Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition

Gerrymandering in 2020 redistricting makes the US House elections less competitive, but net seat gains are small nationally. The partisan bias of the enacted national map is about as biased as non-partisan simulations, due to geography and legal requirements.

Fifty States Data Descriptor

A detailed description of the 50-State Redistricting Simulations and new software to help you use them.

47-Prefectures at the Japanese Society of Quantitative Political Science

We are presenting Friday on malapportionment. 私たちは、アルゴリズムを用いた一票の格差の是正について、金曜日に発表します。

redist 4.0

A major release with big changes to constraints and diagnostics.

47-Prefecture Project

Using redistricting simulation methods to better understand redistricting in Japan.

Revised and published: The use of differential privacy for census data and its impact on redistricting

A new postscript analyzes the final version of the U.S. Census Bureau's Disclosure Avoidance System.

2020 Redistricting Data Files

Census and election data joined together for use in redistricting and voting rights analysis.

Revised: Impact of the Census Disclosure Avoidance System

We are releasing an updated version of our analysis of the U.S. Census' privacy protection system and its impacts on the redistricting process.

Reaction to the Census Bureau's Updated Parameters

The Data Stewardship Executive Policy Committee announces a higher privacy loss budget and other changes to the Disclosure Avoidance System.

FAQ: Impact of the Census Disclosure Avoidance System

Answers to common questions about our recently-released report evaluating the Census' Disclosure Avoidance System.

Impact of the Census Disclosure Avoidance System on Redistricting

In attempting to protect the privacy of 2020 Census respondents, the Census Bureau has made its data unsuitable for redistricting purposes.

redist 3.0

A major release brings new algorithms, new workflows, and significant usability improvements.

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Developing methodology and tools to analyze legislative redistricting.

50-State Redistricting Simulations

Comprehensive project to simulate alternative congressional redistricting plans for all fifty states in the 2022 redistricting cycle.

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The Algorithm-Assisted Redistricting Methodology (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.

redist: Simulation Methods for Legislative Redistricting

Enables researchers to sample redistricting plans from a pre-specified target distribution using state-of-the-art algorithms. Implements a wide variety constraints in the redistricting process, such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included.

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2020 Redistricting Data Files

Precinct-level demographic and election data from the 2020 decennial census and the Voting and Election Science Team which have been tidied and joined together using 2020 precinct boundaries.

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