CAREER: Mass Incarceration, Racial Segregation, and Spillover Effects in U.S. Communities

This project investigates the causes and consequences of mass incarceration within U.S. communities and neighborhoods. The overarching goal is to study how residential inequalities drive incarceration rates and disparities, and how neighborhood exposures to the criminal justice system affect the well-being and political participation of community members. The first part of the project evaluates and tests place-based mechanisms, specifically segregation and housing conditions, that may be driving disparities between demographic groups and shifts in incarceration over time. This part of the study seeks to understand how contemporary incarceration rates and disparities emerge from both historical residential patterns and recent changes in U.S. communities. The second part of the project examines how multiple, interrelated criminal justice system exposures, from contact to imprisonment, may accumulate to affect community well-being and political participation. An open criminal justice data project establishes a replicable system of data collection, processing, archiving, and analysis that coincides with an interdisciplinary educational program of courses, practicums, and symposia. The integrated research and educational aims of this project expand the participation of underserved students in STEM, share newly collected data on criminal justice exposures with the public, and provide useful insights for interventions addressing inequality and mass incarceration. To better understand rates of and disparities in criminal justice contact, as well as their influence on community well-being and democratic membership, this project focuses on the measurement and influence of place-based mechanisms—specifically, residential segregation and neighborhood effects. The two studies that comprise this project integrate data science methodologies, machine learning, spatial analysis, and quasi-experimental design to test theories relating segregation to mass incarceration and community-level spillover effects. To do so, these studies combine rarely accessible geocoded data on policing and incarceration with detailed individual-level data on voting and mortality. By combining top-down sociological theories of place and punishment with bottom-up data science and machine-learning techniques, this project brings new evidence to bear on these theories and contributes synergistic new methods to the fields of sociology, data science, and their application. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.