Algorithm-Assisted Redistricting Methodology (ALARM) Project

Dataverse of the Algorithm-Assisted Redistricting Methodology (ALARM) Project

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1 to 5 of 5 Results

Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods" Nov 5, 2024 Kenny, Christopher; McCartan, Cory; Kuriwaki, Shiro; Simko, Tyler; Imai, Kosuke, 2024, "Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"", https://doi.org/10.7910/DVN/TMIN3H, Harvard Dataverse, V3 The United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm employed for the 2020 Census and the swapping algorithm...
47-Prefecture Redistricting Simulations Sep 3, 2024 Miyazaki, Sho; Yamada, Kento; Yatsuhashi, Rei; Imai, Kosuke, 2022, "47-Prefecture Redistricting Simulations", https://doi.org/10.7910/DVN/Z9UKSH, Harvard Dataverse, V3, UNF:6:p19IftGnCWCsP62d33rkSQ== [fileUNF] The goal of the 47-Prefecture Simulation Project is to generate and analyze redistricting plans for the single-member districts of the House of Representatives of Japan using a redistricting simulation algorithm. In this project, we analyzed the partisan bias of the 2022 redistricting for 25 prefectures subject to redistricting. Our simulations are...
50-State Redistricting Simulations May 2, 2023 McCartan, Cory; Kenny, Christopher T.; Simko, Tyler; Kuriwaki, Shiro; Garcia, George, III; Wang, Kevin; Wu, Melissa; Imai, Kosuke, 2021, "50-State Redistricting Simulations", https://doi.org/10.7910/DVN/SLCD3E, Harvard Dataverse, V14, UNF:6:T2SKZdDrEKt7cKDAK9/IFQ== [fileUNF] Every decade following the Census, states and municipalities must redraw districts for Congress, state houses, city councils, and more. The goal of the 50-State Simulation Project is to enable researchers, practitioners, and the general public to use cutting-edge redistricting simulation analysis to evaluate enacted congressional districts. Evaluat...
Replication Data for: Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition Mar 31, 2023 Kenny, Christopher; McCartan, Cory; Simko, Tyler; Kuriwaki, Shiro; Imai, Kosuke, 2023, "Replication Data for: Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition", https://doi.org/10.7910/DVN/JI1U8X, Harvard Dataverse, V1, UNF:6:nYlhzGaxtdVnHl7IeCQbCA== [fileUNF] Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of...
Replication Data for: The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census Jun 30, 2022 - Harvard Dataverse Kenny, Christopher T.; Kuriwaki, Shiro; McCartan, Cory; Rosenman, Evan; Simko, Tyler; Kosuke, Imai, 2021, "Replication Data for: The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census", https://doi.org/10.7910/DVN/TNNSXG, Harvard Dataverse, V4 Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, cens...

Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"

Nov 5, 2024

Kenny, Christopher; McCartan, Cory; Kuriwaki, Shiro; Simko, Tyler; Imai, Kosuke, 2024, "Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"", https://doi.org/10.7910/DVN/TMIN3H, Harvard Dataverse, V3

The United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm employed for the 2020 Census and the swapping algorithm...

47-Prefecture Redistricting Simulations

Sep 3, 2024

Miyazaki, Sho; Yamada, Kento; Yatsuhashi, Rei; Imai, Kosuke, 2022, "47-Prefecture Redistricting Simulations", https://doi.org/10.7910/DVN/Z9UKSH, Harvard Dataverse, V3, UNF:6:p19IftGnCWCsP62d33rkSQ== [fileUNF]

The goal of the 47-Prefecture Simulation Project is to generate and analyze redistricting plans for the single-member districts of the House of Representatives of Japan using a redistricting simulation algorithm. In this project, we analyzed the partisan bias of the 2022 redistricting for 25 prefectures subject to redistricting. Our simulations are...

50-State Redistricting Simulations

May 2, 2023

McCartan, Cory; Kenny, Christopher T.; Simko, Tyler; Kuriwaki, Shiro; Garcia, George, III; Wang, Kevin; Wu, Melissa; Imai, Kosuke, 2021, "50-State Redistricting Simulations", https://doi.org/10.7910/DVN/SLCD3E, Harvard Dataverse, V14, UNF:6:T2SKZdDrEKt7cKDAK9/IFQ== [fileUNF]

Every decade following the Census, states and municipalities must redraw districts for Congress, state houses, city councils, and more. The goal of the 50-State Simulation Project is to enable researchers, practitioners, and the general public to use cutting-edge redistricting simulation analysis to evaluate enacted congressional districts. Evaluat...

Replication Data for: Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition

Mar 31, 2023

Kenny, Christopher; McCartan, Cory; Simko, Tyler; Kuriwaki, Shiro; Imai, Kosuke, 2023, "Replication Data for: Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition", https://doi.org/10.7910/DVN/JI1U8X, Harvard Dataverse, V1, UNF:6:nYlhzGaxtdVnHl7IeCQbCA== [fileUNF]

Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of...

Replication Data for: The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census

Jun 30, 2022 - Harvard Dataverse

Kenny, Christopher T.; Kuriwaki, Shiro; McCartan, Cory; Rosenman, Evan; Simko, Tyler; Kosuke, Imai, 2021, "Replication Data for: The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census", https://doi.org/10.7910/DVN/TNNSXG, Harvard Dataverse, V4

Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, cens...

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