Salt Lake County Municipal Boundaries, including Cities, Metro Townships and Unincorporated areas.Source:Salt Lake County Surveyor's Office

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.

Subdivision boundaries in Salt Lake County maintained by the Salt Lake County Surveyor's Office.

This map displays active transportation (AT) crash data within the boundaries of South Salt Lake and across Salt Lake County. Crashes involving pedestrians and bicyclists are visualized using a heat map within the city limits to highlight areas with higher concentrations of incidents. Outside the city boundaries, crashes are shown as individual points or additional visualizations covering the broader county area.The map is intended to support transportation safety analysis, identify high-risk locations for people walking and biking, and inform planning and infrastructure investment decisions at both the city and county levels. Data reflects reported crashes and includes information on crash frequency and general location patterns.

This map shows the locations of canals and paved trails in relation to on-road cycling comfort levels. The canal data was obtained from the Utah AGRC, and the bike and trail data was obtained by Salt Lake City and Salt Lake County. This map is static; there is no update schedule. For more information, please contact Jordan Backman (jbackman@utah.gov).

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.

The LEHD Origin Destination Employment Statistics (LODES) dataset is updated annually by the Census Bureau in partnership with the IRS.The LODES dataset provides information on the location and characteristics of every job in the United States that is covered by unemployment insurance.The data shown in for the year 2019, the most current year at the time this map was produced.Layers KeyNumber: Number of PeoplePercent Selected Area: Share of each area (city/township)Percent Map Unit: Share within each map unit (city, small district, tract, block group) as declared in layer nameColumn descriptions: [CODE3]_h: Home Location of those who work in [Map Unit][CODE3]_w: Work Location of those who live in [Map Unit]City/Township Codes are as follows:SHORTDESCCODE3AMERICAN FORKAFKALTAALAALPINEALPBLUFFDALEBDLBRIGHAM CITYBGMBOUNTIFULBNTBRIGHTONBRTCEDAR FORTCDFCENTERVILLECENCHARLESTONCHACEDAR HILLSCHLCLEARFIELDCLFCLINTONCLICOPPERTON METRO TOWNSHIPCMTCOALVILLECOACOTTONWOOD HEIGHTSCWHDANIELDANDRAPER CITYDRAEAGLE MOUNTAINEAGELK RIDGEELKEMIGRATION CANYON METRO TOWNSHIPEMTFARMINGTONFARFRANCISFCSFAIRFIELDFFDFARR WEST CITYFRRFRUIT HEIGHTSFTHGENOLAGLAGOSHENGOSGRANTSVILLEGRLHARRISVILLEHARHIDEOUT (SUMMIT)HDTHIDEOUT (WASATCH)HDTHEBER CITYHEBHERRIMAN TOWNHERHIGHLANDHGHHENEFERHNFCITY OF HOLLADAYHOLHOOPERHOOHUNTSVILLEHVLINDEPENDENCEINDINTERLAKEN TOWNINTKAYSVILLEKAYKAMASKMSKEARNS METRO TOWNSHIPKMTLAYTONLAYLEHILEHLINDONLINMAPLETONMAPMIDVALEMIDMILLCREEKMLCMAGNA METRO TOWNSHIPMMTMORGANMRGMARRIOTT-SLATERVILLE CITYMSLMURRAYMURMIDWAYMWYNORTH OGDEN CITYNOGCITY OF NORTH SALT LAKENSLOGDEN CITYOGDOAKLEYOKLOREMORMPAYSONPAYPLEASANT GROVEPGRPLAIN CITYPLNPARK CITYPRKPERRY CITYPRYPROVOPVOPLEASANT VIEWPVWROY CITYROYRIVERDALERVDRIVERTONRVTSANDY CITYSANSANTAQUIN CITY (UTAH CO)SAQSARATOGA SPRINGSSARSPANISH FORKSFKSOUTH JORDANSJCSALT LAKE CITYSLCSALEMSLMSOUTH OGDENSOGSPRINGVILLESPVSOUTH SALT LAKE CITYSSLSUNSETSUNSOUTH WEBERSWESYRACUSESYRTAYLORSVILLE CITYTAYTOOELETOOUINTAHUINVINEYARDVINWASHINGTON TERRACEWATWALLSBURGWBGWOODLAND HILLSWDLWEST BOUNTIFULWEBWHITE CITY METRO TOWNSHIPWHTWEST HAVENWHVWILLARD CITYWILWEST JORDAN CITYWJCWEST POINTWPTWEST VALLEY CITYWVCWOODS CROSS CITYWXC

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.

