Date of Award

January 2019

Degree Type

Open Access Thesis

Document Type

Master Thesis

Degree Name

Master of Science (MS)


Mathematics and Statistics

First Advisor

Lisa Whitis Kay

Department Affiliation

Mathematics and Statistics

Second Advisor

Shane P. Redmond

Department Affiliation

Mathematics and Statistics

Third Advisor

Michelle L. Smith

Department Affiliation

Mathematics and Statistics


Partisan gerrymandering has been and will continue to be a topic of interest in the coming years. States will soon begin their redistricting process following the 2020 Census. We introduce a method of simulating Congressional elections which provides a new way of examining and visualizing the votes-to-seats relationship for a state Congressional map using past election data. We are able to build upon Mira Bernstein's method of uniformly simulating elections by injecting a data-driven component of variation into the simulations. Additionally, we are able to directly evaluate the accuracy of our simulations using a type of cross-validation. We compare our results from a handful of notable states to other measures of partisan gerrymandering, such as the efficiency gap, and do so in light of recent court cases and other important contexts.