Our Mission

The MGGG Redistricting Lab is a research group at the Brooks School of Public Policy at Cornell University, led by PI Moon Duchin, that grew out of an informal research collective called the Metric Geometry and Gerrymandering Group. From 2018-2024, the Lab was based in the Tisch College of Civic Life at Tufts University, where we maintain an affiliation. Together with our close collaborators, we have expertise and interests spanning mathematics (geometry, topology, discrete math, dynamics), algorithms, software development, geography, science studies, law, and policy. We research data science interventions – better mechanisms, better models, better metrics – for democracy.

Our goals are these:

• to pursue cutting-edge research in the basic science and practically relevant applications of geometry, topology, and computing to the design of democratic mechanisms, including but not limited to redistricting and ranked choice voting;
• to build open-source tools and resources that create public access and analytical power for better understanding election systems 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

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  Recombination and other samplers

  MGGG

  07 November 2024

  Redistricting is the problem of partitioning a set of geographical units into a fixed number of districts, subject to a list of often-vague rules and priorities. In recent years, the use of randomized methods to sample from the vast space of districting plans has been gaining traction in courts of law for identifying partisan gerrymanders, and it was used as an analytical tool by several legislatures and independent commissions.

  The mathematics of sampling graph partitions is interesting in its own right, and there has been enormous progress in recent years. Most of the new methods use an idea that comes from this research group: that spanning trees provide a computationally efficient way to partition a graph, because deleting a single edge cuts the graph into two pieces. We introduced the idea in 2018 and released a supporting software package called GerryChain the same year. Now the spanning tree partition step can be found in several different kinds of samplers, including those introduced by Jonathan Mattingly’s group at Duke and Kosuke Imai’s group at Harvard.

  Here are a few key papers from the lab on samping graph partitions…

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  Major updates to Gerry-Suite

  MGGG

  28 May 2024

  Since the beginning of the year, we’ve made several major updates to our main suite of redistricting analysis tools.

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  A newer, smarter geography package

  MGGG

  27 May 2024

  We are excited to announce a major update of the Python package MAUP, our geospatial tookit for redistricting data. The name of this package comes from the “modifiable areal unit problem” in geography: the same spatial data will look different depending on how you divide up a space into units.

The MGGG Redistricting Lab is a nonpartisan research organization located within the Jonathan M. Tisch College of Civic Life at Tufts University.