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This article introduces the
50stateSimulations, a collection of simulated congressional
districting plans and underlying code developed by the
Algorithm-Assisted Redistricting Methodology (ALARM)
Project. The 50stateSimulations allow for the evaluation of
enacted and other congressional redistricting plans in the
United States. While the use of redistricting simulation
algorithms has become standard in academic research and court
cases, any simulation analysis requires non-trivial efforts to
combine multiple data sets, identify state-specific
redistricting criteria, implement complex simulation
algorithms, and summarize and visualize simulation outputs. We
have developed a complete workflow that facilitates this entire
process of simulation-based redistricting analysis for the
congressional districts of all 50 states. The resulting
50stateSimulations include ensembles of simulated 2020
congressional redistricting plans and necessary replication
data. We also provide the underlying code, which serves as a
template for customized analyses. All data and code are free
and publicly
available. This article details the design,
creation, and validation of the data. |
McCartan, Cory, Christopher Kenny, Tyler
Simko, Emma Ebowe, Michael Zhao, and Kosuke Imai. ``Redistricting Reforms Reduce Gerrymandering
by Constraining Partisan Actors.'' |
Kenny, Christopher T., Cory McCartan,
Tyler Simko, Shiro Kuriwaki, and Kosuke Imai. (2023). ``Widespread Partisan Gerrymandering Mostly
Cancels Nationally, but Reduces Electoral Competition
.'' Proceedings of the National Academy of
Sciences, Vol. 120, No. 25, e2217322120. |
Fifield, Benjamin, Michael Higgins, Kosuke
Imai, and Alexander Tarr. (2020). ``Automated Redistricting Simulation Using
Markov Chain Monte Carlo.'' Journal of
Computational and Graphical Statistics, Vol. 29, No. 4,
pp. 715-728. |
Fifield, Benjamin, Kosuke Imai, Jun
Kawahara, and Christopher T. Kenny. (2020). ``The Essential Role of Empirical
Validation in Legislative Redistricting Simulation.''
Statistics and Public Policy, Vol. 7, No. 1, pp
52-68. |
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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