Project leads: Daryl DeFord, Assistant Professor of Data Analytics in the Department of Mathematics and Statistics at Washington State University
Data scientists: Bernease Herman and Vaughn Iverson
DSSG fellows: Rowana Ahmed, Katherine Chang, Ryan Goehrung, Michael Souffrant. Get the participant bios here.
Project Summary: Gerrymandering, the drawing of legislative boundaries for improper purposes, is a fundamental threat to democracy. While the history of American gerrymandering extends back 200 years, representing a wide variety of specific aims and harms, recent advances in computational methods and data analysis have exacerbated this problem. However, these same circumstances have also allowed us to develop tools for evaluating proposed maps and detecting outliers. Our work this summer aims to create a better understanding of how these methods can be used for the effective design of new maps.
Additionally, in 2018 several states passed bills to reform their redistricting practices, and the For the People Act attempts to address some of these same issues on a national scale. Evaluating the likely impacts of this legislation is a complex problem that calls for detailed analysis of the laws and history of each state, directly incorporating the local political geographies. Since many of the currently implemented analysis techniques were developed and tested in the adversarial court setting, translating these techniques for this broader task will require new, creative ideas.
We intend to use modern computational methods and statistical techniques to evaluate potential tradeoffs between redistricting criteria and proposed methods for challenging newly drawn plans. Recent evidence shows that Markov chain Monte Carlo methods can be successfully implemented to establish baselines of neutrally-constructed maps, and this will be our main analytical tool. The research team will use the GerryChain software in Python to construct ensembles tailored to individual state’s geographies and rules, as well as analyzing the properties of the resulting maps with respect to the relevant legislative and litigation history.
The main objectives of this project are to provide recommendations about effective ways to incorporate sampling methodology into the initial design of redistricting plans and to evaluate the likely success of proposed legislative solutions. These results will be valuable to reform groups, legislators, and commissions and as the 2021 redistricting cycle is currently underway, there will also be opportunities to collaborate directly with many interdisciplinary groups working in this field representing perspectives of reform advocates, legal scholars, and social scientists. Beyond this empirical work, there is also significant space for the investigation of creative theoretical contributions to the mathematical study of this problem.
Fellows Rowana Ahmed and Katherine Chang presented the project with the talk “Bridging the Gap Between Computational Tools and User Adoption for Social Good: A Case Study in Political Redistricting Problems” at the Learning & Doing Data for Good (LDDG) conference at the University of Washington in September 2022.