JN Matthews

selected publications

1. 

   Aggregating Community Maps

   Erin Chambers, Moon Duchin, Ranthony A.C. Edmonds, Parker Edwards, JN Matthews, Anthony E. Pizzimenti, Chanel Richardson, Parker Rule, and Ari Stern

   In The 30th International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’22), Nov 2022

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   This paper is motivated by a practical problem: many U.S. states have public hearings on "communities of interest" as part of their redistricting process, but no state has as yet adopted a concrete method of spatializing and aggregating community maps in order to take them into account in the drawing of new boundaries for electoral districts. Below, we describe a year-long project that collected and synthesized thousands of community maps through partnerships with grassroots organizations and/or government offices. The submissions were then aggregated by geographical clustering with a modified Hausdorff distance; then, the text from the narrative submissions was classified with semantic labels so that short runs of a Markov chain could be used to form semantic sub-clusters. The resulting dataset is publicly available, including the raw data of submitted community maps as well as post-processed community clusters and a scoring system for measuring how well districting plans respect the clusters. We provide a discussion of the strengths and weaknesses of this methodology and conclude with proposed directions for future work.

2. 

   Private Numbers in Public Policy: Census, Differential Privacy, and Redistricting

   Aloni Cohen, Moon Duchin, JN Matthews, and Bhushan Suwal

   Harvard Data Science Review, Jun 2022

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   The 2020 Decennial Census in the United States was 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 nine localities in Texas and Arizona, we find reassuring evidence that TopDown did not threaten the ability to balance districts, to describe their demographic composition accurately, or to detect signals of racial polarization.

3. 

   Census TopDown: The Impacts of Differential Privacy on Redistricting

   Aloni Cohen, Moon Duchin, JN Matthews, and Bhushan Suwal

   In 2nd Symposium on Foundations of Responsible Computing (FORC 2021), May 2021

   Abs PDF Code

   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.

4. 

   Voting Rights, Markov Chains, and Optimization by Short Bursts

   Sarah Cannon, Ari Goldbloom-Helzner, Varun Gupta, JN Matthews, and Bhushan Suwal

   Methodology and Computing in Applied Probability, Feb 2023

   Abs arXiv HTML Code

   Finding extreme outliers in probability distributions can be a hard problem. Taking a real example from Voting Rights Act enforcement, we consider the problem of maximizing the number of simultaneous majority-minority districts in a political districting plan. An unbiased random walk on districting plans is unlikely to find plans that approach this maximum. A common search approach is to use a biased random walk: preferentially select districting plans with more majority-minority districts. Here, we present a third option, called short bursts, in which an unbiased random walk is performed for a small number of steps (called the burst length), then re-started from the most extreme plan that was encountered in the last burst. We give empirical evidence that short-burst runs outperform biased random walks for the problem of maximizing the number of majority-minority districts. Abstracting from our use case, we also consider short bursts where the underlying state space is a line with various probability distributions, and then explore some features of more complicated state spaces and how these impact the effectiveness of short bursts. In all of these cases, we compare efficacy for various burst lengths.