Simulated redistricting plans for the analysis and evaluation of redistricting in the United States

Abstract

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.

The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level

“…Our validation using data from Florida is the best setting for a comparison of the accuracy of EI and our survey-based approach. However, in Appendix B.4 of the Supplementary Material, we obtain EI estimates for all 435 districts based on election data produced by the Voting and Election Science Team (2020) and precinct-level race statistics from the 2020 Census prepared by McCartan et al (2022), and show where our estimates differ. In the average district, EI differs from our survey estimates by over 10 percentage points for white voters and Black voters, and over 20 percentage points for Hispanic voters.…”

LocalView, a database of public meetings for the study of local politics and policy-making in the United States

“…The few large-scale datasets that do exist on local governments, which are generally government releases such as the US Census of Governments for municipalities or the Common Core for school districts from the National Center for Education Statistics, may contain structural and administrative characteristics but are generally insufficient for scholars interested in topics like policy-making and deliberations. A recent explosion of datasets in the social sciences has led to unprecedented, large-scale study of U.S. politics, elections, and policy-making at the national [5][6][7][8] and state levels 9,10 . Meanwhile, most contemporary studies of local policy-making rely primarily on case studies or small sets of individual places 11,12 , lab experiments 13 , or have required extensive (and expensive) manual data collection [14][15][16][17] .…”

A Demographic Approach to Detecting Racial Gerrymanders in U.S. Congressional Districts

“…We use the ensembles produced from the redist package (Kenny et al, 2021) for a variety of reasons. First, a team at Harvard used the redist algorithm to produce and freely distribute ensembles for all states with more than two U.S. House Districts for 2022 (McCartan et al, 2022). These data are available at the Harvard Dataverse under the name Algorithmically-Assisted Redistricting Methodology-or "ALARM."…”