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In the last decade there has been a phenomenal growth in interest in crime pattern analysis. Geographic information systems are now widely used in urban police agencies throughout industrial nations. With this, scholarly interest in understanding crime patterns has grown considerably. Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems discusses leading research on the use of computer simulation of crime patterns to reveal hidden processes of urban crimes, taking an interdisciplinary approach by combining criminology, computer simulation, and geographic information systems into one comprehensive resource.
This book discusses issues relating to the application of AI and computational modelling in criminal proceedings from a European perspective. Part one provides a definition of the topics. Rather than focusing on policing or prevention of crime – largely tackled by recent literature – it explores ways in which AI can affect the investigation and adjudication of crime. There are two main areas of application: the first is evidence gathering, which is addressed in Part two. This section examines how traditional evidentiary law is affected by both new ways of investigation – based on automated processes (often using machine learning) – and new kinds of evidence, automatically generated by AI instruments. Drawing on the comprehensive case law of the European Court of Human Rights, it also presents reflections on the reliability and, ultimately, the admissibility of such evidence. Part three investigates the second application area: judicial decision-making, providing an unbiased review of the meaning, benefits, and possible long-term effects of ‘predictive justice’ in the criminal field. It highlights the prediction of both violent behaviour, or recidivism, and future court decisions, based on precedents. Touching on the foundations of common law and civil law traditions, the book offers insights into the usefulness of ‘prediction’ in criminal proceedings.
Research into social systems is challenging due to their complex nature. Traditional methods of analysis are often difficult to apply effectively as theories evolve over time. This can be due to a lack of appropriate data, or too much uncertainty. It can also be the result of problems which are not yet understood well enough in the general sense so that they can be classified, and an appropriate solution quickly identified. Simulation is one tool that deals well with these challenges, fits in well with the deductive process, and is useful for testing theory. This field is still relatively new, and much of the work is necessarily innovative, although it builds upon a rich and varied foundation. There are a number of existing modelling paradigms being applied to complex social systems research. Additionally, new methods and measures are being devised through the process of conducting research. We expect that readers will enjoy the collection of high quality research works from new and accomplished researchers.
Sociological theories of crime include: theories of strain blame crime on personal stressors; theories of social learning blame crime on its social rewards, and see crime more as an institution in conflict with other institutions rather than as in- vidual deviance; and theories of control look at crime as natural and rewarding, and explore the formation of institutions that control crime. Theorists of corruption generally agree that corruption is an expression of the Patron–Client relationship in which a person with access to resources trades resources with kin and members of the community in exchange for loyalty. Some approaches to modeling crime and corruption do not involve an explicit simulation: rule based systems; Bayesian networks; game theoretic approaches, often based on rational choice theory; and Neoclassical Econometrics, a rational choice-based approach. Simulation-based approaches take into account greater complexities of interacting parts of social phenomena. These include fuzzy cognitive maps and fuzzy rule sets that may incorporate feedback; and agent-based simulation, which can go a step farther by computing new social structures not previously identified in theory. The latter include cognitive agent models, in which agents learn how to perceive their en- ronment and act upon the perceptions of their individual experiences; and reactive agent simulation, which, while less capable than cognitive-agent simulation, is adequate for testing a policy’s effects with existing societal structures. For example, NNL is a cognitive agent model based on the REPAST Simphony toolkit.
Crime science is precisely what it says it is: the application of science to the phenomenon of crime. This handbook, intended as a crime science manifesto, showcases the scope of the crime science field and provides the reader with an understanding of the assumptions, aspirations and methods of crime science, as well as the variety of topics that fall within its purview. Crime science provides a distinctive approach to understanding and dealing with crime: one that is outcome-oriented, evidence-based and that crosses boundaries between disciplines. The central mission of crime science is to find new ways to cut crime and increase security. Beginning by setting out the case for crime science, the editors examine the roots of crime science in environmental criminology and describe its key features. The book is then divided into two sections. The first section comprises chapters by disciplinary specialists about the contributions their sciences can make or have already made to crime science. Chapter 12 of this book is freely available as a downloadable Open Access PDF under a Creative Commons Attribution-Non Commercial-No Derivatives 3.0 license. https://s3-us-west-2.amazonaws.com/tandfbis/rt-files/docs/Open+Access+Chapters/9780415826266_oachapter12.pdf
The future policing ought to cover identification of new assaults, disclosure of new ill-disposed patterns, and forecast of any future vindictive patterns from accessible authentic information. Such keen information will bring about building clever advanced proof handling frameworks that will help cops investigate violations. Artificial Intelligence for Cyber Defense and Smart Policing will describe the best way of practicing artificial intelligence for cyber defense and smart policing. Salient Features: • Combines AI for both cyber defense and smart policing in one place. • Covers novel strategies in future to help cybercrime examinations and police. • Discusses different AI models to fabricate more exact techniques. • Elaborates on problematization and international issues. • Includes case studies and real-life examples. This book is primarily aimed at graduates, researchers, and IT professionals. Business executives will also find this book helpful.
