Suchergebnisse

Abstract
We examine the factors influencing police response times, with a particular focus on staffing levels, calls for service (CFS), and proactive police work. We estimate Bayesian Holt-Winters state-space models for each CFS priority level. Using a novel dataset that combines data from the Salt Lake City Police Department's staffing and Computer-Aided Dispatch (CAD) systems at the daily level over seven years, we estimate the effects that staffing, overtime, call volume, and the level of proactive work (e.g., traffic stops, pedestrian stops, business checks) have on police response times. Our findings indicate that the impact of staffing on response times is significantly greater than that of other independent variables in the models. Furthermore, improvements in response times for higher-priority (i.e., more serious) CFS have a lower elasticity response to increases in staffing levels. As police agencies face increasingly complex challenges, the empirical evidence presented herein serves as a cornerstone for making informed decisions in the intricate balancing act of resources, officer well-being, and public safety priorities.

AbstractThe police murder of George Floyd sparked nationwide protests in the summer of 2020 and revived claims that public outcry over such high‐profile police killings perpetuated a violent "war on cops." Using data collected by the Gun Violence Archive (GVA) on firearm assaults of U.S. police officers, we use Bayesian structural time series (BSTS) modeling to empirically assess if and how patterns of firearm assault on police officers in the United States were influenced by the police murder of George Floyd. Our analysis finds that the murder of George Floyd was associated with a 3‐week spike in firearm assaults on police, after which the trend in firearms assaults dropped to levels only slightly above that which were predicted by pre‐Floyd data. We discuss potential explanations for these findings and consider their relevance to the contemporary discussion of a "war on cops," violence, and officer safety.

AbstractAdministrative discretion can range from benign to troubling, and law enforcement officers possess the power to use physical violence in the discharge of their duties. Body‐worn cameras (BWCs) are a workplace surveillance technology intended to monitor officer behavior in the field, but officers exercise discretion over whether or not to activate their cameras. So, what drives officers to activate BWCs? Combining unique survey and administrative data, three competing explanations of BWC activation are compared in one department: Officer demographics, job function, and attitudes. Job function covariates offer robust predictive power of BWC activation frequency. Demographics do not predict BWC activations except rank, which negatively correlates with activation. Though the bulk of attitudinal measures do not predict BWC activations, negative relationships are noted with how officers perceive BWCs to impact their professional discretion, and their belief that cameras expose officers to public outrage and disapproval.

AbstractMany U.S. cities witnessed both de‐policing and increased crime in 2020, yet whether the former contributed to the latter remains unclear. Indeed, much of what is known about the effects of proactive policing on crime comes from studies that evaluated highly focused interventions atypical of day‐to‐day policing, used cities as the unit of analysis, or could not rule out endogeneity. This study addresses each of these issues, thereby advancing the evidence base concerning the effects of policing on crime. Leveraging two exogenous shocks presented by the onset of the coronavirus 2019 (COVID‐19) pandemic and social unrest after the murder of George Floyd, we evaluated the effects of sudden and sustained reductions in high‐discretion policing on crime at the neighborhood level in Denver, Colorado. Multilevel models accounting for trends in prior police activity, neighborhood structure, seasonality, and population mobility revealed mixed results. On the one hand, large‐scale reductions in stops and drug‐related arrests were associated with significant increases in violent and property crimes, respectively. On the other hand, fewer disorder arrests did not affect crime. These results were not universal across neighborhoods. We discuss the implications of these findings in light of debates concerning the appropriate role of policing in the 21st century.

AbstractLaw enforcement agencies are increasingly adopting artificial intelligence (AI)‐powered tools. While prior work emphasizes the technological features driving public opinion, we investigate how public trust and support for AI in government vary with the institutional context. We administer a pre‐registered survey experiment to 4200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national Federal Bureau of Investigation), algorithmic targets (i.e., public targets via predictive policing versus detecting officer misconduct through automated case review), and agency capacity (i.e., necessary resources and expertise). We find that the public clearly prefers local over national law enforcement use of AI, while reactions to different algorithmic targets are more limited and politicized. However, we find no responsiveness to agency capacity or lack thereof. The findings suggest the need for greater scholarly, practitioner, and public attention to organizational, not only technical, prerequisites for successful government implementation of AI.