With state legislatures nationwide preparing for the once-a-decade redrawing of voting districts, a research team has developed a better computational method to help identify improper gerrymandering designed to favor specific candidates or political parties.
In an article in the Harvard Data Science Review, the researchers describe the improved mathematical methodology of an open source tool called GerryChain. The tool can help observers detect gerrymandering in a voting district plan by creating a pool, or ensemble, of alternate maps that also meet legal voting criteria. This map ensemble can show if the proposed plan is an extreme outlier—one that is very unusual from the norm of plans generated without bias, and therefore, likely to be drawn with partisan goals in mind.
“We wanted to build an open-source software tool and make that available to people interested in reform, especially in states where there are skewed baselines,” said Daryl DeFord, assistant professor of mathematics at Washington State University and a co-lead author on the paper. “It can be an impactful way for people to get involved in this process, particularly going into this year’s redistricting cycle where there are going to be a lot of opportunities for pointing out less than optimal behavior.”
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