Evaluating Methods Used to Quantify Racial Segregation

Abstract: Racial segregation has long been a problem in communities across the United States, and in understanding how it is quantified we enhance our ability to offer proposals for eradication. Many metrics have been developed for measurement, but none fully capture the nuances of this complicated issue: This work provides an overview of four mathematical approaches that have been developed to study segregation, explains how they function, and compares/contrasts their effectiveness in various situations in order to determine which best succeeds. An additional focus lies in a case study of Los Angeles (LA) County. It was found that attempts to further standardize outputs erases crucial data, and compressing this issue into one score is not representative of its complexity. This suggests that future exploration should attempt to study segregation more comprehensively rather than distilling an incredibly complicated and important issue into a single statistic.

Mathematics

Audience: general audience

Comments: Zarina Dhillon is earning her Masters in Applied Statistics at NYU Steinhart with a concentration on data science for social impact. Zarina is also a Parke Research Fellow in the Brennan Center for Justice's Democracy Program, where she focuses on voter turnout and redistricting. She earned her Bachelors in Mathematics from Claremont McKenna College as a proud transfer student from Santa Barbara City College, where she earned nine associates degrees spanning economics, philosophy, communications, psychology, and mathematics.

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NYU CDS Math and Democracy Seminar

Series comments: The Math and Democracy Seminar features research on contact points between the mathematical sciences and the structure of democratic society. The purpose of the seminar is to stimulate mathematical activity on problems relating to democracy, and to foster interdisciplinary collaboration between mathematicians and other scholars and democratic stakeholders.

Examples of topics of interest include detection of gerrymandering, fairness and accountability of algorithms used in social decision-making, voting and apportionment theory, applications of statistics to discrimination law and the census, and mathematical modeling of democratic processes. The scope is not limited to these and is expected to expand as further applications emerge.

Seminars currently conducted via Zoom (with some events also in person). Look for links in individual talk descriptions.

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