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Representatives are chosen geographically in the USA, requiring governments to design districts. With the US census, new political districts must be drawn. The practice of harnessing this administrative process for partisan political gain is often referred to as gerrymandering.
How does one identify/understand gerrymandering? Can we recognize gerrymandering when we see it? What is fair? How does the geopolitical geometry of the state inform these answers?
Comparing to a collection of “neutral” computer-generated, redistricting maps as a normative baseline has quickly become a standard for identifying and understanding gerrymandering. It has been used to advise governors and legislatures and in court proceedings. The Duke group has been involved a number of cases that have led to the redrawing of both federal and state-level redistricting maps for the 2020 elections.
Understanding gerrymandering has prompted the development of new computational algorithms that come with new mathematical/statistical questions. One group of questions led to developing a collection of methods around Markov Chain Monte Carlo sampling of high-dimensional distributions on the space of redistricting.
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