UCLA

Gerrymandering, the term used to describe the drawing of electoral districts to favor one political party or group of people over another, has become a pressing issue with a developing academic interest. A chief concern associated with gerrymandering is how to identify and quantify it in a way that is understandable and actionable. There have been many proposed methods to quantify the effects of gerrymandering, ranging from studies of compactness, analyses using simulated election and geographic data, and measures of partisan symmetry. These measures and methods help to establish and quantify the impacts of gerrymandering but often are not enough to help in prevention. We will define, implement and examine a new statistic that measures the geographic compactness and similarity of racial make-ups for each electoral district. The goal of our statistic is to create a measure that can detect geographic or demographic irregularities in any existing or proposed districts that might suggest manipulation. This statistic could be used to help judge the fairness of proposals during the redistricting processes by helping to identify proposed districts that are drawn in an unintuitive way that might seek to favor one party over another.