This map displays active transportation (AT) crash data within the boundaries of Riverton and across Salt Lake County. Crashes involving pedestrians and bicyclists are visualized using a heat map within the city limits to highlight areas with higher concentrations of incidents. Outside the city boundaries, crashes are shown as individual points or additional visualizations covering the broader county area.The map is intended to support transportation safety analysis, identify high-risk locations for people walking and biking, and inform planning and infrastructure investment decisions at both the city and county levels. Data reflects reported crashes and includes information on crash frequency and general location patterns.

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.

This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.

The PALEOMAP project produces paleogreographic maps illustrating the Earth's plate tectonic, paleogeographic, climatic, oceanographic and biogeographic development from the Precambrian to the Modern World and beyond.

A series of digital data sets has been produced consisting of plate tectonic data, climatically sensitive lithofacies, and biogeographic data. Software has been devloped to plot maps using the PALEOMAP plate tectonic model and digital geographic data sets: PGIS/Mac, Plate Tracker for Windows 95, Paleocontinental Mapper and Editor (PCME), Earth System History GIS (ESH-GIS), PaleoGIS(uses ArcView), and PALEOMAPPER.

Teaching materials for educators including atlases, slide sets, VHS animations, JPEG images and CD-ROM digital images.

Some PALEOMAP products include: Plate Tectonic Computer Animation (VHS) illustrating motions of the continents during the last 850 million years.

Paleogeographic Atlas consisting of 20 full color paleogeographic maps. (Scotese, 1997).

Paleogeographic Atlas Slide Set (35mm)

Paleogeographic Digital Images (JPEG, PC/Mac diskettes)

Paleogeographic Digital Image Archive (EPS, PC/Mac Zip disk) consists of the complete digital archive of original digital graphic files used to produce plate tectonic and paleographic maps for the Paleographic Atlas.

GIS software such as PaleoGIS and ESH-GIS.

This map should be used by UDOT and Utah municipalities to determine their eligibility for a Transit Innovations Grant. Eligible counties include Box Elder, Cache, Davis, Salt Lake, Summit, Tooele, Utah, and Weber.This map contains municipal boundaries throughout the state of Utah, these boundaries were compiled from URGC, and more information can be found at https://gis.utah.gov/products/sgid/boundaries/municipal/. Population data was compiled from multiple sources. Population data from 2013 to 2022 was collected from the U.S. Census using the 2013 5-Year ACS and 2022 5-Year ACS for both place and county level populations. Population Data from 2023-2032 was collected from the WFRC 2023 RTP city area population projections. More information about the WFRC population projections can be found at https://data.wfrc.org/datasets/wfrc::population-projections-city-area-rtp-2023/about. County boundaries from UGRC are also included in this map for ease of identification. Municipal and county percent population change was calculated in the same manner for both time frames. The earlier year population was subtracted from the latter year and divided by the latter year. The percent change for the municipality was then compared its county. Municipalities were identified as growing faster than the county from 2013-2022, from 2023-2032, or both time periods.This map is a work in progress and not yet ready for public release.For questions regarding this information please contact Ryan Hunter at r.hunter@fehrandpeers.com

This data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map. Only two of those classes were distinguished in the geographical portion comprising the FSU.

This data set is a condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU). This map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile). The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base. This data set was prepared by SERM-FBIU. The data is a condensed forest cover type map of Saskatchewan at a scale of 1:1,000,000.

This dataset includes aboveground biomass (AGB) and vegetation of herbaceous and forest wetland at 5.4 m resolution across the Wax Lake Delta (WLD) in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Vegetation classes were derived from Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquired over the Atchafalaya Basin and the Terrebonne Basin in October 2016 in combination with a digital elevation model. The AVIRIS-NG surface reflectance data were also combined with L-band Uninhabited Airborne Vehicle Synthetic Aperture Radar (UAVSAR) HV backscatter and scattering component values from coincident vegetation sample sites to develop and test AGB models for emergent herbaceous and forested wetland vegetation. This study used the integrated airborne data from AVIRIS-NG and UAVSAR to assess the instruments' unique capabilities in combination for estimating AGB in coastal deltaic wetlands. The 5.4 m resolution vegetation classification map for the WLD study area was then used to apply the best models to estimate AGB across the WLD.

This dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map. These 1m resolution maps were used to calculate the fraction of green vegetation within 30m Landsat pixels for the same tidal marsh regions and these data are provided in a related dataset.

This data set provides map images of hydrographic, morphologic, and edaphic features for the northern Amazon Basin in eastern Ecuador. The hydrographic data are available at two scales based on the 1:50,000 and 1:250,000-scale topographic source maps that were generated in 1990 and 1993, respectively. Morphological and edaphological data were digitized from a 1:500,000 map published in 1983. There are 3 compressed (*.zip) data files with this data set.

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

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: