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

这个项目调查了美国社区和社区中大规模监禁的原因和后果。总体目标是研究居住不平等如何推动监禁率和差异，以及社区接触刑事司法系统如何影响社区成员的福祉和政治参与。该项目的第一部分评估和测试基于地方的机制，特别是隔离和住房条件，这些机制可能会导致人口群体之间的差异和监禁情况随时间的变化。这部分研究旨在了解当代监禁率和差异是如何从历史居住模式和美国社区最近的变化中产生的。该项目的第二部分审查从接触到监禁等多重相互关联的刑事司法系统风险如何累积起来影响社区福祉和政治参与。一个公开的刑事司法数据项目建立了一个可复制的数据收集、处理、存档和分析系统，该系统与跨学科的课程、实习和专题讨论会的教育计划相吻合。该项目的综合研究和教育目标是扩大服务不足的学生对STEM的参与，与公众分享新收集的刑事司法风险数据，并为解决不平等和大规模监禁的干预措施提供有用的见解。为了更好地了解刑事司法接触的比率和差异，以及它们对社区福祉和民主成员资格的影响，本项目侧重于基于地点的机制的衡量和影响，特别是住宅隔离和邻里效应。该项目的两项研究整合了数据科学方法、机器学习、空间分析和准实验设计，以测试隔离与大规模监禁和社区层面溢出效应有关的理论。为了做到这一点，这些研究结合了关于警务和监禁的难以获取的地理编码数据，以及关于投票和死亡率的详细个人数据。通过将自上而下的地点和惩罚社会学理论与自下而上的数据科学和机器学习技术相结合，本项目为这些理论提供了新的证据，并为社会学、数据科学及其应用领域提供了协同的新方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.