Abstract by Cristina Lange

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Cristina Lange

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Tyler Jarvis
Annika King
Jacob Murri

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Faculty Advisor

Tyler Jarvis


Gerrymandering in Utah: Relevance and Leveraging Data


We present a mathematical analysis of Utah's 2012 political redistricting, using Markov-chain Monte Carlo methods to construct a large ensemble of alternative district plans that satisfy the legal requirements of contiguous districts with equal population. Over the last several years gerrymandering has increasingly come up in federal courts and it has become pertinent to find a standardized method that can be used as evidence in trials. We chose Utah since it already had precinct data and shape files of the districts. We employed arcgis to help us format the data into the precincts. Using MCMC methods  to generate new plans, we were then able to create a measure to identify gerrymandering in the state.



I want to present in the same session as Annika King and Jacob Murri, preferably In order of me, Annika, and Jacob.

Also I resubmitted my proposal to be included in the data science session.