Posts
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.
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.
