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
Current Membership
- Aneetej Arora, Ohio State University
- Emma Ebowe, Department of Government, Harvard University
- Lucy Ding, Harvard College
- Ben Fifield, Meta
- Yasmeen Hanon, University of Missouri Kansas City
- Kosuke Imai, Departments of Government and Statistics, Harvard University
- Ethan Jasny, Harvard College
- Christopher T. Kenny, Department of Government, Harvard University
- Shiro Kuriwaki, Department of Political Science, Yale University
- Yusuf Mian, Harvard College
- Sho Miyazaki, Stanford University
- Cory McCartan, Center for Data Science, New York University
- Philip O’Sullivan, Department of Statistics, Harvard University
- Tyler Simko, Department of Government, Harvard University
- Itsuki Umeyama, University of Tokyo
- Melissa Wu, Harvard College
- Kento Yamada, Harvard College
- Angeline Zhao
- Michael Zhao, Harvard College
- Brian Zhou
Project Alumni
- Jennifer Gao, Harvard College
- George Garcia III, Department of Economics, MIT
- Evan Rosenman, Mathematical Scienced Department, Claremont McKenna College
- Taran Samarth, Department of Political Science, Yale University
- Sam Thau, Department of Economics, Stanford University
- Kevin Wang, Oxford University
- Rei Yatsuhashi, Harvard College
- Anna Yorozuya, Department of Political Science, Yale University
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 Annals of Applied Statistics (2023) Vol. 17, No. 4.
- 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.
- Simulated redistricting plans for the analysis and evaluation of redistricting in the United States Scientific Data (2022) Vol. 9, No. 1.
- Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System Harvard Data Science Review (2023) Special Issue 2.
- Researchers Need Better Access to U.S. Census Data Science (2023) Vol. 380, No. 6648.
- Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition Proceedings of the National Academy of Sciences (2023) Vol 120, No. 25.
- Evaluating Bias and Noise Induced by the U.S. Census Bureau’s Privacy Protection Methods
Related R Packages
- alarmdata: Download, Merge, and Process Redistricting Data. McCartan, Kenny, Simok, Zhao, and Imai (2024).
- birdie: Estimating disparities when race is not observed. McCartan, Goldin, Ho, and Imai (2023).
- blockpop: Estimate Census Block Populations for 2020. McCartan (2021).
- censable: Making Census Data More Usable. Kenny (2021).
- easycensus: Quickly Find, Extract, and Marginalize U.S. Census Tables. McCartan (2022).
- 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).
- tinytiger: Lightweight Interface to TIGER/Line Shapefiles. Kenny and McCartan (2023).
- wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation. Khanna and Imai (2021).