Complex networks are nowadays employed in several applications. Modeling urban street networks is one of them, and in particular to analyze criminal aspects of a city. Several research groups have focused on such application, but until now, there is a lack of a well-defined methodology for employing complex networks in a whole crime analysis process, i.e. from data preparation to a deep analysis of criminal communities. Furthermore, the “toolset” available for those works is not complete enough, also lacking techniques to maintain up-to-date, complete crime datasets and proper assessment measures. In this sense, we propose a threefold methodology for employing complex networks in the detection of highly criminal areas within a city. Our methodology comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of assessment measures for analyzing intrinsic criminality of communities, especially when considering different crime types. We show our methodology by applying it to a real crime dataset from the city of San Francisco—CA, USA. The results confirm its effectiveness to identify and analyze high criminality areas within a city. Hence, our contributions provide a basis for further developments on complex networks applied to crime analysis.
Shinode and Shinode @cite_13 aim at introducing a search-window method to analyze urban crimes in street networks. They represent a search window as a subarea that concentrates a high number of crimes in small regions; besides that, the crimes are used to identify clusters spatially and temporally. The authors validate their methods through an empirical case study. However, their criminal data derive from 911-calls made in 1996; that is, they used a dataset that carries few details and that is outdated, especially when one considers the conclusions claimed by the authors. Such information implies a superficial analysis of the crime behavior and the ways to prevent it.
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This study proposes a street-level space‐time hotspot detection method to analyse crime incidents recorded at the street-address level and provides description of the micro-level variation of crime incidents over space and time. It expands the notion of search-window techniques widely used in crime science by developing a method that can account for the spatial‐temporal distribution of crime incidents measured in network distance. The study first describes the methodological framework by presenting the concept of a new type of search window and how it is used in the process of statistical testing for detecting crime hotspots. This is followed by analyses using 1 a simulated distribution of points along the street network, and 2 a set of real street-crime incident data. The simulation study demonstrates that the proposed method is effective in identifying space‐time hotspots, which include those that are not detected by a non-temporal method. The empirical analysis of the drug markets and assaults in downtown Buffalo, New York, revealed a detailed space‐time signature of each type of crime, highlighting the recurrent nature of drug dealing at specific locations as well as the sporadic tendency of assault incidents. Abstract Street profile analysis is a new method for analyzing temporal and spatial crime patterns along major roadways in metropolitan areas. This crime mapping technique allows for the identification of crime patterns along these street segments. These are linear spaces where aggregate crime patterns merge with crime attractors generators and human movement to demonstrate how directionality is embedded in city infrastructures. Visually presenting the interplay between these criminological concepts and land use can improve police crime management strategies. This research presents how this crime mapping technique can be applied to a major roadway in Burnaby, Canada. This technique is contrasted with other crime mapping methods to demonstrate the utility of this approach when analyzing the rate and velocity of crime patterns overtime and in space. Preface to the Fifth Edition Acknowledgments About the Authors Chapter 1: Eight Fallacies about Crime The Dramatic Fallacy The Cops and Courts Fallacy The Not-Me Fallacy The Innocent- Youth Fallacy The Ingenuity Fallacy The Organized Crime Fallacy The Big Gang Fallacy The Agenda Fallacy Chapter 2: The Chemistry for Crime Risky Settings Stages of a Criminal Act First Three Elements of a Criminal Act Eck's Crime Triangle Predatory Crimes Calming the Waters and Looking after Places Hot Products The General Chemistry of Crime Chapter 3: Offenders Make Decisions The Decision to Commit a Crime Offenders Respond to Everyday Cues Odd and Bizarre Crimes Chapter 4: How Violence Erupts Making Students Angry in a Lab In Real Life Fights vs. Predatory Attacks Violent Decisions It Might Not Work Out Well Chapter 5: Bringing Crime to You Four Stages in the History of Everyday Life Life and Crime in the Convergent City Crime and the Divergent Metropolis Population Density, Shifts, and Patterns of Crime The Great Paradox: Central City Problems Versus Self-Report Evidence Chapter 6: Teenage Crime Perspective on Youth Crime Teenage Zigzags Chapter 7: Big Gang Theory What are Gang Crimes? Fundamentals The Puzzle The Reason for a Gang Chapter 8: Crime Multipliers One Crime Requires Another One Crime Disinhibits Another One Crime Advertises Another One Crime Entices Another One Crime Sets Up Another One Crime Escalates Into Another One Crime Starts A Victim Chain One Co-Offender Attacks Another Chapter 9: Moving Stolen Goods The Thief and the Public Inviting People to Steal More It's Easier to Sell Stolen Goods to the Poor New Avenues for Stolen Goods Chapter 10: Situational Crime Prevention Four Natural Experiments Situational Prevention and Crime Analysis Diverse Applications of Situational Crime Prevention Chapter 11: Local Design Against Crime Remarkable Research on Local Supervision Oscar Newman's Four Types of Space Versions of a Village The Offender-Target Convergence Process Seven Studies in Reducing Local Crime Chapter 12: "White-Collar" Crime How Offenders Get to Their Targets Crimes of Specialized Access Organizational Rank The Specialized-Access Crime Grid Chapter 13: Crime and Social Change Technology, Rightly Understood The Clean Water Example A General Approach: Summing Up This Book How to Think about Changing Crime References Index Motivated by empirical observations of spatio-temporal clusters of crime across a wide variety of urban settings, we present a model to study the emergence, dynamics, and steady-state properties of crime hotspots. We focus on a two-dimensional lattice model for residential burglary, where each site is characterized by a dynamic attractiveness variable, and where each criminal is represented as a random walker. The dynamics of criminals and of the attractiveness field are coupled to each other via specific biasing and feedback mechanisms. Depending on parameter choices, we observe and describe several regimes of aggregation, including hotspots of high criminal activity. On the basis of the discrete system, we also derive a continuum model; the two are in good quantitative agreement for large system sizes. By means of a linear stability analysis we are able to determine the parameter values that will lead to the creation of stable hotspots. We discuss our model and results in the context of established crim...