Geospatial data about Milwaukee County, Wisconsin Municipal Boundaries. Export to CAD, GIS, PDF, CSV and access via API.

Milwaukee County Boundary. Additional administrative boundary layers:Milwaukee County Municipal Boundaries

Milwaukee County School District Boundaries

Milwaukee County Boundary

Update frequency: as needed

Full quarter sections within Milwaukee County. These maps show engineering and tax information regarding parcels within the county and municipalities. Please visit our Quarter Section Lookup web app to download PDFs of individual engineering and tax quarter section maps.

Shapefile is projected in Wisconsin State Plane South NAD27 (WKID 32054)

School District Boundaries with School Information. Data is compiled and maintained by Milwaukee County GIS & Land Information with information from Wisconsin School Directory.Additional administrative boundary layers:Milwaukee County Municipal BoundariesMilwaukee County School District BoundariesMilwaukee County Boundary

************************In early 2025, the source of MCLIO public layers will change.*****************************

Please refer to these documents for changes:

https://mclio.maps.arcgis.com/home/item.html?id=0bb68bbae37445adb045d6a44fed3f2a

https://mclio.maps.arcgis.com/home/item.html?id=79c6c9d737c94753a388db7c6f480149

Please update maps, apps and data connections accordingly!This layer shows the municipal boundaries within Milwaukee County.

Polygons representing municipal voting wards in Milwaukee County, based on Census 2020 boundaries. *Some datasets are from the Legislative Technology Services Bureau. LTSB Website & LTSB HUB Site Other election boundary related data:Wisconsin State Senate Districts Source: LTSB Visit LTSB HUB site to download.Wisconsin State Assembly Districts Source: LTSB Visit LTSB HUB Site to download.Milwaukee County City Common Council/Village BoardMilwaukee County Supervisory Districts Wisconsin Congressional Districts Source: LTSB . Visit LTSB HUB site to download.Milwaukee County US Senate RepresentationMilwaukee County Municipal ExecutivesMilwaukee County Voting Wards

************************In early 2025, the source of MCLIO public layers will change.*****************************

Please refer to these documents for changes:

https://mclio.maps.arcgis.com/home/item.html?id=0bb68bbae37445adb045d6a44fed3f2a

https://mclio.maps.arcgis.com/home/item.html?id=79c6c9d737c94753a388db7c6f480149

Please update maps, apps and data connections accordingly!This layer contains the boundaries of Municipal Aldermanic Districts in Milwaukee County.

A dataset listing Wisconsin cities by population for 2024.

West Allis Parcel Web Viewer is a GIS Web application that helps citizens identify their parcel and obtain information about it as well as information about their surrounding Neighborhood. This application is typically used by citizens to see their parcel assessed valuation, and compare it to other parcels in their neighborhood, but it can be used for many other uses as well. You can also see the Schools in West Allis, both public and private, as well as the West Allis West Milwaukee Public School District boundaries. Additionally, you can view zip code areas, Neighborhood District boundaries, Assessment boundaries for Residential and Commercial neighborhoods, Census 2010 Tracts, Block Groups, and Blocks, Zoning Districts, TIF and BID Districts, Existing Land Use, Land Use 2010, and Comp Plan 2030 Land Use. There is also an Intersection Bing Google feature that when turned on and clicked, will link you directly to either a Bing Map or Google Map of the area providing additional aerial photos. The Viewer will extend out to the limits of Milwaukee County, and show you Municipal Divisions in Waukesha County as well. Ortho Photo coverage for all of Milwaukee County is using the latest 2015 and 2018 imagery. West Allis Parcel Viewer Map can be used by anyone who needs to check out a parcel of land within West Allis, WI. This application provides 24/7 access to the parcel information and typically supplements customer service phone calls to City Staff with questions concerning a particular parcel

************************In early 2025, the source of MCLIO public layers will change.*****************************

Please refer to these documents for changes:

