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.
Rey @cite_10 employ spatiotemporal techniques to represent and analyze urban crimes. Their approach provides a way to quantify neighborhood criminality and is able to identify patterns from the data. Moreover, they discuss spatial crime analyses as a way to determine the influence of a crime on its surroundings, analyzing an entire city and providing an evaluation of its crimes. However, their work is based on outdated data, derived from a police district in the period from 2005 to 2009. Besides, their methods rely on black-box GIS technologies, used to summarize criminal areas by geocoding each crime into a quarter-mile grid cell. Furthermore, they analyze only city cells having residential units.
@cite_10
2017290774
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. 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. 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. Our goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ones may have been committed by the same individual(s). If automated, data-driven tools for crime pattern detection are made available to assist analysts, these tools could help police to better understand patterns of crime, leading to more precise attribution of past crimes, and the apprehension of suspects. To do this, we propose a pattern detection algorithm called Series Finder, that grows a pattern of discovered crimes from within a database, starting from a "seed" of a few crimes. Series Finder incorporates both the common characteristics of all patterns and the unique aspects of each specific pattern, and has had promising results on a decade's worth of crime pattern data collected by the Crime Analysis Unit of the Cambridge Police Department. 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... 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. 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 CrimeStat is a spatial statistics program used in crime mapping. The program inputs incident or point locations and outputs statistics that can be displayed graphically in a geographic information systems (GIS) program. Among the routines are those for summary spatial description, hot spot analysis, interpolation, space–time analysis, and journey-to-crime modeling. Version 3.0 has a crime travel demand module for analyzing travel patterns over a metropolitan area. The program and documentation are distributed by the National Institute of Justice. 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. Abstract The wealth of information provided by real-time streams of data has paved the way for life-changing technological advancements, improving the quality of life of people in many ways, from facilitating knowledge exchange to self-understanding and self-monitoring. Moreover, the analysis of anonymized and aggregated large-scale human behavioral data offers new possibilities to understand global patterns of human behavior and helps decision makers tackle problems of societ al importance. In this article, we highlight the potential societ al benefits derived from big data applications with a focus on citizen safety and crime prevention. First, we introduce the emergent new research area of big data for social good. Next, we detail a case study tackling the problem of crime hotspot classification, that is, the classification of which areas in a city are more likely to witness crimes based on past data. In the proposed approach we use demographic information along with human mobility characteristics as der... 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. 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." 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. Recent studies have examined racial disparities in stop-and-frisk, a widely employed but controversial policing tactic. The statistical evidence, however, has been limited and contradictory. We investigate by analyzing three million stops in New York City over five years, focusing on cases where officers suspected the stopped individual of criminal possession of a weapon (CPW). For each CPW stop, we estimate the ex ante probability that the detained suspect has a weapon. We find that in more than 40 of cases, the likelihood of finding a weapon (typically a knife) was less than 1 , raising concerns that the legal requirement of “reasonable suspicion” was often not met. We further find that blacks and Hispanics were disproportionately stopped in these low hit rate contexts, a phenomenon that we trace to two factors: (1) lower thresholds for stopping individuals — regardless of race — in high-crime, predominately minority areas, particularly public housing; and (2) lower thresholds for stopping minorities relative to similarly situated whites. Finally, we demonstrate that by conducting only the 6 of stops that are statistically most likely to result in weapons seizure, one can both recover the majority of weapons and mitigate racial disparities in who is stopped. We show that this statistically informed stopping strategy can be approximated by simple, easily implemented heuristics with little loss in efficiency. 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. 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. 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. 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.