Introducing `alarmdata`

The first stable version of our package `alarmdata` is now on CRAN, introducing a data-focused package for using the outputs of ALARM Project research.

GUEST POST: Louisville/Jefferson County metro government (balance), Kentucky

An analysis of the electoral competitiveness of Louisville Metro Council Elections.

GUEST POST: Competitiveness in Charlotte, North Carolina

Analyzing competitiveness in Charlotte, North Carolina city council elections, as well as its influence on partisanship of each district.

GUEST POST: Virginia Beach City

As one of many lawsuits against at-large voting systems for districts in local elections, Virginia Beach City recently adopted a new City Council district map that eliminates at-large seats. We explore the new implemented plan.

Using big data to make elections fairer

An opinion piece in CommonWealth Magazine with Ruth Greenwood.

Working Paper: Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods

Our new working paper uses the new Noisy Measurement File release to understand bias and noise caused by swapping (1990-2010) and the TopDown algorithm (2020).

Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition Published in PNAS

Our paper which details gerrymandering and partisan fairness in the 2022 redistricting maps is now published in PNAS.

Letter: Researchers need better access to US Census data published in Science

Our letter providing recommendations to the Census Bureau about the Noisy Measurements File (NMF) now published in Science.

Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition Forthcoming in PNAS

Our paper describing partisan gerrymandering and competition in the 2022 US congressional districts is now forthcoming in PNAS.

redist 4.1

A medium-sized release with more flexible plotting, better diagnostics, and speed improvements.

Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System

Our response to boyd and Sarathy (2022) is now published in the HDSR!

redist Recieved POLMETH's 2022 Statistical Software Award

Our software won the Society for Political Methodology's Statistical Software Award.

50statesSimulations in Nature Scientific Data

Now published at Nature Scientific Data.

Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System

We're excited to announce our forthcoming article discussing boyd and Sarathy (2022).

'One vote disparity' can be improved with state-of-the-art algorithms

Our article in Nikkei Business on reducing Japanese malapportionment was released!

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.

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.

More articles »


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.

Explore the project and data »

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.

Go to package home page »

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.

Access the data »