https://mclio.maps.arcgis.com/home/item.html?id=0bb68bbae37445adb045d6a44fed3f2a

https://mclio.maps.arcgis.com/home/item.html?id=79c6c9d737c94753a388db7c6f480149

Please update maps, apps and data connections accordingly!This layer shows municipal and Wisconsin State Parks in Milwaukee County.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the NOAA Lake Level Viewer. It depicts potential lake level rise and fall and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at lake level change, coastal flooding impacts, and exposed lakeshore. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The NOAA Lake Level Viewer may be accessed at: https://coast.noaa.gov/llv. This metadata record describes the Lake Michigan digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Lake Level Viewer described above. This DEM includes the best available lidar, US Army Corps of Engineer dredge surveys, and National Park Service multibeam data known to exist at the time of DEM creation that met project specifications. This DEM includes data for Allegan, Antrim, Benzie, Berrien, Charlevoix, Delta, Emmet, Grand Traverse, Leelanau, Mackinac, Manistee, Mason, Menominee, Muskegon, Oceana, Ottawa, Schoolcraft, and Van Buren counties in Michigan; Lake, La Porte, and Porter Counties in Indiana, Cook and Lake Counties in Illinois, and Brown, Door, Kenosha, Kewaunee, Manitowoc, Marinette, Milwaukee, Oconto, Ozaukee, Racine, and Sheboygan Counties in Wisconsin. The DEM was produced from the following lidar data sets: 1. 2016 NOAA Topobathy Lidar: Upper Lake Michigan Islands 2. 2015 FEMA Marinette County 3. 2013 Indiana Statewide Lidar Collection: Lake, La Porte, Tippecanoe, Newton, Jasper and Porter County Buy-Up 4. 2013 Muskegon County, Michigan Lidar Co-Op 5. 2013 USACE NCMP Topobathy Lidar: Lake Michigan North (MI) 6. 2012 USACE NCMP Topobathy Lidar: Lake Michigan (MI,WI) 7. 2012 USACE NCMP Topobathy Lidar: Lake Michigan (IL,IN,MI,WI) 8. 2010 Brown County Lidar 9. 2008 USACE NCMP Topobathy Lidar: Lake Michigan (IN) 10. 2008 USACE NCMP Topobathy Lidar: Lake Michigan (WI) 11. 2008 USACE NCMP Topobathy Lidar: Lake Michigan (IL) 12. 2008 USACE NCMP Topobathy Lidar: Lake Michigan (MI) 13. 2007 USACE NCMP Topobathy BE Lidar: Lake Michigan (MI) and Lake Erie (PA) 14. 2007 ARRA Lidar: Lake County (IL) 15. 2006 USACE NCMP Topobathy Lidar: Lake Michigan (IN), Lake Erie (OH,PA), Lake Huron (MI) The DEM was produced from the following sonar data sets: 16. 2015 USACE Detroit District, Port Washington Harbor, WI 17. 2015 USACE Detroit District, South Haven Harbor, MI 18. 2015 USACE Detroit District, Washington Island (Detroit Harbor), WI 19. 2015 USACE Detroit District, Washington Island (Jackson Harbor), WI 20. 2015 USACE Detroit District, Grand Haven Harbor, MI 21. 2015 USACE Detroit District, Pentwater Harbor, MI 22. 2015 USACE Detroit District, Pensaukee Harbor, WI 23. 2015 USACE Detroit District, St. Joseph Harbor, MI 24. 2015 USACE Detroit District, Manistee Harbor, MI 25. 2015 USACE Detroit District, Green Bay Harbor, WI 26. 2015 USACE Detroit District, Saugatuck Harbor, MI 27. 2015 USACE Detroit District, Oconto Harbor, WI 28. 2015 USACE Detroit District, White Lake Harbor, MI 29. 2015 USACE Detroit District, Manistique Harbor, MI 30. 2014 USACE Detroit District, Milwaukee Harbor, WI 31. 2014 USACE Detroit District, Frankfort Harbor, MI 32. 2014 USACE Detroit District, St. Joseph Harbor, MI 33. 2014 USACE Detroit District, Holland Harbor, MI 34. 2014 USACE Chicago District, Burns Waterway Harbor, IN 35. 2014 USACE Chicago District, Burns Small Boat Harbor, IN 36. 2014 USACE Chicago District, Michigan City, IN 37. 2014 USACE Chicago District, Waukegan Harbor, IL 38. 2014 USACE Chicago District, Calumet River, IL 39. 2014 USACE Detroit District, Menominee Harbor, MI/WI The DEM was produced from the following NPS multibeam sonar data sets: 40. 2011, National Park Service, Sleeping Bear Dunes National Lakeshore Multibeam Sonar 41. 2012, National Park Service, Sleeping Bear Dunes National Lakeshore Multibeam Sonar The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

************************In early 2025, the source of MCLIO public layers will change.*****************************

Please refer to these documents for changes:

https://mclio.maps.arcgis.com/home/item.html?id=0bb68bbae37445adb045d6a44fed3f2a

https://mclio.maps.arcgis.com/home/item.html?id=79c6c9d737c94753a388db7c6f480149

Please update maps, apps and data connections accordingly!This layer shows tax parcel polygons with all related data, including tax key, address, ownership, assessment values, school district, and legal description. Polygons are "stacked" so that there is one parcel polygon per tax key. Data is updated quarterly from the City of Milwaukee and annually from the surrounding municipalities.

