New waves of statisticians, including a team at Harvard, have developed tools they think can help police the longstanding problem of gerrymandering of congressional and legislative districts in states by parties seeking to tip the scales for their candidates.
Gerrymandering has been part of American politics since the 1800s, with results that are at the very least controversial and sometimes illegal, particularly if it's done to dilute the voting power of communities of color. The battle is renewed every decade in state legislatures when Census figures show which districts need to be rebalanced owing to population shifts. Sometimes violations appear blatant but often they are more subtle, and in either case they can be difficult to prove.
Graph looks at how each district of Ohio’s enacted plan compares to the set of simulated districts. Credit: Algorithm-Assisted Redistricting Methodology Project The redist software uses what's called the sequential Monte Carlo algorithm to run its simulations. The software starts with a blank map, then draws one district map at a time, then does it again and again. Each alternative map is drawn in parallel and incorporates that state's population,, and districting laws. Once these alternative maps are drawn, the redist software uses visualization tools to help users understand the data with charts and graphs summarizing the simulations.
Redist has been used by plaintiffs in gerrymandering cases, including actions in Alabama, New York, Ohio, and South Carolina. In New York, an elections analyst used the SMC algorithm to produce 10,000 maps to help show the map drawn by New York state's Democratic legislature was an"'extreme outlier' that likely reduced the number of Republican congressional seats from eight to four." A state Appeals Court ordered the map be redrawn.
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