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.
Crime Analysis. White @cite_8 represent criminal activities via a georeferenced complex network, through which they compare criminal demographics and identify criminal communities. The demographic regions are created by clustering the urban areas using socioeconomic borders, whereas the crime network is formed by linking crime locations that are close according to the Euclidean distance. To identify the criminal communities, the authors employ a label propagation technique. Their results show that the crime network can be constructed without requiring information about individual crimes. However, their crime network does not take the urban topology into account, so their comparison is based only on socioeconomic information.
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It is undoubtedly cliche to say that we are in the Age of Big Data Analytics or Data Science; every computing and IT publication you find talks about Big Data and companies no longer are interested in software engineers and analysts but instead they are looking for Data Scientists! In spite of the excessive use of the term, the truth of the matter is that data has never been more available and the increase in computation power allows for more sophisticated tools to identify patterns in the data and on the networks that governs these systems (complex networks). Crime is not different, the open data phenomena has spread to thousand of cities in the world, which are making data about crime activity available for any citizen to look at. Furthermore, new criminology studies argue that criminals typically commit crimes in areas in which they are familiar, usually close to home. Using this information we propose a new model based on networks to build links between crimes in close physical proximity. We show that the structure of the criminal activity can be partially represented by this spatial network of sites. In this paper we describe this process and the analysis of the networks we have constructed to find patterns in the underlying structure of criminal activity. The aim of this paper is to investigate whether weak ties play an important role in explaining criminal activities. We first develop a model where individuals learn about crime opportunities by interacting with other peers. These interactions can take the form of either strong or weak ties. We find that increasing the percentage of weak ties induces more transitions from non-crime to crime and thus the crime rate in the economy increases. This is because, when the percentage of weak ties is high, delinquents and non-delinquents are in close contact with each other. We then test these predictions using the U.S. National Longitudinal Survey of Adolescent Health (AddHealth), which contains unique detailed informations on friendship relationships among teenagers. The theoretical predictions of our model are confirmed by the empirical analysis since we find that weak ties, as measured by friends of friends, have a positive impact on criminal activities. Prior work on criminal incident prediction has relied primarily on the historical crime record and various geospatial and demographic information sources. Although promising, these models do not take into account the rich and rapidly expanding social media context that surrounds incidents of interest. This paper presents a preliminary investigation of Twitter-based criminal incident prediction. Our approach is based on the automatic semantic analysis and understanding of natural language Twitter posts, combined with dimensionality reduction via latent Dirichlet allocation and prediction via linear modeling. We tested our model on the task of predicting future hit-and-run crimes. Evaluation results indicate that the model comfortably outperforms a baseline model that predicts hit-and-run incidents uniformly across all days. 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... 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 The high degree of variance of crime rates across space (and across time) is one of the oldest puzzles in the social sciences (see Quetelet (1835)). Our empirical work strongly suggests that this variance is not the result of observed or unobserved geographic attributes. This paper presents a model where social interactions create enough covariance across individuals to explain the high cross- city variance of crime rates. This model provides a natural index of social interactions which can compare the degree of social interaction across crimes, across geographic 1units and across time. Our index gives similar results for different data samples and suggests that the amount of social interactions are highest in petty crimes (such as larceny and auto theft), moderate in more serious crimes (assault, burglary and robbery) and almost negligible in murder and rape. The index of social interactions is also applied to non-criminal choices and we find that there is substantial interaction in schooling choice. Since the incident about 9.11, the Security Sectors of many countries have put great attentions on gathering and mining of crime data and establishing anti-terrorist databases. With the emergence of anti-terrorist application, data mining for anti-terrorist has attracted great attention from both researchers and officers as well as in China. The purpose of analyzing and mining related terrorist or crimes data is that analyzing of psychology, behavior and related laws about crime, and providing hidden clues to prevent a criminal case, and forecasting terror happening to keeping with crime limits. Abstract This short paper introduces the six papers comprising the Special Section on Crime Forecasting. A longer title for the section could have been “Forecasting crime for policy and planning decisions and in support of tactical deployment of police resources.” Crime forecasting for police is relatively new. It has been made relevant by recent criminological theories, made possible by recent information technologies