Our Mission

The MGGG Redistricting Lab is a research group at Tisch College of Tufts University that grew out of an informal research collective called the Metric Geometry and Gerrymandering Group. The Lab is led by PI Moon Duchin. Our staff has expertise and interests spanning mathematics (geometry, topology, discrete math, dynamics), algorithms, software development, geography, STS, and policy. We research data science interventions for civil rights.

Our goals are these:

• to pursue cutting-edge research in the basic science and practically relevant applications of geometry, topology, and computing to the redistricting problem;
• to build open-source tools and resources that create public access and analytical power for better understanding districts and their consequences;
• to partner with civil rights organizations to reexamine and strengthen the quantitative toolkit for protecting voting rights;
• to offer formal and informal expert consulting to stakeholders on all sides.

Highlights

• 

  Differential Privacy and Redistricting

  MGGG

  14 April 2021

  The 2020 Decennial Census will be released with a new disclosure avoidance system in place, putting differential privacy in the spotlight for a wide range of data users. We consider several key applications of Census data in redistricting, developing tools and demonstrations for practitioners who are concerned about the impacts of this new noising algorithm called TopDown. Based on a close look at reconstructed Texas data, we find reassuring evidence that TopDown will not threaten the ability to produce districts with tolerable population balance or to detect signals of racial polarization for Voting Rights Act enforcement.

• 

  Voting Rights Act Research

  MGGG

  17 February 2021

  We have several new projects looking at possible futures for the Voting Rights Act of 1965. One is a new collaborative paper by data scientists Amariah Becker, Moon Duchin, and Dara Gold with voting rights attorney Sam Hirsch. BDGH build a new system for scoring a district’s opportunity for minority groups to elect candidates of choice—with this effectiveness score, they are able to generate large ensembles of maps with many effective districts. The main case study is the Texas congressional map, where these methods are shown to find maps that provide as much, or much more, electoral opportunity than the status quo.
  Read the preprint here.

  In another piece of work, Moon Duchin and law/policy professor Doug Spencer team up to respond to “The Race-Blind Future of Voting Rights”, a piece by Jowei Chen and Nick Stephanopoulos in the Yale Law Journal. Duchin–Spencer take a close look at the methods, and also at the fit of the methods to the application, and find some problems. Here’s the response.

• 

  Ranked Choice Voting and Minority Representation

  MGGG

  12 February 2021

  In a new preprint, we outline and demonstrate a data-driven methodology that voting rights advocates can use to understand likely impacts of ranked choice voting for securing minority representation in local government. We incorporate both election data and demographics, with variable assumptions on candidate availability and voter turnout. The core of our analysis uses four models of voter ranking behavior that take racial polarization into account; to assess districts, we use random district-generation algorithms developed at the Lab. We focus on the case of STV or “single transferable vote,” where multiple officials are elected from a single district, because this is known to promote proportionality.

The MGGG Redistricting Lab is a nonpartisan research organization. MGGG has major support from the Jonathan M. Tisch College of Civic Life at Tufts University and the Amar G. Bose Research Grant Program at MIT and an active partnership with the Lawyers' Committee for Civil Rights Under Law.