The University of Richmond’s Mapping Inequality project shares the data used by the federal government in enacting policies of redlining throughout the 20th century. As a project, and particularly the interactive map, it works as a piece of historical research to better understand the relationship between race, ethnicity, and housing law in segregated America. This is especially seen with the data included below the clarifying remarks section, which makes it clear that areas with more immigrants and African-Americans were assigned lower scores.
This project began with the National Archives’ records of Home Owners Loan Corporation (HOLC) data. The maps and data contained in these records are a strong indicator of racial bias in the housing system, but were only available in print. To make them more easily accessible, the researchers scanned each file. They then rotated the maps to align with the normal cardinal directions of cities, and converted them into a JSON shapefile to make them more easily manipulated. Lastly, they uploaded the maps to a map of the United States, and annotated the blocks with the descriptions taken from the HOLC records.
One question I have is whether text analysis could be used to enhance the data. By searching for correlations between mentions of ethnic groups that were seen as lowering house prices and the ratings of the areas where those groups were mentioned, would a clear pattern emerge? This is just one example of further research that could be done more efficiently now that all of the HOLC data has been digitized.
