Mapping Inequality: Reflection

For this week’s blog reflection, I’m going to be analyizing the project Mapping Inequality: Redlining in New Deal America.

The goal of this project is to allow you to explore 1930s redlining maps and the history of racial and ethnic discrimination across the United States. You can also delve further into the area descriptions provided alongside these maps to look at trends of different types of discrimination in different areas and across the U.S.. The data used is scans of maps and area descriptions that are public domain and easily accesible through the project’s sidebar options. They also provide a GitHub link for additional data and metadata. This allows those interested in delving further into this topic to look directly at the data and make new conclusions that this project may not have noted.

Sources/Assets

• Scans of maps and area descriptions from City Survey Files, 1935-1940
• San Antonio map from University of Texas, San Antonio Special Collections
• Portland, Maine, map from Bowdoin College
• Historic Redlining Indicator for 2010 and 2020 US Census Tracts
• Additional GitHub data and metadata
  • The area description text in a csv file
  • Metadata for the various area description forms (as json) mapping labels to the data
  • The HOLC spatial data crosswalked to 2020 and 2010 census tracts (as geopackages and geojson files)

Processes/Services

The start of the process was these sources being digitally scanned. Then, the team georeferenced the images and digitally traced and color-coded the different segments of each digitized map. Each area map was atrributed with several fields for sorting purposes, including: area_id, city, state, city_survey, category, grade, label, commercial, industrial, residential, and fill/color. The project has been updated numerous times since its original creation, incorporating additional maps and area descriptions to further represent the history of redlining across the U.S..

Presentation/Display

This project is really intresting in that it has multiple distinct ways of representing this data and providing additional analysis.

The first is the map, which provides an in depth key describing not only what each symbol or color means, but some ways they can be explored as a starting off point for viewers who may be overwhelmed by the amount of options. You can toggle between two different views of the map, depending on what is easiest to read for you. Each node on the map is either a Home Owners’ Loan Corporation (HOLC) map which have a consistent grading system and color scheme or a orange dot represnting map’s created by other entities. The HOLC nodes show the percentage of the location allocated to the different area categories: Best, Still Desirable, Definitely Declining, and Hazardous. You can look at the trends in redlining across the country with this map and click on each node for more information about each map, the associated area descriptions, and the area demographics.

The second aspect to this project is the area descriptions search. It utilizes the digitally scanned area descriptions and each search generates a list of each area description with its area rating and location. Alongside that list is another map, but only with areas where there were instances of the search term selected. The intial search page also provides you with some example terms to guide the your initial exploration, such as different races and ethncities, discriminatory terms, classes, occupations, industries, and enviromental factors.

The final aspect of the project’s presentation are the read and teach about redlining sections. These are sources for further information and guides for teachers to use to teach about redlining to students.

A Question for the Future of the Project

A question that further exploration of this project sparked is could current day racial and ethnic demographic data be applied to the map so the impacts of redlining on current distributions of people? For example, I’m from Chicago and know about the extensive history of redlining that has led to the city still being quite segregated to this day. I think this could be an option to toggle on when closely examining a map, but this also causes me to wonder if there is some technical reason that explains why this feature was not implemented to begin with or if this conflicts with the intent of the project. Overall, this project was very interesting to delve further into and hopefully will be used in education throughout the country.