Statistical Mapping and Redlining: A Milwaukee Case Study
By Jayne Kilander
US Federal Home Loan Bank Board. Map of Milwaukee County, Wisconsin: Residential Security Map, 1938. https://collections.lib.uwm.edu/digital/collection/agdm/id/3028/
In 1938, the Federal Home Loan Bank Board created the above map of Milwaukee County, ranking neighborhoods based on their “indexes of the over-all degrees of risk” (Light, 499) These categories were physical – like the overall quality and condition of buildings – but also social categories, such as the proportion of white residents in a neighborhood (Light, 502). The FHA made these maps for hundreds of cities across the United States, a process now referred to as redlining.
There has been much discussion about the repercussions of these neighborhood assessment maps, from de facto segregation that persists to this day, to other policies like public housing and freeway projects that have had lasting impacts on neighborhoods. The process of statistical mapping was essential to the process of redlining. The resulting map published by the Federal Home Loan Bank Board is an amalgam of other, more specific statistical maps that carved out the racial and class divides in the City of Milwaukee.
Milwaukee Board of Public Land Commissioners. City of Milwaukee Housing Survey, Residential Land Use, 1931. https://collections.lib.uwm.edu/digital/collection/agdm/id/25569/rec/3
This map demonstrates a statistical representation of residential land use in Milwaukee in 1931. The map displays the proportion of homes in different areas of the city based on what types of dwellings there are (single-family buildings, duplex buildings, and apartment buildings), as well as what types of units there are in those buildings. While this data was previously available from earlier US Census studies, the presentation of this data reflects a trend of graphically representing “moral statistics,” or important social values graded on a scale.
Statistical representation on maps made the information more accessible, while also granting a scientific level of legitimacy to the map that would serve as a base to neighborhood assessments. Friendly and Palsky explain how moral statistics are often reflected on a gradient scale, informed by a map from Charles Dupin, one of the first gradient representations of moral statistics in graphic cartographic form. In Dupin’s map, which displayed public education in France, uneducated areas were dark gray or black, while highly educated areas were in a lighter color (Ackerman and Karrow, 241). This association of darker colors with the “morally wrong” appears in this map of residential land use as well: the more valuable, single-family homes that reflected the traditional family model are represented by the white section of pie charts and bar diagrams in the map above, while the less valuable dual family, or stigmatized highly populated apartments are demonstrated by gray and black. This sets up a gradient of value, or pure single-family homes to dirty, dark apartment complexes. The graphic representation of the census and survey data on this base map of Milwaukee reflects a longer legacy of projecting and compartmentalizing social ideals and moral statistics into statistical mapping, demonstrating which land-uses were valuable, and which were undesirable.
Land-use and physical building occupancy were factors in the FHA grading system. Light writes of how Hoyt, one of the directors of risk assessment, describes land-use: “Hoyt instructed insuring officers to outline business, retail, warehouse, and industrial areas on a city base map, followed by residential districts with large apartment buildings and areas with one- and two-family dwellings.” (Light, 499) This was then followed by layering factors of rent and land-value, as well as race and ethnicity. Hoyt’s direction at the beginning of the mapping process highlights just how important the home type was in determining a neighborhood’s ranking, using data like the set displayed in the Milwaukee Residential Land Use Map as a foundation to compare other social factors that might pose a “risk” to the community. This map is just one example in a group of maps called the “City of Milwaukee Housing Survey” that also detailed information like the number of unlit streets, the number of “foreign born whites” in an area, population density, and blighted areas. These maps reflect the preliminary assessments of Milwaukee’s neighborhoods, laid out in an easily digestible, comparable fashion. Compiling and mapping all this data, the FHA, with teams like the one headed by Hoyt, were able to consider different aspects of neighborhoods to determine their “risks” and their benefits that might result in their eventual letter grading.
To see the impact of this map on the eventual FHA rating map, one only has to look on the surface at the color grades and gradient scales. Areas with high proportions of multi-family dwellings, mostly clustered around the first ring outside the center of the city, correlate directly with the same ring of red on the FHA map. Additionally, the lighter pie charts and bar graphics on the outer ring of the city correlate with the lighter green and yellow ratings in the same areas on the FHA map. There are some areas that do not directly align, like the strip of red ratings in the southwest of the city that lies in an area with mostly single-family homes, however this mismatch does not reflect a lack of consideration of the land-use map in planning, rather a cumulative analysis of multiple factors that influenced the FHA ratings. Other factors, like ethnicity or tax-default status, might be higher in those areas. The residential land-use map is just one piece of the puzzle in the final FHA rating map but highlights crucial details about how moral statistics and statistical mapping were used in the process of making such maps, and the social realities of those who lived in graded neighborhoods.
Works Cited:
Light, Jennifer. “Discriminating Appraisals: Cartography, Computation, and Access to Federal Mortgage Insurance in the 1930s.” Technology and Culture 52, no. 3 (2011): 485–522. http://www.jstor.org/stable/23020643.
Akerman, James R., and Robert W. Karrow. Maps : Finding Our Place in the World “Visualizing Nature and Society” / Edited by James R. Akerman and Robert W. Karrow, Jr. Chicago: University of Chicago Press, 2007. 241.
American Geographical Society Library 