Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under Basic Parcels."Database containing parcel boundary, parcel identifier, parcel address, owner type, and county recorder contact information" - HB113. The intent of the bill was to not include any attributes that the counties rely on for data sales. If you want other attributes associated with the parcels you need to contact the county recorder.Users should be aware the owner type field 'OWN_TYPE' in the parcel polygons is a very generalized ownership type (Federal, Private, State, Tribal). It is populated with the value of the 'OWNER' field where the parcel's centroid intersects the CADASTRE.LandOwnership polygon layer.This dataset is a snapshot in time and may not be the most current. For the most current data contact the county recorder.

This map shows specific water-quality items and hydrologic data site information which come from QWDATA (Water Quality) and GWSI (Ground Water Information System). Both QWDATA and GWSI are subsystems of NWIS (National Water Inventory System)of the USGS (United States Geologic Survey).

This map is for Cache County, Utah.

The scope and purpose of NWIS is defined on the web site:

http://water.usgs.gov/public/pubs/FS/FS-027-98/

This map shows the USGS (United States Geologic Survey), NWIS (National Water Inventory System) Hydrologic Data Sites for Cache County, Utah. The scope and purpose of NWIS is defined on the web site: http://water.usgs.gov/public/pubs/FS/FS-027-98/

The geospatial data presented here as ArcGIS layers denote landcover/landuse classifications to support field sampling efforts that occurred within the Cache Creek Settling Basin (CCSB) from 2010-2017. Manual photointerpretation of a National Agriculture Imagery Program (NAIP) dataset collected in 2012 was used to characterize landcover/landuse categories (hereafter habitat classes). Initially 9 categories were assigned based on vegetation structure (Vegtype1). These were then parsed into two levels of habitat classes that were chosen for their representativeness and use for statistical analyses of field sampling. At the coarsest level (Landcover 1), five habitat classes were assigned: Agriculture, Riparian, Floodplain, Open Water, and Road. At the more refined level (Landcover 2), ten habitat classes were nested within these five categories. Agriculture was not further refined within Landcover 2, as little consistency was expected between years as fields rotated between corn, pumpkin, tomatoes, and other row crops. Riparian habitat, marked by large canopy trees (such as Populus fremontii (cottonwood)) neighboring stream channels, also was not further refined. Floodplain habitat was separated into two categories: Mixed NonWoody (which included both Mowed and Barren habitats) and Mixed Woody. This separation of the floodplain habitat class (Landcover1) into Woody and NonWoody was performed with a 100 m2 moving window analysis in ArcGIS, where habitats were designated as either ≥50% shrub or tree cover (Woody) or <50%, and thus dominated by herbaceous vegetation cover (NonWoody). Open Water habitat was refined to consider both agricultural Canal (created) and Stream (natural) habitats. Road habitat was refined to separate Levee Roads (which included both the drivable portion and the apron on either side) and Interior roads, which were less managed. The map was tested for errors of omission and commission on the initial 9 categories during November 2014. Random points (n=100) were predetermined, and a total of 80 were selected for field verification. Type 1 (false positive) and Type 2 (false negative) errors were assessed. The survey indicated several corrections necessary in the final version of the map. 1) We noted the presence of woody species in “NonWoody” habitats, especially Baccharus salicilifolia (mulefat). Habitats were thus classified as “Woody” only with ≥50% presence of canopy species (e.g. tamarisk, black willow) 2) Riparian sites were over-characterized, and thus constrained back to “near stream channels only”. Walnut (Juglans spp) and willow stands alongside fields and irrigation canals were changed to Mixed Woody Floodplain. Fine tuning the final habitat distributions was thus based on field reconnaissance, scalar needs for classifying field data (sediment, water, bird, and fish collections), and validation of data categories using species observations from scientist field notes. Calibration was made using point data from the random survey and scientist field notes, to remove all sources of error and reach accuracy of 100%. The coverage “CCSB_Habitat_2012” is provided as an ARCGIS shapefile based on a suite of 7 interconnected ARCGIS files coded with the suffixes: cpg, dbf, sbn, sbx, shp, shx, and prj. Each file provides a component of the coverage (such as database or projection) and all files are necessary to open the “CCSB_Habitat_2012.shp” file with full functionality.

