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As granular data about elections and voters become available,
redistricting simulation methods are playing an increasingly
important role when legislatures adopt redistricting plans and
courts determine their legality. These simulation methods are
designed to yield a representative sample of all redistricting plans
that satisfy statutory guidelines and requirements such as
contiguity, population parity, and compactness. A proposed
redistricting plan can be considered gerrymandered if it constitutes
an outlier relative to this sample according to partisan fairness
metrics. Despite their growing use, an insufficient effort has been
made to empirically validate the accuracy of the simulation methods.
We apply a recently developed computational method that can
efficiently enumerate all possible redistricting plans and yield an
independent sample from this population. We show that this
algorithm scales to a state with a couple of hundred geographical
units. Finally, we empirically examine how existing simulation
methods perform on realistic validation data sets. |
Fifield, Benjamin, Michael Higgins, Kosuke
Imai, and Alexander Tarr. (2020). ``Automated Redistricting Simulator Using Markov
Chain Monte Carlo.'' Journal of Computational and
Graphical Statistics, Vol. 29, No. 4,
pp. 715-728. |
McCartan, Cory, and Kosuke
Imai. (2023). ``Sequential
Monte Carlo for Sampling Balanced and Compact Redistricting
Plans.'' Annals of Applied Statistics,
Vol. 17, No. 4 (December), pp. 3300-3323. |
Fifield, Benjamin, Christopher T. Kenny,
Cory MaCartan, and Kosuke Imai. ``redist: Computational
Algorithms for Redistricting Simulation.'' available
through The Comprehensive R
Archive Network and GitHub.
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