Date of Award

Fall 2019

Degree Type

Restricted to Claremont Colleges Dissertation

Degree Name

Political Science and Information Systems and Technology, PhD

Program

School of Social Science, Politics, and Evaluation

Advisor/Supervisor/Committee Chair

Melissa Rogers

Dissertation or Thesis Committee Member

Brian N. Hilton

Dissertation or Thesis Committee Member

June K. Hilton

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2019 Roger J Chin

Subject Categories

Criminology | Geographic Information Sciences | Public Policy

Abstract

The New York City Police Department (NYPD) stop-and-frisk policy is a contentious crime prevention strategy that has increased the mistrust and perceived antagonism between the police department and the community. The objective of this policy is to stop, question, and potentially frisk individuals as a proactive strategy to prevent crimes. Despite the noble intentions of reducing crime and recovering dangerous weapons, this policy has alienated minorities, in particular the African American and Hispanic populations. This program has been widely seen as discriminatory toward non-Whites, and this perception led to policy reforms in the hope that reducing police stops would decrease the number of discriminatory activities. In order to understand whether the policy reforms have mitigated the racial disparity in the enforcement of the NYPD stop-and frisk policy at the micro-spatial level, this transdisciplinary study contributes to the literature by using diverse methodologies from the fields of public policy, political science, criminology, criminal justice, statistics, and information systems. Specifically, this research will measure the racial disparity in the enforcement of this policy at the smallest geographic unit—the census block—to gain a concrete understanding of the geographic variation in policing and crime prevention. This research explores whether the policy reforms that were designed to reduce racial disparity by curtailing the total number of stops and frisks achieved their intended effect. This research takes a mixed-methods approach to examine complex issues in patrolling heterogeneous communities, to explore the pertinence of spatial and temporal factors in policing, and to scrutinize the outcomes of police contacts. The research will focus on the NYPD, but the analytical findings will be beneficial and germane in refining the policing tactics of other law enforcement agencies. The results of the space time cube analysis found that there were locations in New York City with statistically significant non-random distributions of NYPD pedestrian stops. Most of the persistent hot spots were located in the Manhattan, Bronx, and Brooklyn boroughs with a drastic decline in the amount of persistent hot spots starting in 2012. For all of the races and ethnicities that were a part of this study, the total number of persistent hot spots dropped to 0 in 2016. When exploring blocks that had persistent high enforcement locations for 7 or more years, this study found that these areas mostly occurred in Manhattan, Brooklyn, and Queens, with NYPD Precinct 14 having the most persistent high enforcement blocks. The panel and ordinary least squares (OLS) regression analyses indicated that the rate of ethnic heterogeneity, number of vacant homes, percent of individuals who were renting, percent of individuals 18 years old or older, and percent of males were statistically significant predictors of the stop rate at the block level. Ethnic heterogeneity and vacant homes caused a decrease in the number of stops in a block while the percent of renters, percent of people 18 years old or older, and percent male caused an increase in the stop rates in a block. Despite the outcomes and challenges of this study, this exploratory research on the NYPD stop-and-frisk policy revealed the impediments of conducting policing research at the block level due to the paucity of available data and the necessity of collecting reliable policing data for micro-spatial analysis in the future.

Download

Share

COinS