An opinion piece in CommonWealth Magazine with Ruth Greenwood.
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).
Our paper which details gerrymandering and partisan fairness in the 2022 redistricting maps is now published in PNAS.
Our letter providing recommendations to the Census Bureau about the Noisy Measurements File (NMF) now published in Science.
Our paper describing partisan gerrymandering and competition in the 2022 US congressional districts is now forthcoming in PNAS.
A medium-sized release with more flexible plotting, better diagnostics, and speed improvements.
Our response to boyd and Sarathy (2022) is now published in the HDSR!
Our software won the Society for Political Methodology's Statistical Software Award.
Now published at Nature Scientific Data.
We're excited to announce our forthcoming article discussing boyd and Sarathy (2022).
Our article in Nikkei Business on reducing Japanese malapportionment was released!
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.
A detailed description of the 50-State Redistricting Simulations and new software to help you use them.
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。
A major release with big changes to constraints and diagnostics.
Using redistricting simulation methods to better understand redistricting in Japan.
A new postscript analyzes the final version of the U.S. Census Bureau's Disclosure Avoidance System.
Census and election data joined together for use in redistricting and voting rights analysis.
We are releasing an updated version of our analysis of the U.S. Census' privacy protection system and its impacts on the redistricting process.
The Data Stewardship Executive Policy Committee announces a higher privacy loss budget and other changes to the Disclosure Avoidance System.
Answers to common questions about our recently-released report evaluating the Census' Disclosure Avoidance System.
In attempting to protect the privacy of 2020 Census respondents, the Census Bureau has made its data unsuitable for redistricting purposes.
A major release brings new algorithms, new workflows, and significant usability improvements.
Comprehensive project to simulate alternative congressional redistricting plans for all fifty states in the 2022 redistricting cycle.
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.
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.