Agent-Based Modelling for Criminological Theory Testing and Development addresses the question whether and how we can use simulation methods in order to test criminological theories, and if they fail to be corroborated, how we can use simulation to mend and further develop theories. It is by no means immediately obvious how results being observed in an artificial environment have any relevance for what is going on in the real world. By using the concept of a "stylized fact," the contributors bridge the gap between artificial and real world. With backgrounds in criminology or artificial intelligence (AI), these contributors present agent-based model studies that test aspects of various theories, including crime pattern theory, guardianship in action theory, near repeat theory, routine activity theory, and general deterrence theory. All six simulation models presented have been specially developed for the book. Contributors have specified the theory, identified stylized facts, developed an agent-based simulation model, let it run, and interpreted whether the chosen stylized fact is occurring in their model, and what we should conclude from congruence or incongruence between simulation and expectations based on the theory under scrutiny. The final chapter discusses what can be learnt from these six enterprises. The book will be of great interest to scholars of criminology (in particular computational criminologists and theoretical criminologists) and AI (with an emphasis on AI for generative social processes), and more widely researchers in social science in general. It will also be valuable for master's courses in quantitative criminology.
In recent years, the idea of emergence, which suggests that observed patterns in behavior and events are not fully reductive and stem from complex lower-level interactions, has begun to take hold in the social sciences. Criminologists have started to use this framework to improve our general understanding of the etiology of crime and criminal behavior. When Crime Appears: The Role of Emergence is concerned with our ability to make sense of the complex underpinnings of the end-stage patterns and events that we see in studying crime and offers an early narrative on the concept of emergence as it pertains to criminological research. Collectively, the chapters in this volume provide a sense of why the emergence framework could be useful, outlines its core conceptual properties, provides some examples of its potential application, and presents some discussion of methodological and analytic issues related to its adoption.
This updated and expanded new edition continues its unique approach and engrossing exploration of the elements of residential burglary. Presented in five parts, the first is concerned with what is on a burglar’s mind when he or she considers whether to commit a burglary and which house to choose. The second part is concerned with time and the opportunities and limits it places on both burglar and victim, while the third section probes how burglaries are fit into space and the importance of perception of space in the burglary process. The fourth section describes how burglars select a home to burglarize and uses Greenwich, Connecticut as a model to contrast target and nontarget homes. The fifth part reviews some of the “nuts and bolts” techniques and reasons for their use as described by burglars and addresses elements about housing architecture, the burglary process, and offers suggestions for controlling the problem of burglary. It concludes with a discussion of changes in our lifestyles and communities and how these changes will play out in future patterns of residential burglary. The authors draw on in-depth interviews with admitted burglars, and the inclusion of the ideas and actual words of the burglars brings the material to life. The text continues to offer the most unique overview of residential burglary. It combines ethnographic research with study of official records and combines the strengths of both approaches.
This unique book brings together a comprehensive set of papers on the background, theory, technical issues and applications of agent-based modelling (ABM) within geographical systems. This collection of papers is an invaluable reference point for the experienced agent-based modeller as well those new to the area. Specific geographical issues such as handling scale and space are dealt with as well as practical advice from leading experts about designing and creating ABMs, handling complexity, visualising and validating model outputs. With contributions from many of the world’s leading research institutions, the latest applied research (micro and macro applications) from around the globe exemplify what can be achieved in geographical context. This book is relevant to researchers, postgraduate and advanced undergraduate students, and professionals in the areas of quantitative geography, spatial analysis, spatial modelling, social simulation modelling and geographical information sciences.