As the Supreme Court considers Gill v. Whitford, a challenge to the practice of partisan gerrymandering that may rewrite the rules used to draw congressional districts, a team of computer scientists has come up with a new algorithmic approach to redistricting that’s less political and more mathematical. In a paper posted on arXiv.org, the researchers describe a computerized method for dividing state populations evenly into compact polygonal districts that average six or fewer sides. The neatly arrayed districts are a stark contrast to gerrymandered districts, which are stretched and contorted to provide an overall congressional advantage for one political party or another. “What we’re trying to do is come up with a system that makes it hard to engineer districts for political gain,” said Philip Klein, a computer scientist at Brown University and a coauthor of the paper. “It doesn’t give the user much freedom in deciding how the lines are drawn, which we view as a good thing because that freedom can be abused.”
This certainly isn’t the first time researchers have applied computational methods to the problem of redistricting. “I think what our approach offers is a clean, simple mathematical definition of what makes a solution acceptable,” Klein said.
The approach that Klein and his colleagues brought to bear in redistricting comes from their research in clustering problems—specifically “k-means” clustering. K-means is generally called on to determine how limited resources can be distributed in an efficient fashion. It can be used, for example, to determine where fire stations should be located to best serve a collection of neighborhoods or where stores can be located to optimize convenience for customers.
Full Article: Researchers devise an algorithm to combat gerrymandering.