Bryce Elling Peterson and Daniel S. Lawrence
Body-worn cameras (BWCs) are small devices that police officers can affix to their person—in a head-, shoulder-, or chest-mounted position—that can audio and video record their interactions with community members. BWCs have received strong support from the public and, in recent years, widespread buy-in from police leadership and officers because of their ability to improve accountability and transparency and enhance the collection of evidence. Implementation guidelines recommend that officers activate their BWCs during each officer–citizen interaction and inform the people they encounter that they are being recorded. Early research on this technology found that officers equipped with body cameras were significantly less likely to engage in force and receive citizen complaints. However, more recent studies with larger samples have had mixed findings about the impact of body cameras on use of force, citizen complaints, and other police activities and behaviors.
Numerous legal and ethical considerations are associated with BWCs, including their implications for privacy concerns and public disclosure. However, police officials, policymakers, civil rights groups, and the public must continue to weigh these privacy concerns against the potential for BWCs to enhance police accountability and transparency. Future scholarship should focus on the degree to which BWCs can improve police–community relations and yield valuable evidence for both criminal cases and internal investigations.
David Weisburd and Sean Wire
Hot spots of crime, and the criminology of place more generally, deviate from the traditional paradigm of criminology, in which the primary assumption and goal is to explain who is likely to commit crime and their motivations, and to explore interventions aimed at reducing individual criminality. Alternatively, crime hot spots account for the “where” of crime, specifically referring to the concentration of crime in small geographic areas. The criminology of place demands a rethinking in regard to how we understand the crime problem and offers alternate ways to predict, explain, and prevent crime. While place, as large geographic units, has been important since the inception of criminology as a discipline, research examining crime concentrations at a micro-geographic level has only recently begun to be developed. This approach has been facilitated by improvements to data availability, technology, and the understanding of crime as a function of the environment. The new crime and place paradigm is rooted in the past three decades of criminological research centered on routine activity theory, crime concentrations, and hot spots policing.
The focus on crime hot spots has led to several core empirical findings. First, crime is meaningfully concentrated, such that a large proportion of crime events occur at relatively few places within larger geographies like cities. This may be termed the law of crime concentration at places (see Weisburd, 2015). Additionally, most hot spots of crime are stable over time, and thus present promising opportunities for crime prevention. Crime hot spots vary within higher geographic units, suggesting both that there is a loss of information at higher levels of aggregation and that there are clear “micro communities” within the larger conceptualization of a neighborhood. Finally, crime at place is predictable, which is important for being able to understand why crime is concentrated in one place and not another, as well as to develop crime prevention strategies. These empirical characteristics of crime hot spots have led to the development of successful police interventions to reduce crime. These interventions are generally termed hot spots policing.
John E. Eck
Place management theory—a part of routine activity theory—explains why a relatively few places have a great deal of crime while most places have little or no crime. The explanation is the way place managers carry out their four primary functions: organization of space, regulation of conduct, control of access, and acquisition of resources.
Place managers are those people and organizations that own and operate businesses, homes, hotels, drinking establishments, schools, government offices, places of worship, health centers, and other specific locations. They can even operate mobile places such as busses, trains, ships, and aircraft. Some are large—a multi-story office tower for example—while others are tiny—a bus stop, for example. Place managers are important because they can exercise control over the people who use these locations and in doing so contribute to public order and safety. Consequently, it is important to understand place management, how it can fail, and what one can do to prevent failures.
Place management has implications beyond high-crime sites. When crime places are connected, they can create crime hot spots in an area. The concentration of high crime places can inflate crime in a neighborhood. Moreover, place management can be applied to virtual locations, such as servers, websites, and other network infrastructures.
There is considerable evaluation evidence that place managers can change high crime locations to low crime locations. Research also shows that displacement to other places, though possible, is far from inevitable. Indeed, research shows that improving a high crime place can reduce crime at places nearby places. Although much of this research has studied how police intervene with place managers, non-police regulatory agencies can carry out this public safety function.
Predictive policing, also known as crime forecasting, is a set of high technologies aiding the police in solving past crimes and pre-emptively fighting and preventing future ones. With the right deployment of such technologies, law enforcement agencies can combat and control crime more efficiently with time and resources better employed and allocated. The current practices of predictive policing include the integration of various technologies, ranging from predictive crime maps and surveillance cameras to sophisticated computer software and artificial intelligence. Predictive analytics help the police make predictions about where and when future crime is most likely to happen and who will be the perpetrator and who the potential victim. The underpinning logic behind such predictions is the predictability of criminal behavior and crime patterns based on criminological research and theories such as rational choice and deterrence theories, routine activities theory, and broken windows theory.
Currently many jurisdictions in the United States have deployed or have been experimenting with various predictive policing technologies. The most widely adopted applications include CompStat, PredPol, HunchLab, Strategic Subject List (SSL), Beware, Domain Awareness System (DAS), and Palantir. The realization of these predictive policing analytics systems relies heavily on the technological assistance provided by data collection and integration software, facial/vehicle identification and tracking tools, and surveillance technologies that keep tabs on individual activities both in the physical environment and in the digital world. Some examples of these assisting technologies include Automatic License Plate Recognition (ALPR), Next-Generation Identification (NGI) System, the Global Positioning System (GPS), Automatic Vehicle Location (AVL), next-generation police body-worn cameras (BWC) with facial recognition and tracking functions, aerial cameras and unmanned aircraft systems, DeepFace, Persistent Surveillance Systems, Stingrays/D(i)RT-Box/International Mobile Subscriber Identity Catcher, SnapTrends that monitors and analyzes feeds on Twitter, Facebook, Instagram, Picasa, Flickr, and YouTube.
This new fashion of using predictive analytics in policing has elicited extensive doubt and criticism since its invention. Whereas scholarly evaluation research shows mixed findings about how effectively predictive policing actually works to help reduce crime, other concerns center around legal and civil rights issues (including privacy protection and the legitimacy of mass surveillance), inequality (stratified surveillance), cost-effectiveness of the technologies, militarization of the police and its implications (such as worsened relationship and weakened trust between the police and the public), and epistemological challenges to understanding crime. To make the best use of the technologies and avoid their pitfalls at the same time, policymakers need to consider the hotly debated controversies raised in the evolution of predictive policing.