including geographic information systems (GIS), and made desirable because of innovative crime management practices. While focused primarily on the police component of the criminal justice system, the six papers provide a wide range of forecasting settings and models including UK and US jurisdictions, long- and short-term horizons, univariate and multivariate methods, and fixed boundary versus ad hoc spatial cluster areal units for the space and time series data. Furthermore, the papers include several innovations for forecast models, with many driven by unique features of the problem area and data. This chapter documents what we believe to be the first systematic study of the costs of cybercrime. The initial workshop paper was prepared in response to a request from the UK Ministry of Defence following scepticism that previous studies had hyped the problem. For each of the main categories of cybercrime we set out what is and is not known of the direct costs, indirect costs and defence costs – both to the UK and to the world as a whole. We distinguish carefully between traditional crimes that are now “cyber” because they are conducted online (such as tax and welfare fraud); transitional crimes whose modus operandi has changed substantially as a result of the move online (such as credit card fraud); new crimes that owe their existence to the Internet; and what we might call platform crimes such as the provision of botnets which facilitate other crimes rather than being used to extract money from victims directly. As far as direct costs are concerned, we find that traditional offences such as tax and welfare fraud cost the typical citizen in the low hundreds of pounds euros dollars a year; transitional frauds cost a few pounds euros dollars; while the new computer crimes cost in the tens of pence cents. However, the indirect costs and defence costs are much higher for transitional and new crimes. For the former they may be roughly comparable to what the criminals earn, while for the latter they may be an order of magnitude more. As a striking example, the botnet behind a third of the spam sent in 2010 earned its owners around $2.7 million, while worldwide expenditures on spam prevention probably exceeded a billion dollars. We are extremely inefficient at fighting cybercrime; or to put it another way, cyber-crooks are like terrorists or met al thieves in that their activities impose disproportionate costs on society. Some of the reasons for this are well-known: cybercrimes are global and have strong externalities, while traditional crimes such as burglary and car theft are local, and the associated equilibria have emerged after many years of optimisation. As for the more direct question of what should be done, our figures suggest that we should spend less in anticipation of cybercrime (on antivirus, firewalls, etc.) and more in response – that is, on the prosaic business of hunting down cyber-criminals and throwing them in jail. A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene. 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. A central and recurring concern in the sociology of criminal law is racial disparity in imprisonment-blacks are much more likely than whites to be imprisoned for crimes. Sociological theories disagree over the sources of imprisonment disparity. Prior research has ignored the social, economic, and legal characteristics of states and regions of the country that may contribute to disparity. The present study finds differences in the social standing of blacks relative to whites that explain substantial and statistically significant variation in racial disparity in imprisonment across states after racial differences in involvement in crime are controlled. Blacks are more likely than whites to be imprisoned in states where the black population is a small percentage of the total population and predominantly urban. The implications of these findings for a structural theory of punishment are discussed. Over the past decade, racial and ethnic disparities in imprisonment have provoked national concern. While blacks and other racial minorities constitute a relatively small share of the general population, they make up a very large share of federal and state prison populations. In 1982, blacks made up 12 percent of the U.S. population and 48 percent of the prison population.' Such disparity continues to fuel debate about the treatment of minorities in the administration of criminal justice (Blumstein 1982; Hagan 1974a; Kieck 1981; Langan 1985). One writer described the debate as "one of the most distressing and troublesome aspects of the operation of the criminal justice system in the United States" (Blumstein 1982, p. 1259). *We thank Paul Burstein, Herbert Costner, Avery Guest, James Inverarity, Joe Weis, L. Wesley Wager, and two anonymous referees for their comments and suggestions. The project reported in this paper was sponsored by a grant from the Legislature of the State of Washington to the Institute for Public Policy, Graduate School of Public Affairs, University of Washington. Address correspondence to George S. Bridges, Department of Sociology-DK40, University of Washington, Seattle, WA 98195. ? 