  Partisan gerrymandering, i.e., manipulation of electoral district boundaries
for political advantage, is one of the major challenges to election integrity
in modern day democracies. Yet most of the existing methods for detecting
partisan gerrymandering are narrowly tailored toward fully contested two-party
elections, and fail if there are more parties or if the number of candidates
per district varies (as is the case in many plurality-based electoral systems
outside the United States). We propose two methods, based on nonparametric
statistical learning, that are able to deal with such cases. The use of
multiple methods makes the proposed solution robust against violation of their
respective assumptions. We then test the proposed methods against real-life
data from national and subnational elections in 17 countries employing the FPTP
system.
