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.

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).

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 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 plat 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.

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

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: