This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks—networks of offenders who have committed crimes together—have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.

Winner, 2018 Law & Legal Studies PROSE Award The consequences of big data and algorithm-driven policing and its impact on law enforcement In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence. This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information. The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool. Andrew Guthrie Ferguson reveals how these new technologies —viewed as race-neutral and objective—have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices. After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias. But behind the data are real people, and difficult questions remain about racial discrimination and the potential to distort constitutional protections. In this first book on big data policing, Ferguson offers an examination of how new technologies will alter the who, where, when and how we police. These new technologies also offer data-driven methods to improve police accountability and to remedy the underlying socio-economic risk factors that encourage crime. The Rise of Big Data Policing is a must read for anyone concerned with how technology will revolutionize law enforcement and its potential threat to the security, privacy, and constitutional rights of citizens. Read an excerpt and interview with Andrew Guthrie Ferguson in The Economist.

Predict and Surveil offers an unprecedented, inside look at how police use big data and new surveillance technologies. Sarah Brayne conducted years of fieldwork with the LAPD--one of the largest and most technically advanced law enforcement agencies in the world-to reveal the unmet promises and very real perils of police use of data--driven surveillance and analytics.

Chapter “Predictive Policing in 2025: A Scenario” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

In a crucial shift within posthumanistic media studies, Bernhard Siegert dissolves the concept of media into a network of operations that reproduce, displace, process, and reflect the distinctions fundamental for a given culture. Cultural Techniques aims to forget our traditional understanding of media so as to redefine the concept through something more fundamental than the empiricist study of a medium’s individual or collective uses or of its cultural semantics or aesthetics. Rather, Siegert seeks to relocate media and culture on a level where the distinctions between object and performance, matter and form, human and nonhuman, sign and channel, the symbolic and the real are still in the process of becoming. The result is to turn ontology into a domain of all that is meant in German by the word Kultur. Cultural techniques comprise not only self-referential symbolic practices like reading, writing, counting, or image-making. The analysis of artifacts as cultural techniques emphasizes their ontological status as “in-betweens,” shifting from firstorder to second-order techniques, from the technical to the artistic, from object to sign, from the natural to the cultural, from the operational to the representational. Cultural Techniques ranges from seafaring, drafting, and eating to the production of the sign-signaldistinction in old and new media, to the reproduction of anthropological difference, to the study of trompe-l’oeils, grids, registers, and doors. Throughout, Siegert addresses fundamental questions of how ontological distinctions can be replaced by chains of operations that process those alleged ontological distinctions within the ontic. Grounding posthumanist theory both historically and technically, this book opens up a crucial dialogue between new German media theory and American postcybernetic discourses.

This edited text draws together the insights of numerous worldwide eminent academics to evaluate the condition of predictive policing and artificial intelligence (AI) as interlocked policy areas. Predictive and AI technologies are growing in prominence and at an unprecedented rate. Powerful digital crime mapping tools are being used to identify crime hotspots in real-time, as pattern-matching and search algorithms are sorting through huge police databases populated by growing volumes of data in an eff ort to identify people liable to experience (or commit) crime, places likely to host it, and variables associated with its solvability. Facial and vehicle recognition cameras are locating criminals as they move, while police services develop strategies informed by machine learning and other kinds of predictive analytics. Many of these innovations are features of modern policing in the UK, the US and Australia, among other jurisdictions. AI promises to reduce unnecessary labour, speed up various forms of police work, encourage police forces to more efficiently apportion their resources, and enable police officers to prevent crime and protect people from a variety of future harms. However, the promises of predictive and AI technologies and innovations do not always match reality. They often have significant weaknesses, come at a considerable cost and require challenging trade- off s to be made. Focusing on the UK, the US and Australia, this book explores themes of choice architecture, decision- making, human rights, accountability and the rule of law, as well as future uses of AI and predictive technologies in various policing contexts. The text contributes to ongoing debates on the benefits and biases of predictive algorithms, big data sets, machine learning systems, and broader policing strategies and challenges. Written in a clear and direct style, this book will appeal to students and scholars of policing, criminology, crime science, sociology, computer science, cognitive psychology and all those interested in the emergence of AI as a feature of contemporary policing.

Predictive policing is the use of analytical techniques to identify targets for police intervention with the goal of preventing crime, solving past crimes, or identifying potential offenders and victims. These tools are not a substitute for integrated approaches to policing, nor are they a crystal ball. This guide assesses some of the most promising technical tools and tactical approaches for acting on predictions in an effective way.

From random security checks at airports to the use of risk assessment in sentencing, actuarial methods are being used more than ever to determine whom law enforcement officials target and punish. And with the exception of racial profiling on our highways and streets, most people favor these methods because they believe they’re a more cost-effective way to fight crime. In Against Prediction, Bernard E. Harcourt challenges this growing reliance on actuarial methods. These prediction tools, he demonstrates, may in fact increase the overall amount of crime in society, depending on the relative responsiveness of the profiled populations to heightened security. They may also aggravate the difficulties that minorities already have obtaining work, education, and a better quality of life—thus perpetuating the pattern of criminal behavior. Ultimately, Harcourt shows how the perceived success of actuarial methods has begun to distort our very conception of just punishment and to obscure alternate visions of social order. In place of the actuarial, he proposes instead a turn to randomization in punishment and policing. The presumption, Harcourt concludes, should be against prediction.

This innovative collection of original essays showcases the use of social networks in the analysis and understanding of various forms of crime. More than any other past research endeavor, the seventeen chapters in this book apply to criminology the many conceptual and methodological options from social network analysis. Crime and Networks is the only book of its kind that looks at the use of networks in understanding crime, and can be used for advanced undergraduate and beginner’s graduate level courses in criminal justice and criminology.

Proactive policing, as a strategic approach used by police agencies to prevent crime, is a relatively new phenomenon in the United States. It developed from a crisis in confidence in policing that began to emerge in the 1960s because of social unrest, rising crime rates, and growing skepticism regarding the effectiveness of standard approaches to policing. In response, beginning in the 1980s and 1990s, innovative police practices and policies that took a more proactive approach began to develop. This report uses the term "proactive policing" to refer to all policing strategies that have as one of their goals the prevention or reduction of crime and disorder and that are not reactive in terms of focusing primarily on uncovering ongoing crime or on investigating or responding to crimes once they have occurred. Proactive policing is distinguished from the everyday decisions of police officers to be proactive in specific situations and instead refers to a strategic decision by police agencies to use proactive police responses in a programmatic way to reduce crime. Today, proactive policing strategies are used widely in the United States. They are not isolated programs used by a select group of agencies but rather a set of ideas that have spread across the landscape of policing. Proactive Policing reviews the evidence and discusses the data and methodological gaps on: (1) the effects of different forms of proactive policing on crime; (2) whether they are applied in a discriminatory manner; (3) whether they are being used in a legal fashion; and (4) community reaction. This report offers a comprehensive evaluation of proactive policing that includes not only its crime prevention impacts but also its broader implications for justice and U.S. communities.