Gerrymandering in Utah

• Worked with Dr. Tyler Jarvis, Annika King, Jake Callahan

• December 2019 - April 2021

• Paper "Mathematical Analysis of Redistricting in Utah" published in Statistics and Public Policy

Summary of Research

• Used Markov Chain Monte Carlo methods and Metropolis-Hastings sampling to sample from the space of valid districting plans

• Determined which metrics of gerrymandering are the most effective and relevant

• Obtained, processed, and cleaned geographic data and election data

Talks

• February 2021, BYU Student Research Conference

• February 2020, BYU Student Research Conference

GitHub Repository

https://github.com/jwmurri/MathematicalElectionAnalysis

Paper Abstract

We investigate the claim that the Utah congressional districts enacted in 2011 represent an unfair Republican gerrymander, and we evaluate the most common measures of partisan fairness and gerrymandering in the context of Utah's congressional districts. We do this by generating large ensembles of alternative redistricting plans using Markov chain Monte Carlo methods. We also propose a new metric of partisan fairness in Utah, namely, the Republican vote share in the least-Republican district. This metric only makes sense in settings with at most one competitive district, but it is very effective for quantifying gerrymandering and for evaluating other metrics used in such settings.

Annika King, Jacob Murri, Jake Callahan, Adrienne Russell & Tyler J. Jarvis (2022) Mathematical Analysis of Redistricting in Utah, Statistics and Public Policy, DOI: 10.1080/2330443X.2022.2105770