Predictive Models for Public Safety Using Social Network Analysis
Predictive Models for Public Safety Using Social Network Analysis

Author: Mohammad Ali Tayebi

Publisher:

Published: 2015

Total Pages: 133

ISBN-13:

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Crime reduction and prevention is the major concern of law enforcement agencies in order to increase public safety, reduce the crime costs to society and protect the personal integrity and property of citizens. Along with big data analytics, predictive policing which is a new paradigm for crime analysis has been emerging. An important task in predictive policing is analyzing the relationships between offenders to fully understand the criminal collaboration patterns. Law enforcement agencies have long realized the importance of analyzing co-offending networks, networks of offenders who have committed crimes together, for designing 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. In this research, we study co-offending network analysis as effective tool assisting predictive policing. We start with a formal representation of crime data and co-offending networks to bridge the conceptual gap between abstract crime data level and co-offending network mining. To gain a better understanding of co-offending networks we thoroughly study their structural properties. Specifically, how centrality measures can be employed to identify key players of co-offending networks to disrupt these networks. Then, we propose an algorithmic solution for detecting organized crime groups from a social network analysis perspective. We explore predicting criminal collaborations from two angles. First, given partial information about offenders involved in a crime incident, our proposed approach assists in investigating the most probable suspects in that incident. Second, we propose a supervised learning framework for co-offence prediction. Finally, we propose a random walk based method for crime location prediction which is personalized for every offender by using spatial information about offenders and the co-offending networks structure. The efficacy of the proposed methods are experimentally evaluated using a large crime dataset representing five years of police arrest-data for the regions of the Province of British Columbia which are policed by the RCMP, and compared to other existing methods.

Social Network Analysis in Predictive Policing
Social Network Analysis in Predictive Policing

Author: Mohammad A. Tayebi

Publisher: Springer

Published: 2016-10-11

Total Pages: 141

ISBN-13: 3319414925

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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.

Predictive Policing and Artificial Intelligence
Predictive Policing and Artificial Intelligence

Author: John McDaniel

Publisher: Routledge

Published: 2021-02-25

Total Pages: 452

ISBN-13: 0429560389

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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.

Cultural Techniques
Cultural Techniques

Author: Bernhard Siegert

Publisher: Fordham Univ Press

Published: 2015-05-01

Total Pages: 286

ISBN-13: 0823263770

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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.

Predictive Policing
Predictive Policing

Author: Walt L. Perry

Publisher: Rand Corporation

Published: 2013-09-23

Total Pages: 187

ISBN-13: 0833081551

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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.

Trends in Social Network Analysis
Trends in Social Network Analysis

Author: Rokia Missaoui

Publisher: Springer

Published: 2017-04-29

Total Pages: 263

ISBN-13: 3319534203

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The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field.

The Rise of Big Data Policing
The Rise of Big Data Policing

Author: Andrew Guthrie Ferguson

Publisher: NYU Press

Published: 2019-11-15

Total Pages: 267

ISBN-13: 147986997X

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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.

Social Network Analysis Model for Law Enforcement Identifications of Community Intelligence Contacts
Social Network Analysis Model for Law Enforcement Identifications of Community Intelligence Contacts

Author: Pat Nelson (Ph. D.)

Publisher:

Published: 2013

Total Pages: 154

ISBN-13:

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In order to disseminate and exchange community intelligence with the local community, local law enforcement agencies must understand how to identify the appropriate points of contact within the community based on the community's social structure. Based on the research, no clear model has been used to identify the appropriate points of contact in the community, and this gap has led to distrust and misinformation between local law enforcement and community members. The purpose of this conversion mixed methods study was to understand the extent to which social network analysis can be a feasible model for identifying the points of contact in a Midwestern Somali community for the exchange of community intelligence. The theoretical framework for the study was social capital theory. Data were collected through 6 semi-structured interviews and were converted to binary, directed data for social network analysis. Data were then coded for thematic analysis to provide triangulation in testing the model against the research question. A cohesive, dense network, as well as key players or "points of contact" in the network, were identified. The key players were also identified in the thematic analysis as powerful, connected, and influential, which correlated the theoretical framework and social network analysis in the identification of the social structure of the community. The recommendation is to test the model in other communities to determine the feasibility of application of the model by local law enforcement. These findings have implications for positive social change for law enforcement, who may capitalize on the utility of social network analysis to identify appropriate points of contact, to collaborate on concerns, and to build a stronger trust relationship with their community.

Intelligent Data Analytics for Terror Threat Prediction
Intelligent Data Analytics for Terror Threat Prediction

Author: Subhendu Kumar Pani

Publisher: John Wiley & Sons

Published: 2020-12-31

Total Pages: 352

ISBN-13: 1119711614

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Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. This book provides innovative insights that will help obtain interventions to undertake emerging dynamic scenarios of criminal activities. Furthermore, it presents emerging issues, challenges and management strategies in public safety and crime control development across various domains. The book will play a vital role in improvising human life to a great extent. Researchers and practitioners working in the fields of data mining, machine learning and artificial intelligence will greatly benefit from this book, which will be a good addition to the state-of-the-art approaches collected for intelligent data analytics. It will also be very beneficial for those who are new to the field and need to quickly become acquainted with the best performing methods. With this book they will be able to compare different approaches and carry forward their research in the most important areas of this field, which has a direct impact on the betterment of human life by maintaining the security of our society. No other book is currently on the market which provides such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is a newly emerging field and research in data mining and machine learning is still in the early stage of development.