataset is updated monthly and can also be found at: https://gis.utah.gov/data/location/address-data/.

  9. g

    EnviroAtlas - Salt Lake City, UT - BenMAP Results by Block Group | gimi9.com...

    • gimi9.com
    + more versions
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    EnviroAtlas - Salt Lake City, UT - BenMAP Results by Block Group | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_enviroatlas-salt-lake-city-ut-benmap-results-by-block-group4
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    License

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

    Area covered
    Salt Lake City, Utah
    Description

    This EnviroAtlas dataset demonstrates the effect of changes in pollution concentration on local populations in 612 block groups in Salt Lake City, UT. The US EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) was used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) and associated monetary value that result from changes in pollution concentrations for Salt Lake City and County, UT. Incidence and value estimates for the block groups are calculated using i-Tree models (www.itreetools.org), local weather data, pollution data, and U.S. Census derived population data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  10. w

    Geologic Map of the Huntsman Ranch Quadrangle, Elko County, Nevada, NBMG...

    • data.wu.ac.at
    html
    Updated Dec 4, 2017
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    (2017). Geologic Map of the Huntsman Ranch Quadrangle, Elko County, Nevada, NBMG M163 [Dataset]. https://data.wu.ac.at/schema/geothermaldata_org/Y2RiNDI1ZmQtOTcyMC00NjU4LWE4NTktYTk5MzA1NGYyZGNh
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    htmlAvailable download formats
    Dataset updated
    Dec 4, 2017
    Area covered
    19934b86d4fd99d03016bfa872f1326197727e5c
    Description

    A 1:24,000 scale Geologic Map of the Huntsman Ranch Quadrangle, Elko County, Nevada. Detailed geologic mapping by Alan R. Wallace, Michael E. Perkins, and Robers J. Fleck of the U.S. Geological Survey, Reno, NV, the University of Utah, Salt Lake City, UT, and the U.S. Geological Survey, Menlo Park, CA. Feild work was completed between 2003 and 2005 and supported by the U. S. Geological Survey. The Geodatabase specifies feature datasets and feature classes, together with feature attributes, subtypes and domains, suitable for the printed geologic map. In addition to basic geology (lithology, contacts and faults, etc.), the maps may include metamorphic overprints, cross-sections, and explanatory legend-graphics such as correlation charts, used to supplement columnar legends.

  11. a

    MnIReport2015 Counties

    • hub.arcgis.com
    • dwre-utahdnr.opendata.arcgis.com
    Updated May 31, 2018
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    Utah DNR Online Maps (2018). MnIReport2015 Counties [Dataset]. https://hub.arcgis.com/maps/utahDNR::mnireport2015-counties
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    Dataset updated
    May 31, 2018
    Dataset authored and provided by
    Utah DNR Online Maps
    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

    This dataset represents current county boundaries in Utah at 1:24,000 scale. Includes changes to the Salt Lake & Utah Counties (Draper Ridgeline) in 2005, changes to the Emery & Grand Counties (Green River) in 2002, and changes to Salt Lake & Davis Counties (NSL exchange). Potable, secondary, and supply water estimates for 2015 were joined to the county polygons by the Utah Division of Water Resources (April 2018).

  12. Master Of M V Salt Lake City C O Ag Company profile with phone,email,...

    • volza.com
    csv
    Updated May 30, 2025
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    Volza FZ LLC (2025). Master Of M V Salt Lake City C O Ag Company profile with phone,email, buyers, suppliers, price, export import shipments. [Dataset]. https://www.volza.com/company-profile/master-of-m-v-salt-lake-city-c-o-ag-20182735
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    csvAvailable download formats
    Dataset updated
    May 30, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

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

    Time period covered
    2014 - Sep 30, 2021
    Area covered
    Salt Lake City
    Variables measured
    Count of exporters, Count of importers, Sum of export value, Sum of import value, Count of export shipments, Count of import shipments
    Description

    Credit report of Master Of M V Salt Lake City C O Ag contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

