Electoral Plot - Detecting Gerrymandering with Mathematics
On January 9th, a three-judge court declared North Carolina’s congressional map unconstitutionally gerrymandered, stating that the drawing of the state’s electoral districts gave an advantage to the Republican Party. The US Federal court cited the work of Jonathan Mattingly, Chair of the Mathematics Department at Duke University. Based on his research, Mattingly used an algorithm that generated over 24,000 redistricting maps. The results were clear - while using the same voting data, almost every map produced by Mattingly showed a larger number of wins for the Democratic Party.
North Carolina’s current redistricting was done in 2016 by the Republican majority in the US General Assembly. The Assembly filed an appeal, and the case will likely be brought to the US Supreme Court. Mattingly’s objective metrics will be essential as electoral districts will be redrawn based on the upcoming 2020 United States Census.
Along with his postdoctoral fellow Gregory Herschlag and a team of students, Mattingly uses sampling methods to estimate the entire population of admissible redistricting plans. They accomplish this by sampling a probability measure placed on compliant redistricting plans. You can read more about the mathematics employed in the recent SIAM article: "Detecting Gerrymandering with Mathematics."