Parcel ownership and address information, parcel valuation information and basic information about the land and structure(s) associated with a given tax assessment account are maintained by SDAT and incorporated with parcel boundaries and other ancillary information maintained by the Montgomery County Planning Department.For more information about the fields and attributes in the dataset, see the data dictionary.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

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The geospatial data presented here as ArcGIS layers denote landcover/landuse classifications to support field sampling efforts that occurred within the Cache Creek Settling Basin (CCSB) from 2010-2019. Manual photointerpretation of a National Agriculture Imagery Program (NAIP) dataset collected in 2012 was used to characterize landcover/landuse categories (hereafter habitat classes). Initially 9 categories were assigned based on vegetation structure (Vegtype1). These were then parsed into two levels of habitat classes that were chosen for their representativeness and use for statistical analyses of field sampling. At the coarsest level (Landcover 1), five habitat classes were assigned: Agriculture, Riparian, Floodplain, Open Water, and Road. At the more refined level (Landcover 2), ten habitat classes were nested within these five categories. Agriculture was not further refined within Landcover 2, as little consistency was expected between years as fields rotated between corn, pumpkin, tomatoes, and other row crops. Riparian habitat, marked by large canopy trees (such as Populus fremontii (cottonwood)) neighboring stream channels, also was not further refined. Floodplain habitat was separated into two categories: Mixed NonWoody (which included both Mowed and Barren habitats) and Mixed Woody. This separation of the floodplain habitat class (Landcover1) into Woody and NonWoody was performed with a 100 m2 moving window analysis in ArcGIS, where habitats were designated as either ≥50% shrub or tree cover (Woody) or <50%, and thus dominated by herbaceous vegetation cover (NonWoody). Open Water habitat was refined to consider both agricultural Canal (created) and Stream (natural) habitats. Road habitat was refined to separate Levee Roads (which included both the drivable portion and the apron on either side) and Interior roads, which were less managed. The map was tested for errors of omission and commission on the initial 9 categories during November 2014. Random points (n=100) were predetermined, and a total of 80 were selected for field verification. Type 1 (false positive) and Type 2 (false negative) errors were assessed. The survey indicated several corrections necessary in the final version of the map. 1) We noted the presence of woody species in “NonWoody” habitats, especially Baccharus salicilifolia (mulefat). Habitats were thus classified as “Woody” only with ≥50% presence of canopy species (e.g. tamarisk, black willow) 2) Riparian sites were over-characterized, and thus constrained back to “near stream channels only”. Walnut (Juglans spp) and willow stands alongside fields and irrigation canals were changed to Mixed Woody Floodplain. Fine tuning the final habitat distributions was thus based on field reconnaissance, scalar needs for classifying field data (sediment, water, bird, and fish collections), and validation of data categories using species observations from scientist field notes. Calibration was made using point data from the random survey and scientist field notes, to remove all sources of error and reach accuracy of 100%. The coverage “CCSB_Habitat_2012” is provided as an ARCGIS shapefile based on a suite of 7 interconnected ARCGIS files coded with the suffixes: cpg, dbf, sbn, sbx, shp, shx, and prj. Each file provides a component of the coverage (such as database or projection) and all files are necessary to open the “CCSB_Habitat_2012.shp” file with full functionality. CCSB_Basin_Map.png represents the CCSB study area color coded by the four primary habitat types identified in this study.

We created a shapefile of Utah's Cache Valley street water conveyance system using ArcGIS. This included gutters, canals, and discontinued canals that transport secondary water to customers. This data collection and research supports coupled human-natural systems research because it connects human and environmental water systems. The purpose of our data collection and mapping is to support future analysis of street gutters and canals as unique secondary water delivery systems. We georeferenced the network of street water conveyance in summer 2016 that delivers secondary water. We drove, cycled, and walked Logan streets and marked those with observed water conveyance through gutters and canals on a printed map that was then transferred into an ArcGIS shapefile. To accurately determine which street gutters are part of the irrigation water delivery system, we contacted Cache County irrigation companies to receive guidance and feedback.