  13. v

    Virginia NAIP Imagery

    • vgin.vdem.virginia.gov
    Updated Mar 31, 2022
    + more versions
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    Virginia Geographic Information Network (2022). Virginia NAIP Imagery [Dataset]. https://vgin.vdem.virginia.gov/datasets/virginia-naip-imagery/about
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    Dataset updated
    Mar 31, 2022
    Dataset authored and provided by
    Virginia Geographic Information Network
    Area covered
    Description

    The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.For more information, visit the NAIP Page at: https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index

  14. u

    Utah Cities by Population

    • utah-demographics.com
    Updated Jun 20, 2024
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    Kristen Carney (2024). Utah Cities by Population [Dataset]. https://www.utah-demographics.com/cities_by_population
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    Dataset updated
    Jun 20, 2024
    Dataset provided by
    Cubit Planning, Inc.
    Authors
    Kristen Carney
    License

    https://www.utah-demographics.com/terms_and_conditionshttps://www.utah-demographics.com/terms_and_conditions

    Description

    A dataset listing Utah cities by population for 2024.

  15. Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade

    • gis-for-racialequity.hub.arcgis.com
    • cityscapes-projects-gisanddata.hub.arcgis.com
    Updated Jul 24, 2020
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    Home Owners' Loan Corporation (HOLC) Neighborhood Redlining Grade [Dataset]. https://gis-for-racialequity.hub.arcgis.com/maps/063cdb28dd3a449b92bc04f904256f62
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    Dataset updated
    Jul 24, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. NOTE: This map has been updated as of 1/16/24 to use a newer version of the data layer which contains more cities than it previously did. As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. Map opens in Sacramento, CA. Use bookmarks or the search bar to get to other cities.Cities included in this mapAlabama: Birmingham, Mobile, MontgomeryArizona: PhoenixArkansas: Arkadelphia, Batesville, Camden, Conway, El Dorado, Fort Smith, Little Rock, Russellville, TexarkanaCalifornia: Fresno, Los Angeles, Oakland, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: Boulder, Colorado Springs, Denver, Fort Collins, Fort Morgan, Grand Junction, Greeley, Longmont, PuebloConnecticut: Bridgeport and Fairfield; Hartford; New Britain; New Haven; Stamford, Darien, and New Canaan; WaterburyFlorida: Crestview, Daytona Beach, DeFuniak Springs, DeLand, Jacksonville, Miami, New Smyrna, Orlando, Pensacola, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Columbus, Macon, SavannahIowa: Boone, Cedar Rapids, Council Bluffs, Davenport, Des Moines, Dubuque, Sioux City, WaterlooIllinois: Aurora, Chicago, Decatur, East St. Louis, Joliet, Peoria, Rockford, SpringfieldIndiana: Evansville, Fort Wayne, Indianapolis, Lake County Gary, Muncie, South Bend, Terre HauteKansas: Atchison, Greater Kansas City, Junction City, Topeka, WichitaKentucky: Covington, Lexington, LouisvilleLouisiana: New Orleans, ShreveportMaine: Augusta, Boothbay, Portland, Sanford, WatervilleMaryland: BaltimoreMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Fall River, Fitchburg, Haverhill, Holyoke Chicopee, Lawrence, Lexington, Lowell, Lynn, Malden, Medford, Melrose, Milton, Needham, New Bedford, Newton, Pittsfield, Quincy, Revere, Salem, Saugus, Somerville, Springfield, Waltham, Watertown, Winchester, Winthrop, WorcesterMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Jackson, Kalamazoo, Lansing, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Austin, Duluth, Mankato, Minneapolis, Rochester, Staples, St. Cloud, St. PaulMississippi: JacksonMissouri: Cape Girardeau, Carthage, Greater Kansas City, Joplin, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Elizabeth City, Fayetteville, Goldsboro, Greensboro, Hendersonville, High Point, New Bern, Rocky Mount, Statesville, Winston-SalemNorth Dakota: Fargo, Grand Forks, Minot, WillistonNebraska: Lincoln, OmahaNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen County, Camden, Essex County, Monmouth, Passaic County, Perth Amboy, Trenton, Union CountyNew York: Albany, Binghamton/Johnson City, Bronx, Brooklyn, Buffalo, Elmira, Jamestown, Lower Westchester County, Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Schenectady, Staten Island, Syracuse, Troy, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorain, Portsmouth, Springfield, Toledo, Warren, YoungstownOklahoma: Ada, Alva, Enid, Miami Ottawa County, Muskogee, Norman, Oklahoma City, South McAlester, TulsaOregon: PortlandPennsylvania: Allentown, Altoona, Bethlehem, Chester, Erie, Harrisburg, Johnstown, Lancaster, McKeesport, New Castle, Philadelphia, Pittsburgh, Wilkes-Barre, YorkRhode Island: Pawtucket & Central Falls, Providence, WoonsocketSouth Carolina: Aiken, Charleston, Columbia, Greater Anderson, Greater Greensville, Orangeburg, Rock Hill, Spartanburg, SumterSouth Dakota: Aberdeen, Huron, Milbank, Mitchell, Rapid City, Sioux Falls, Vermillion, WatertownTennessee: Chattanooga, Elizabethton, Erwin, Greenville, Johnson City, Knoxville, Memphis, NashvilleTexas: Amarillo, Austin, Beaumont, Dallas, El Paso, Forth Worth, Galveston, Houston, Port Arthur, San Antonio, Waco, Wichita FallsUtah: Ogden, Salt Lake CityVirginia: Bristol, Danville, Harrisonburg, Lynchburg, Newport News, Norfolk, Petersburg, Phoebus, Richmond, Roanoke, StauntonVermont: Bennington, Brattleboro, Burlington, Montpelier, Newport City, Poultney, Rutland, Springfield, St. Albans, St. Johnsbury, WindsorWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Madison, Milwaukee County, Oshkosh, RacineWest Virginia: Charleston, Huntington, WheelingAn example of a map produced by the HOLC of Philadelphia:

