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.

翻译：党派不公正选区划分（即为了政治利益操纵选举区边界）是当代民主国家选举诚信面临的主要挑战之一。然而，现有的大多数检测党派不公正选区划分的方法都狭隘地针对完全竞争的两党选举，当存在多个政党或每个选区的候选人数量不同时（美国以外许多基于相对多数制的选举体系即如此），这些方法便会失效。我们提出了两种基于非参数统计学习的方法，能够处理此类情况。多种方法的使用使得所提出的解决方案对其各自假设的违背具有鲁棒性。随后，我们利用来自17个采用简单多数制（FPTP）体系的国家及次国家选举的真实数据对提出方法进行了测试。