Last Update: 08/13/2024This trails map layer represents off-road recreational trail features and important road connections that augment Utah’s recreational trail network. This map layer features many of Utah’s recreational trails but is not yet complete. Currently, the trails information primary depicts trails used by hiking and mountain biker users. It began with map data purchased from a private entity (Orbital View, Inc), for which full rights were purchased by the GOED Office of Outdoor Recreation in July 2014. Since the original purchase, subsequent substantive updates have included: 2014 Revisions from National Park service; 2015 additions from Emery County; June 2016 additions from Cache County US Forest Service Geodata Hub (all forests except Sawtooth in NW Box Elder County), and Mountainland Association of Governments (Utah and Wasatch counties). Significant updates were made in 2018 as part of the multimodal project. These included pathways and off-road bike trails in the project area. Additional trails can be submitted to Utah UGRC for inclusion in this map layer.Note: Presently, the planned approach for on street bicycle lanes and routes is to incorporate them in the the statewide roads GIS layer using left and right side attributes. On street bike infrastructure is not expected to be represented in this map layer.More information about this layer can be found here: https://gis.utah.gov/data/recreation/trails/

This dataset contains the urban growth simulation results of future land use in 2040 of the Wasatch Range Metropolitan Area (WRMA) .In this study, we defined the WRMA as a broad, ten-county region that surrounds the Wasatch Mountain Range east of the Great Salt Lake and Salt Lake City in Utah. This region encompasses four Wasatch Front counties west of the mountain range (Weber County, Davis County, Salt Lake County, and Utah County), three Wasatch Back counties east of the mountain range (Morgan County, Summit County, and Wasatch County), and three counties neighboring the Wasatch Front (Cache County, Box Elder County, and Tooele County).

SLEUTH-3r urban growth simulation model is used to generate this dataset. Detailed SLEUTH model protocol can be found at: http://www.ncgia.ucsb.edu/projects/gig/index.html. The data used to run the SLEUTH-3r model include National Land Cover Database 2001, 2006, and 2011, US Census TIGER/Line shapefile for 2000 and 2011, United States Geological Survey 7.5 min elevation model, and Utah Landownership map from Utah Automated Geographic Reference Center.

Three alternative scenarios were developed to explore how conserving Utah’s agriculturale land and maintaining healthy watersheds would affect the patterns and trajectories of urban development: 1) The first scenario is a “Business as Usual” scenario. In this scenario, federal, state, and local parks, conservation easement areas, and surface water bodies, were completely excluded (value = 100) from development, and all the remaining lands are were naively assumed as developable (value = 0). This is the same excluded layer that was also used during model calibration. Under this scenario, we hypothesized that future urban grow will occur following the historical growth behaviors and trajectories and no changes in land designation or policies to restrict future growth will be implemented. 2) The second scenario is an “Agricultural Conservation” scenario. Within the developable areas that we identified earlier, we then identified places that are classified by the United States Department of Agriculture (USDA) as prime farmland, unique farmland, farmland of statewide importance, farmland of local importance, prime farmland if irrigated, and prime farmland if irrigated and drained. Each of these classes were assigned with an exclusion value from urban development of 100, 80, 70, 60, 50, and 40 respectively. These exclusion values reflect the relative importance of each farmland classification and preservation priorities. By doing so, the model discourages but does not totally eliminate growth from occurring on agricultural lands, which reflects a general policy position to conserve agricultural landscapes while respecting landowners’ rights to sell private property. 3) A “Healthy Watershed” scenario aims to direct urban growth away from areas prone to flooding and areas critical for maintaining healthy watersheds. First, we made a 200-meter buffer around existing surface water bodies and wetlands and assigned these areas an exclusion value of 100 to keep growth from occurring there. In addition, we assigned areas that have frequent, occasional, rare and no-recorded flooding events with exclusion values of 100, 70, 40 and 0 accordingly. We also incorporated the critical watershed restoration areas identified by the Watershed Restoration Initiative of Utah Division of Wildlife Resources (https://wri.utah.gov/wri/) into this scenario. These watershed restoration areas are priority places for improving water quality and yield, reducing catastrophic wildfires, restoring the structure and function of watersheds following wildfire, and increasing habitat for wildlife populations and forage for sustainable agriculture. However, there are not yet legal provisions for protecting them from urbanization, so we assigned these areas a value of 70 to explore the potential urban expansion outcomes if growth were encouraged elsewhere.

Future land use projections of 2040 are in GIF format, which can be reprojected and georeferenced in ArcGIS or QGIS, or be read directly as a picture.