  16. a

    Arizona 2019 NAIP Imagery

    • azgeo-open-data-agic.hub.arcgis.com
    Updated Sep 24, 2020
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    AZGeo Data Hub (2020). Arizona 2019 NAIP Imagery [Dataset]. https://azgeo-open-data-agic.hub.arcgis.com/datasets/0614dd24fdeb4feeb023e4ab4e4cfe42
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    Dataset updated
    Sep 24, 2020
    Dataset authored and provided by
    AZGeo Data Hub
    Area covered
    Description

    The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition. NAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, and a three-year cycle began in 2009. Click here >> for an interactive status map of NAIP acquisitions from 2002 - 2019. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/

  17. a

    National Agriculture Imagery Program (NAIP) History 2002-2021

    • supply-chain-data-hub-nmcdc.hub.arcgis.com
    Updated May 25, 2022
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    New Mexico Community Data Collaborative (2022). National Agriculture Imagery Program (NAIP) History 2002-2021 [Dataset]. https://supply-chain-data-hub-nmcdc.hub.arcgis.com/documents/8eb6c5e7adc54ec889dd6fc9cc2c14c4
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    Dataset updated
    May 25, 2022
    Dataset authored and provided by
    New Mexico Community Data Collaborative
    Description

    What is NAIP?The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the contiguous U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Production and Conservation Business Center through the Aerial Photography Field Office in Salt Lake City. The APFO as of August 16, 2020 has transitioned to the USDA FPAC-BC's Geospatial Enterprise Operations Branch (GEO). This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.How can I Access NAIP?On the web GEO (APFO) public image services can be accessed through the REST endpoint here. Compressed County Mosaics (CCMs) are available to the general public through the USDA Geospatial Data Gateway. All years of available imagery may be downloaded as 1/2, 1, or 2 meter CCMs depending on the original spatial resolution. CCMs with a file size larger than 8 GB are not able to be downloaded from the Gateway. Full resolution 4 band quarter quads (DOQQs) are available for purchase from FPAC GEO. Contact the GEO Customer Service Section for information on pricing for DOQQs and how to obtain CCMs larger than 8 GB. A NAIP image service is also available on ArcGIS Online through an organizational subscription.How can NAIP be used?NAIP is used by many non-FSA public and private sector customers for a wide variety of projects. A detailed study is available in the Qualitative and Quantitative Synopsis on NAIP Usage from 2004 -2008: Click here for a list of NAIP Information and Distribution Nodes.When is NAIP acquired?NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, a three-year cycle began in 2009, NAIP was on a two-year cycle until 2016, currently NAIP is on a 3 year refresh cycle. Click here >> for an interactive PDF status map of NAIP acquisitions from 2002 - 2018. 2021 acquisition status dashboard is available here.What are NAIP Specifications?NAIP imagery is currently acquired at 60cm ground sample distance (GSD) with a horizontal accuracy that matches within four meters of photo-identifiable ground control points.The default spectral resolution beginning in 2010 is four bands: Red, Green, Blue and Near Infrared.Contractually, every attempt will be made to comply with the specification of no more than 10% cloud cover per quarter quad tile, weather conditions permitting.All imagery is inspected for horizontal accuracy and tonal quality. Make Comments/Observations about current NAIP imagery.If you use NAIP imagery and have comments or find a problem with the imagery please use the NAIP Imagery Feedback Map to let us know what you find or how you are using NAIP imagery. Click here to access the map.