Little House (Seat) on the Prairie: Compactness, Gerrymandering, and Population Distribution

Gerrymandering is the process of creating electoral districts for partisan advantage, allowing a party to win more seats than what is reasonable for their vote. While research on gerrymandering has recently grown, many issues are still not fully understood such as what influences the degree to which a party can gerrymander and what techniques can be used to counter it. One commonly suggested (and, in some US states, mandated) requirement is that districts be “geographically compact”. However, there are many competing compactness definitions and the impact of compactness on the gerrymandering abilities of the parties is not well understood. Also not well understood is how the growing urban-rural divide between supporters of different parties impacts redistricting. We develop a modular, scalable, and efficient algorithm that can design districts for various criteria. We confirm its effectiveness on several US states by pitting it against maps “hand-drawn” by political experts. Using real data from US political elections we use our algorithm to study the interaction between population distribution, partisanship, and geographic compactness. We find that compactness can lead to more fair plans (compared to implemented plans) and limit gerrymandering potential, but there is a consistent asymmetry where the party with rural supporters has an advantage. We also show there are plans which are fair from a partisan perspective, but they are far from optimally compact.