Bringing spatial interaction measures into multi-criteria assessment of redistricting plans using interactive web mapping.

Redistricting is the process by which electoral district boundaries are drawn so as to capture coherent communities of interest (COIs). While states rely on various proxies for community illustration, such as compactness and municipal split counts, to guide redistricting, recent legal challenges and scholarly works have shown the difficulty of balancing multiple criteria in district plan creation. To address these issues, we propose the use of spatial interaction to directly quantify the degree to which districts capture the underlying COIs. Using large-scale human mobility flow data, we condense spatial interaction community capture for a set of districts into a single number, the interaction ratio (IR), for redistricting plan evaluation. To compare the IR to traditional redistricting criteria (compactness and fairness), we employ a Markov chain-based regionalization algorithm (ReCom) to produce ensembles of valid plans and calculate the degree to which they capture spatial interaction communities. Furthermore, we propose two methods for biasing the ReCom algorithm towards different IR values. We perform a multi-criteria assessment of the space of valid maps, and present the results in an interactive web map. The experiments on Wisconsin congressional districting plans demonstrate the effectiveness of our methods for biasing sampling towards higher or lower IR values. Furthermore, the analysis of the districts produced with these methods suggests that districts with higher IR and compactness values tend to produce district plans that are more proportional with regard to seats allocated to each of the two major parties. [ABSTRACT FROM AUTHOR]