**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: National Agriculture Imagery Program (NAIP) History 2002-2021Item Type: Web Mapping Application URL Summary: Story map depicting the highlights and changes throughout the National Agriculture Imagery Program (NAIP) from 2002-2021.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: URL referencing this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=445e3dfd16c4401f95f78ad5905a4cceFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8eb6c5e7adc54ec889dd6fc9cc2c14c4UID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

  18. a

    GLRI - Step 1a: Collection - NAIP Imagery

    • glri-usace.hub.arcgis.com
    Updated Sep 29, 2021
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    GLRI - Step 1a: Collection - NAIP Imagery [Dataset]. https://glri-usace.hub.arcgis.com/maps/291124a24c6b4e4aa8a4e71570181093
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    Dataset updated
    Sep 29, 2021
    Dataset authored and provided by
    usace_sam_rd3
    Area covered
    Description

    What is NAIP?The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.

  19. a

    NAIP quarter quad m 3311238 se 12 1 20150529

    • geodata-asu.hub.arcgis.com
    Updated Aug 30, 2022
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    Arizona State University (2022). NAIP quarter quad m 3311238 se 12 1 20150529 [Dataset]. https://geodata-asu.hub.arcgis.com/documents/c9b5a5c7e66b40a4ad7f4519b404c924
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    Dataset updated
    Aug 30, 2022
    Dataset authored and provided by
    Arizona State University
    Description

    2015 National Agriculture Imagery Program (NAIP) imagery for Arizona. The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A

     The National Agriculture Imagery Program (NAIP) acquires aerial 
    

    imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition. NAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, and a three-year cycle began in 2009. Click here >> for an interactive status map of NAIP acquisitions from 2002 - 2015. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/

  20. a

    Arizona NAIP 2023 Imagery

    • azgeo-open-data-agic.hub.arcgis.com
    Updated Jan 10, 2025
    + more versions
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    AZGeo Data Hub (2025). Arizona NAIP 2023 Imagery [Dataset]. https://azgeo-open-data-agic.hub.arcgis.com/datasets/dedabb5657644beea8769175d9e56889
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    Dataset updated
    Jan 10, 2025
    Dataset authored and provided by
    AZGeo Data Hub
    Area covered
    Description

    2023 National Agriculture Imagery Program (NAIP) imagery for ArizonaThe National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition. NAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, and a three-year cycle began in 2009. Click here >> for an interactive status map of NAIP acquisitions from 2002 - 2023. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/

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(2022). Municipal Boundaries [Dataset]. https://opendata.utah.gov/dataset/Municipal-Boundaries/e8k6-zy9k

Municipal Boundaries

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xml, json, application/rdfxml, application/rssxml, csv, tsvAvailable download formats
Dataset updated
Aug 20, 2022
Description

Salt Lake County Municipal Boundaries, including Cities, Metro Townships and Unincorporated areas.


Source:
Salt Lake County Surveyor's Office

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