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File Geodatabase - Click hereShapefile - Click hereThis dataset contains current parcel boundaries and related attributes for approximately 2.4 million parcels maintained by the Los Angeles County Assessor (updated monthly on the second of every month). Due to the size of the data, it is only available for download as a zipped file geodatabase or shapefile at this time. For additional annual assessment roll history and attribute metadata descriptions, please visit the L.A. County Open Data Portal and search for Assessor. To better understand individual data elements, or to interactively view individual parcel information, please visit the Assessor’s Portal. A public-facing parcel map cache can be accessed here (updated weekly): https://public.gis.lacounty.gov/public/rest/services/LACounty_Cache/LACounty_Parcel/MapServer/0All inquiries should be directed to the Mapping & GIS Services Section, LA County Office of the Assessor at gisinfo@assessor.lacounty.gov

Statewide Traffic Volume Historic and Forecast Historic traffic volume observations, future traffic volume forecasts, and adjustment factors -- are summarized using Utah's roadway planning summary segments -- for the Wasatch Front Regional Council metropolitan planning organization travel model area. This dataset can be viewed in an interactive map at: https://wfrc.org/traffic-volume-map/. This dataset provides segment level traffic volume data (historic estimates and future forecasts) within the state of Utah. Wasatch Front Regional Council (WFRC) metropolitan planning organization's travel model boundaries (including Salt Lake, Davis, western Weber, and southern Box Elder counties' urbanized areas). Future forecasts have been developed with the support of the Wasatch Front Travel Demand Model (v8.3.1) in conjunction with the adopted 2019 Regional Transportation Plan (RTP). This dataset was first released May 5th, 2020 (check the RELEASE field/column attribute for most recent update date). MAG travel model boundaries include the urbanized areas of Utah County. Cache travel model boundaries include Cache County. Dixie travel model boundaries include Washington County. Also contained within this dataset are adjustment factors, developed as part of a statewide effort led by UDOT, that can be used to scale the Average Annual Daily Traffic (AADT) volumes estimates and forecasts to provide more time-period specific volumes for a time period subsets (e.g. weekdays, weekends, specific months, seasons, maximum month, etc). Contact and additional information is available from https://wfrc.org/models-and-forecasting or through email contact to analytics@wfrc.org.UPDATE 12/14/2020: USTM segments updated with interim-year forecasts for non-MPO areas of state. Field names and descriptions are as follows: RELEASE (version of dataset) SEGID (Segment ID, combination of Route_ID and BMP) ROUTE_ID (Route Identification, <1000 for Interstate/State Routes, >1,000 for Federal Aid Routes) BMP (Begin Milepost, or milepost of beginning of segment) EMP (End Milepost, or milepost of ending of segment) FULLNAME (name of segment) CO_FIPS (County Federal Information Processing Standard, unique code for each county) CO_NAME (Name of county) X (Centroid of Segment, UTM Zone 12N) Y (Y Centroid of Segment, UTM Zone 12N) DISTANCE (length of segment in miles) F2050...F2019 (forecast AADT volumes for model years per 2019 RTP) CH17TO50...CH17TO19 (change in AADT volumes between model years) FNOTES (forecast notes, typically when drop or large increase in volumes) MOREINFO (url to more general information on models and forecasts) WFRC_FLG (flag value used internally by WFRC) AADT2017...AADT1981 (AADT estimates for a given year from UDOT) SUTRK2017 (Single-Unit, Box Type Truck percentage for 2017) CUTRK2017 (Combo-Unit, Semi Type Truck percentage for 2017) DOWFACFC (Day-of-Week Factor Functional Class) DOWFACAT (Day-of-Week Factor Area Type) FAC_MON...FAC_SUN (Day-of-Week factors for given days) FAC_WDAVG (Average Weekday Factor Monday-Thursday, multiply AADT by factor to get AWDT, divide AWDT by factor to get AADT) FAC_WEAVG (Average Weekend Factor Friday-Sunday) FAC_WEMAX (Max Weekend Factor Friday-Sunday) SSNGRP (Seasonal Factor Group) SSNVOLCLS (Seasonal Factor Volume Class) SSNATGROUP (Seasonal Factor Area Type Group) FAC_JAN...FAC_DEC (Month Factors, multiply AADT or AWDT get month ADT or AWDT) FAC_WIN (Winter Factor, December-February) FAC_SPR (Spring Factor, March-May) FAC_SUM (Summer Factor, June-August) FAC_FAL (Fall Factor, September-November) FAC_MAXMO (Month in which Maximum Month Factor is found) FAC_MAX (Maximum Month Factor)

