IATH NEWS
Data-Driven Approaches to Identifying a Gerrymander
March 16, 2018
The University of Virginia's Quantitative Collaborative (QC) will host Moon Duchin of Tufts University to speak on Wednesday, March 21st, 12-1:30pm in the Cocke Hall Library, on "Data-Driven Approaches to Identifying a Gerrymander." The event is co-sponsored by the Mathematics and Politics Departments.
Abstract: The courts have struggled for decades to decide when a districting plan is excessively skewed. New technical methods—comparing a plan to an ensemble drawn from the space of all possible maps—might provide the needed breakthrough, and have had an excellent track record in courts in the last year. I'll survey the recent developments and turn an eye on Wisconsin, North Carolina, and Pennsylvania.
Prof. Duchin is an associate professor of Mathematics, a senior fellow in the Jonathan M. Tisch College of Civic Life, and the director of the Program in Science, Technology, and Society at Tufts University. Her pure math work is in geometry, topology, groups, and dynamics. Her work in applied pure math is focused on redistricting: she is currently collaborating with civil rights organizers, coders, political scientists, lawyers, geographers, and philosophers on a large-scale project to detect and address gerrymandering. In 2016, she founded the Metric Geometry and Gerrymandering Group, a Boston-based team of mathematicians studying applications of geometry and computing to U.S. redistricting. She recently served as a consulting expert for Governor Tom Wolf of Pennsylvania in the court-ordered scramble to remake PA's congressional map.
QC provides an organizational umbrella that supports faculty and graduate students who employ quantitative methods to analyze social behavior. it aims to encourage nascent partnerships across the quantitative social sciences in the exploration of new avenues of research, and in tackling key issues. It looks to build informal, interdisciplinary networks and disseminate the most recent advances in the field to faculty, graduate students, and undergraduates.
