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.
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.
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.
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
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.
A comprehensive collection on police and policing, written by experts in political theory, sociology, criminology, economics, law, public health, and critical theory.
***Author Radio Interview Join Dr. Frank A. Colaprete for an upcoming interview on the Privacy Piracy show on KUCI 88.9FM. Click here on September 2nd, 2013 at 8:00 a.m. PST to listen in. Pre-employment investigations have been the subject of intense review and debate since 9/11 made the vetting of applicants a critical function of every organization. Nowhere has the scrutiny been more intense than in the public safety sector. Pre-Employment Background Investigations for Public Safety Professionals provides readers with the knowledge, investigative techniques, applicable laws, decision-making models, and tools to successfully implement and manage the process of pre-employment investigation. The book focuses on six key topics: Practical implications of pre-employment investigation The pre-employment screening process Legal issues in the hiring process Medical and psychological standards of pre-employment screening Informational sources and the final investigative package The past predicting the future of pre-employment investigations Each chapter begins with learning objectives and key terms and concepts. Discussion questions and exercises appear at the end of each chapter to test readers’ assimilation of the material. A comprehensive review of all the issues faced in the investigation and hiring process, this volume assists all stakeholders in the hiring arena by highlighting the critical steps involved in vetting a prospective employee. While no screening process can be completely failsafe, this volume enables decision makers to move confidently through the hiring process, quickly weeding out the most likely problematic hires so that the ideal employee can be selected.
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.
Divided into four sections—public safety agencies, key issues like interoperability and cybercrime, management skills, and emerging trends like the transfer of military technologies to civilian agencies, Managing Public Safety Technology illustrates how essential managing technology is to the success of any project. Based on the authors’ years of experience dealing with information systems and other tools, this book offers guidance for line personnel, supervisors, managers, and anyone dealing with public safety technology. Designed for current or future public safety personnel, especially those in management, Managing Public Safety Technology can also be used for undergraduate and graduate public safety management and leadership programs.
"A Comprehensive Framework for Adapting National Intelligence for Domestic Law Enforcement" is a groundbreaking book that delves into the intricate process of integrating sophisticated national intelligence methodologies into domestic law enforcement practices. Authored by a seasoned expert in the field of intelligence, this book emerges as a critical resource for military leaders, policymakers, members of the intelligence community, and law enforcement personnel. This insightful work begins by exploring the historical evolution of intelligence sharing, offering a thorough analysis of past and present strategies. It then seamlessly transitions into discussing the current challenges and opportunities faced in integrating national intelligence into domestic law enforcement. The book provides an in-depth examination of legal and ethical frameworks, ensuring that the proposed methods adhere to the highest standards of civil liberties and legal compliance. Central to the book is the development of a comprehensive framework that bridges the gap between national intelligence operations and local law enforcement requirements. This framework not only addresses operational aspects but also focuses on the technological advancements, such as AI and big data analytics, reshaping intelligence gathering and analysis. The author brings to light the importance of cross-sector collaboration, suggesting innovative ways to enhance cooperation between various sectors – government, private, and non-profit – in intelligence activities. Case studies of successful intelligence collaboration, both domestic and international, are meticulously analyzed, offering practical insights and lessons learned. Moreover, the book addresses the training and skill development necessary for effectively adapting national intelligence practices in a domestic context. It emphasizes the need for continuous professional development and the cultivation of a learning culture within law enforcement agencies. "A Comprehensive Framework for Adapting National Intelligence for Domestic Law Enforcement" concludes with strategic recommendations for policy and practice, advocating for a progressive approach towards intelligence integration. This book is an invaluable asset for anyone involved in or interested in the intersection of national security, intelligence, and domestic law enforcement, providing a comprehensive guide to navigating this complex and evolving landscape.