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/

This vector tile layer provides a detailed vector reference overlay for the world symbolized with a light gray, neutral background style with minimal colors, labels, and features that is designed to draw attention to your thematic content. This layer is similar in content to Light Gray Reference, which is delivered as a raster fused map cache tile layer. This vector tile layer provides unique capabilities for customization and high-resolution display.This layer includes populated places. The map is built using the same data sources used for the Light Gray Canvas raster basemap and other Esri basemaps. Use this MapThis map is designed to be used as a reference layer in a web map. You can add this layer to a web map and save as your own map. If you would like to use this map as a reference layer in a web map, you may use the vector basemap Light Gray Canvas web map.Customize this MapBecause this map is delivered as a vector tile layer, users can customize the map to change its content and symbology, including fonts. Users are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. See the Vector Basemap group for other vector tile layers. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog and view the Esri Vector Basemaps Reference Document.

This hosted tile layer contains aerial imagery collected in the year 2023 for Linn County, Iowa. Tile Layer DetailsUpdate Frequency: NeverCollected By: EagleViewSource Pixel Resolution (urban): 3-inchSource Pixel Resolution (rural): 6-inchSource Coordinate System: NAD 1983 (2011) State Plane Iowa North (wkid: 6463)Tile Layer Coordinate System: WGS 1984 Web Mercator Auxiliary Sphere (wkid: 3857)Cache Scale Minimum: 1:577,791Cache Scale Maximum: 1:564 (urban only) Additional ResourcesVisit Linn County, Iowa on the web. Visit Linn County, Iowa GIS on the web. This site is updated as needed to reflect maps and apps of interest from various departments. Contact InformationQuestions? Contact the GIS Division by phone at (319) 892-5250 or by email.

This hosted tile layer contains aerial imagery collected in the year 2018 for Linn County, Iowa. Tile Layer DetailsUpdate Frequency: NeverCollected By: EagleViewSource Pixel Resolution (urban): 3-inchSource Pixel Resolution (rural): 6-inchSource Coordinate System: NAD 1983 State Plane Iowa North (wkid: 3417)Tile Layer Coordinate System: WGS 1984 Web Mercator Auxiliary Sphere (wkid: 3857)Cache Scale Minimum: 1:577,791Cache Scale Maximum: 1:564 (urban only) Additional ResourcesVisit Linn County, Iowa on the web. Visit Linn County, Iowa GIS on the web. This site is updated as needed to reflect maps and apps of interest from various departments. Contact InformationQuestions? Contact the GIS Division by phone at (319) 892-5250 or by email.

This hosted tile layer contains aerial imagery collected in the year 2008 for Linn County, Iowa. Tile Layer DetailsUpdate Frequency: NeverCollected By: FugroSource Pixel Resolution (urban): 6-inchSource Pixel Resolution (rural): 6-inchSource Coordinate System: NAD 1983 State Plane Iowa North (wkid: 3417)Tile Layer Coordinate System: WGS 1984 Web Mercator Auxiliary Sphere (wkid: 3857)Cache Scale Minimum: 1:577,791Cache Scale Maximum: 1:1,128 Additional ResourcesVisit Linn County, Iowa on the web. Visit Linn County, Iowa GIS on the web. This site is updated as needed to reflect maps and apps of interest from various departments. Contact InformationQuestions? Contact the GIS Division by phone at (319) 892-5250 or by email.

This hosted tile layer contains aerial imagery collected in the year 2008 for Linn County, Iowa. Tile Layer DetailsUpdate Frequency: NeverCollected By: SanbornSource Pixel Resolution (urban): 3-inchSource Pixel Resolution (rural): 3-inchSource Coordinate System: NAD 1983 State Plane Iowa North (wkid: 3417)Tile Layer Coordinate System: WGS 1984 Web Mercator Auxiliary Sphere (wkid: 3857)Cache Scale Minimum: 1:577,791Cache Scale Maximum: 1:564 (urban only) Additional ResourcesVisit Linn County, Iowa on the web. Visit Linn County, Iowa GIS on the web. This site is updated as needed to reflect maps and apps of interest from various departments. Contact InformationQuestions? Contact the GIS Division by phone at (319) 892-5250 or by email.