The Project
The ALARM Project
is a research team at Harvard University led by Kosuke Imai. It conducts
research into redistricting sampling algorithms, best practices and
workflows for redistricting analysis, and tools to visualize, explore,
and understand redistricting plans. ALARM Project researchers develop redist,
an open-source R package for redistricting simulation and analysis which
implements state-of-the-art MCMC and SMC redistricting sampling
algorithms. The package allows for the implementation of various
constraints in the redistricting process such as geographic compactness
and population parity requirements, and includes tools to compute
various summary statistics and create useful plots.
People
- Ben Fifield, American Civil Liberties Union
- George Garcia III, Department of Economics, Massachusetts Institute of Technology
- Kosuke Imai, Departments of Government and Statistics, Harvard University
- Christopher Kenny, Department of Government, Harvard University
- Shiro Kuriwaki, Department of Political Science, Stanford University
- Sho Miyazaki, Keio University
- Cory McCartan, Department of Statistics, Harvard University
- Evan Rosenman, Harvard Data Science Initiative
- Tyler Simko, Department of Government, Harvard University
- Sam Thau, Harvard College
- Kevin Wang, Harvard College
- Melissa Wu, Harvard College
- Kento Yamada, Harvard College
- Rei Yatsuhashi, Harvard College
- Anna Yorozuya, University of Tokyo
Publications
- Automated Redistricting Simulation Using Markov Chain Monte Carlo Journal of Computational and Graphical Statistics (2020) Vol. 29, No. 4, pp. 715-728.
- The Essential Role of Empirical Validation in Legislative Redistricting Simulation Statistics and Public Policy (2020) Vol. 7, No. 1, pp. 52-68.
- Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans
- The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census Science Advances (2021) Vol. 7, No. 41.
Related R Packages
- blockpop: Estimate Census Block Populations for 2020. McCartan (2021).
- geomander: Geographic Tools for Studying Gerrymandering. Kenny (2021).
- ggredist: Scales, Palettes, and Extensions of ‘ggplot2’ for Redistricting . Kenny and McCartan (2022).
- PL94171: Tabulate P.L. 94-171 Redistricting Data Summary Files. McCartan and Kenny (2021).
- ppmf: Read Census Privacy Protected Microdata Files. Kenny (2021).
- redist: Simulation Methods for Legislative Redistricting. Kenny, McCartan, Fifield, and Imai (2021).
- redistmetrics: Redistricting Metrics. Kenny, McCartan, Fifield, and Imai (2022).
- wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation. Khanna and Imai (2021).