Colorado in Context: Ensemble Analysis and Data Science for Fair Redistricting in Colorado

Welcome! We are a research team of mathematicians, statisticians, and computer scientists working to apply mathematical and data-focused methods to fair redistricting in Colorado.

Our Work

Analysis of Prospective 2021 Districts

Slides for October 7 Presentation to Colorado Independent Legislative Redistricting Commission.

Addendum to Congressional Redistricting Report, including analysis of Congressional plan adopted by Independent Congressional Redistricting Commission.

Ensemble Analysis for 2021 Legislative Redistricting in Colorado, First and Second Staff Plans, by Jeanne Clelland, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa. A report comparing Colorado’s First and Second Staff Maps for State Legislative districts to a large ensemble of randomly generated maps. Uses 2020 precinct data and election data from 2016-2020. 2-Page Summary of Report.

Ensemble Analysis for 2021 Congressional Redistricting in Colorado, by Jeanne Clelland, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa. An initial report comparing Colorado’s First Staff Map for Congressional districts to a large ensemble of randomly generated random maps. Uses 2020 precinct data and election data from 2016-2020.


Analysis of 2011 Districts

Colorado in context: Congressional redistricting and competing fairness criteria in Colorado, by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa. Published in Journal of Computational Social Science, May 2021. An in-depth ensemble analysis of Colorado’s 2011 enacted Congressional districts, using 2018 election data. Reach published version here.

Colorado in context II: Legislative redistricting and competing fairness criteria in Colorado, by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa. Supplementary report to above paper examining 2011 enacted state legislative districts in Colorado, using 2018 election data.


Supplementary Data Analysis

Evaluating a Vote Band Metric for Competitiveness in Colorado Districts, by Jeanne Clelland, Daryl DeFord, Abigail Ezell, Beth Malmskog, and Flavia Sancier-Barbosa. An analysis of how well past election results being “close” (Republican and Democrat vote returns within 5%, 10%, or 15% of one another) predicts a district actually changing hands between decennial censuses. Considers previous two decades of Colorado districts. Incorporates data and analysis from a senior thesis by Abigail Ezell.

Evaluating a Proportional Population Distribution Model: How Well Can We Expect Preliminary Maps to Achieve Equal Population? by Austin Eide, Beth Malmskog, Kadin Mangalik, Jose Monge-Castro, and Edgar Santos-Vega, with Haley Colgate, Jeanne Clelland, Daryl Deford, and Flavia Sancier-Barbosa. An analysis of accuracy of using population totals from a coarse-grained division of the state (like counties) to predict population at a fine-grained level (like voting precincts or census blocks).


About Us

Jeanne Clelland

Dr. Clelland is a Professor in the Department of Mathematics at University of Colorado Boulder. She received her Ph.D. from Duke University in 1996 and completed a National Science Foundation Postdoctoral Research Fellowship at the Institute for Advanced Study prior to joining the faculty at CU-Boulder in 1998.  Her research focuses on differential geometry and applications of geometry to the study of differential equations, and more recently on mathematical topics related to redistricting.  Dr. Clelland is the author of the textbook From Frenet to Cartan: The Method of Moving Frames, and she is the 2018 winner of the Burton W. Jones Distinguished Teaching Award from the Rocky Mountain Section of the Mathematical Association of America.


Daryl DeFord

Dr. DeFord is an assistant professor in the Department of Mathematics and Statistics at Washington State University. His research program focuses on applying algebraic and combinatorial methods to the analysis of social data with an emphasis on applications of discrete sampling techniques to political redistricting and social network models. During Summer 2021 he led a team of research fellows at the University of Washington e-Sciences Institute’s Data Science for Social Good program focused on applying computational tools to evaluating newly created districting plans. Prior to joining the faculty at WSU, he completed a postdoctoral position at MIT in the Geometric Data Processing Group while collaborating with the Metric Geometry and Gerrymandering Group on mathematical modeling of political geography and developing open-source software for detecting and combating gerrymandering. Dr. DeFord completed his Ph.D. at Dartmouth College with a thesis evaluating dynamical models for complex networks.


Beth Malmskog

Dr. Malmskog is an assistant professor in the Department of Mathematics and Computer Science at Colorado College. Her research is in number theory, algebraic geometry, and applied discrete mathematics. She began working on mathematical aspects of fair redistricting in 2019. Dr. Malmskog earned her PhD at Colorado State University. She serves on the Board of Directors for the League of Women Voters of Colorado.


Flavia Sancier-Barbosa

Dr. Sancier-Barbosa is an assistant professor in the Department of Mathematics and Computer Science at Colorado College. Her research is in applied statistics, stochastic processes, applied probability, and usually involves interdisciplinary collaborations. Her work on statistical analyses for fair redistricting began in 2019. Dr. Sancier-Barbosa earned her PhD at Southern Illinois University Carbondale. 


Student Contributors

2020-2021

Colorado College: Dominic Altamura, Sam Caro, Lilly Davis, Abigal Ezell, Josmary Fernandez, Joshua Kalenga, Casmali Lopez, Bright Throngprastertchai.


2019-2020

Colorado College: Haley Colgate, Jose Monge Castro, Austin Eide, Kadin Mangalik, Edgar Santos-Vega.

University of Colorado Boulder: Nicholas Bossenbroek, Thomas Heckmaster, Adam Nelson, Peter Rock, and Jade VanAusdall.