1988 The University of North Carolina Press Advancements in mobile technology and computing have fostered the collection of a large number of civic datasets that capture the pulse of urban life. Furthermore, the open government and data initiative has led many local authorities to make these datasets publicly available, hoping to drive innovation that will further improve the quality of life for the city-dwellers. In this paper, we develop a novel application that utilizes crime data to provide safe urban navigation. Specifically, using crime data from Chicago and Philadelphia we develop a risk model for their street urban network, which allows us to estimate the relative probability of a crime on any road segment. Given such model we define two variants of the SafePaths problem where the goal is to find a short and low-risk path between a source and a destination location. Since both the length and the risk of the path are equally important but cannot be combined into a single objective, we approach the urban-navigation problem as a biobjective shortest path problem. Our algorithms aim to output a small set of paths that provide tradeoffs between distance and safety. Our experiments demonstrate the efficacy of our algorithms and their practical applicability. Crimeware-as-a-service (CaaS) has become a prominent component of the underground economy. CaaS provides a new dimension to cyber crime by making it more organized, automated, and accessible to criminals with limited technical skills. This paper dissects CaaS and explains the essence of the underground economy that has grown around it. The paper also describes the various crimeware services that are provided in the underground market. The rise of cybercrime in the last decade is an economic case of individuals responding to monetary and psychological incentives. Two main drivers for cybercrime can be identi fied: the potential gains from cyberattacks are increasing with the growth of importance of the Internet, and malefactors' expected costs (e.g., the penalties and the likelihood of being apprehended and prosecuted) are frequently lower compared with traditional crimes. In short, computer-mediated crimes are more convenient, and pro table, and less expensive and risky than crimes not mediated by the Internet. The increase in cybercriminal activities, coupled with ineff ective legislation and ineffective law enforcement pose critical challenges for maintaining the trust and security of ourcomputer infrastructures.Modern computer attacks encompass a broad spectrum of economic activity, where various malfeasants specialize in developing speci c goods (exploits, botnets, mailers) and services (distributing malware, monetizing stolen credentials, providing web hosting, etc.). A typical Internet fraud involves the actions of many of these individuals, such as malware writers, botnet herders, spammers, data brokers, and money launderers.Assessing the relationships among various malfeasants is an essential piece of information for discussing economic, technical, and legal proposals to address cybercrime. This paper presents a framework for understanding the interactions between these individuals and how they operate. We follow three steps.First, we present the general architecture of common computer attacks, and discuss the flow of goods and services that supports the underground economy. We discuss the general flow of resources between criminal groups and victims, and the interactions between diff erent specialized cybercriminals.Second, we describe the need to estimate the social costs of cybercrime and the profi ts of cybercriminals in order to identify optimal levels of protection. One of the main problems in quantifying the precise impact of cybercrime is that computer attacks are not always detected, or reported. Therefore we propose the need to develop a more systematic and transparent way of reporting computer breaches and their eff ects.Finally, we propose some possible countermeasures against criminal activities. In particular, we analyze the role private and public protection, and the incentives of multiple stake holders. The proliferation of mobile phones in society has led to a concomitant increase in their use in and connected to criminal activity. The examination and analysis of all telecommunications equipment has become an important aid to law enforcement in the investigation of crime. An understanding of the mechanism of the mobile phone network is vital to appreciate the worth of data retrieved during such an examination. This paper describes in principle the way a cellular mobile phone network operates and how the data is processed. In addition it discusses some of the tools available to examine mobile phones and SIM cards and some of their strengths and weaknesses. It also presents a short overview of the legal position of an analyst when examining a mobile phone. Crime is classically âunpredictableâ. It is not necessarily random, but neither does it take place consistently in space or time. A better theoretical understanding is needed to facilitate practical crime prevention solutions that correspond to specific places and times. In this study, we discuss the preliminary results of a crime forecasting model developed in collaboration with the police department of a United States city in the Northeast. We first discuss our approach to architecting datasets from original crime records. The datasets contain aggregated counts of crime and crime-related events categorized by the police department. The location and time of these events is embedded in the data. Additional spatial and temporal features are harvested from the raw data set. Second, an ensemble of data mining classification techniques is employed to perform the crime forecasting. We analyze a variety of classification methods to determine which is best for predicting crime âhotspotsâ. We also investigate classification on increase or emergence. Last, we propose the best forecasting approach to achieve the most stable outcomes. The result of our research is a model that takes advantage of implicit and explicit spatial and temporal data to make reliable crime predictions. 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 Based on a groundbreaking theory of crime prevention, this practical and empowering book shows how citizens, business owners, and police can work together to ensure the safety of their communities. George Kelling, one of America s leading criminologists, has proven the success of his method across the country, from the New York City subways to the public parks of Seattle. Here, Kelling and urban anthropologist and lawyer Catherine Coles demonstrate that by controlling disorderly behavior in public spaces, we can create an environment where serious crime cannot flourish, and they explain how to adapt these effective methods for use in our own homes and communities."