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:

This data set was collected to provide wetland information for use in county, city, village, and town planning activities in the respective counties and for regional planning activities in Southeastern Wisconsin. This was clipped from a data set that consists of digital map files containing point and polygon features of wetland information covering Kenosha, Milwaukee, Ozaukee, Racine, Walworth, Washington, and Waukesha Counties. The files were compiled by the Southeastern Wisconsin Regional Planning Commission on behalf of the Wisconsin Department of Natural Resources as part of a cooperative agreement to update the Wisconsin Wetland Inventory in Southeastern Wisconsin. Each file covers one U.S. Public Land Survey System survey township, for a total of 82 files in the data set. The survey township files have also been merged into files covering entire counties. The digital map files were compiled with reference to one-inch-equals-100-feet-scale and one-inch-equals-200-feet-scale orthophotography acquired in the Spring of 2010, and also with reference to land use, vegetation, topographic, and soils information.

These wards were produced by the Legislative Technology Services Bureau for the 2011 Legislative Redistricting Project. Election data from the current decade is included.Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data AttributesWard Data Overview: These municipal wards were created by grouping Census 2010 population collection blocks into municipal wards. This project started with the release of Census 2010 geography and population totals to all 72 Wisconsin counties on March 21, 2011, and were made available via the Legislative Technology Services Bureau (LTSB) GIS website and the WISE-LR web application. The 180 day statutory timeline for local redistricting ended on September 19, 2011. Wisconsin Legislative and Congressional redistricting plans were enacted in 2011 by Wisconsin Act 43 and Act 44. These new districts were created using Census 2010 block geography. Some municipal wards, created before the passing of Act 43 and 44, were required to be split between assembly, senate and congressional district boundaries. 2011 Wisconsin Act 39 allowed communities to divide wards, along census block boundaries, if they were divided by newly enacted boundaries. A number of wards created under Wisconsin Act 39 were named using alpha-numeric labels. An example would be where ward 1 divided by an assembly district would become ward 1A and ward 1B, and in other municipalities the next sequential ward number was used: ward 1 and ward 2. The process of dividing wards under Act 39 ended on April 10, 2012. On April 11, 2012, the United States Eastern District Federal Court ordered Assembly Districts 8 and 9 (both in the City of Milwaukee) be changed to follow the court’s description. On September 19, 2012, LTSB divided the few remaining municipal wards that were split by a 2011 Wisconsin Act 43 or 44 district line.Election Data Overview: Election data that is included in this file was collected by LTSB from the Government Accountability Board (GAB)/Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. The ward data that is collected after each decennial census is made up of collections of whole and split census blocks. (Note: Split census blocks occur during local redistricting when municipalities include recently annexed property in their ward submissions to the legislature).Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the GAB/WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the GAB at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2011) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election Results: The process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from GAB/WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards spring 2017 (Census 2010 totals used for disaggregation)Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (Occurred with spring 2017) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2016Blocks 2011 [with all election data] -> Wards 2011 (then MCD 2011, and County 2011) All election data (including later elections such as 2016) is aggregated to the Wards 2011 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though municipal and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same. Therefore, data totals within a county should be the same between 2011 wards and 2018 wards.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2018. This is due to (a) boundary corrections in the data from 2011 to 2018, and (b) annexations, where a block may have been reassigned.

There is a newer and more authoritative version of this layer here! It is owned by the University of Richmond's Digital Scholarship Lab and contains data on many more cities.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. This layer includes 7,148 neighborhoods spanning 143 cities across the continental United States. NOTE: 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. More cities are available in this feature layer from University of Richmond.Cities included in this layerAlabama: Birmingham, Mobile, MontgomeryCalifornia: Fresno, Los Angeles, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: DenverConnecticut: East Hartford, New Britain, New Haven, StamfordFlorida: Jacksonville, Miami, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Chattanooga, Columbus, MaconIllinois: Aurora, Chicago, Decatur, Joliet, GaryIndiana: Evansville, Fort Wayne, Indianapolis, Gary, Muncie, South Bend, Terre HauteKansas: Greater Kansas City, WichitaKentucky: Lexington, LouisvilleLouisiana: New OrleansMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Haverhill, Holyoke Chicopee, Lexington, Malden, Medford, Melrose, Milton, Needham, Newton, Quincy, Revere, Saugus, Somerville, Waltham, Watertown, Winchester, WinthropMaryland: BaltimoreMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Kalamazoo, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Duluth, MinneapolisMissouri: Greater Kansas City, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Greensboro, Winston SalemNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen Co., Camden, Essex County, Hudson County, TrentonNew York: Bronx, Brooklyn, Buffalo, Elmira, Binghamton/Johnson City, Lower Westchester Co., Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Staten Island, Syracuse, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorrain, Portsmouth, Springfield, Toledo, Warren, YoungstownOregon: PortlandPennsylvania: Altoona, Erie, Johnstown, New Castle, Philadelphia, PittsburghSouth Carolina: AugustaTennessee: Chattanooga, KnoxvilleTexas: DallasVirginia: Lynchburg, Norfolk, Richmond, RoanokeWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Milwaukee, Oshkosh, RacineWest Virginia: Charleston, WheelingAn example of a map produced by the HOLC of Philadelphia: