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.
Other works include the proposals of Galbrun @cite_3 , who focus on crime mapping to estimate the probability of a crime occurrence within a street segment; of Fitterer @cite_4 , who use a statistical model to predict crimes in the city of Vancouver; and of Bogomolov @cite_9 , who predict in London by using mobile data. Our work differs from theirs because (i) we consider crimes within a more complete and up-to-date period; (ii) we map crimes directly, there is no space summarization in the crime mapping phase; and (iii) we benefit from complex networks, which are a reliable toolset to design georeferenced data.
@cite_9 @cite_4 @cite_3
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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. Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitterdriven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction. We use Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States. We then incorporate these topics into a crime prediction model and show that, for 19 of the 25 crime types we studied, the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation. We identify a number of performance bottlenecks that could impact the use of Twitter in an actual decision support system. We also point out important areas of future work for this research, including deeper semantic analysis of message con 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 Ex