Using Modeling To Predict And Prevent Victimization
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Using Modeling to Predict and Prevent Victimization
Author: Ken Pease
language: en
Publisher: Springer Science & Business Media
Release Date: 2014-01-09
This work provides clear application of a new statistical modeling technique that can be used to recognize patterns in victimization and prevent repeat victimization. The history of crime prevention techniques range from offender-based, to environment/situation-based, to victim-based. The authors of this work have found more accurate ways to predict and prevent victimization using a statistical modeling, based around crime concentration and sub-group profiling with regard to crime vulnerability levels, to predict areas and individuals vulnerable to crime. Following from this prediction, they propose policing strategies to improve crime prevention based on these predictions. With a combination of immediate actions and longer-term research recommendations, this work will be of interest to researchers and policy makers in focused on crime prevention, police studies, victimology and statistical applications.
Handbook of Crime Prevention and Community Safety
This second edition of the Handbook of Crime Prevention and Community Safety provides a completely revised and updated collection of essays focusing on the theory and practice of crime prevention and the creation of safer communities. This book is divided into five comprehensive parts: Part I, brand new to this edition, is concerned with theoretical perspectives on crime prevention and community safety. Part II considers general approaches to preventing crime, including a new chapter on the theory and practice of deterrence. Part III focuses on specific crime prevention strategies, including a new chapter on regulation for crime prevention. Part IV focuses on the prevention of specific categories of crime and the fear they generate, including new chapters on organised crime and cybercrime. Part V considers the preventative process: the methods through which presenting problems can be analysed, responses formulated and implemented, and their effectiveness evaluated. Bringing together leading academics and practitioners from the UK, US, Australia and the Netherlands, this volume will be an invaluable reference for researchers and practitioners whose work relates to crime prevention and community safety, as well as for undergraduate and postgraduate courses in crime prevention.
Predictive Policing and Artificial Intelligence
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.