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.
Mapping of Urban Crimes. Spicer @cite_0 describe a theoretical framework capable of mapping urban crimes into related locations in a city. Their work discusses the pros and cons of mapping methods. To represent a city, they use street networks and GIS software, describing methods for mapping georeferenced elements into a complex network. However, their methods associate a crime with the nearest node or edge based on address geocoding, not having any relation with graph measures. Moreover, all methods are superficially introduced, not presenting any formal fundaments.
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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. Abstract Street networks are complex networks that represent the topology and geometry of cities; as so, they can be used to solve problems related to ill-designed urban structures. This application, in real urban scenarios, has been the focus of several types of research, from cities characterization to transportation enhancement. Nevertheless, these works lack a clear and in-depth methodology to characterize the urban space by means of complex networks. Aided by topo-geometrical measures from street networks, we present a methodology to identify what we call urban inconsistencies, which are characterized by low-access regions containing nodes (crossing streets) that lack efficient access from or to other regions in a city. We devised algorithms capable of preprocessing and analyzing street networks, pointing to existing mobility problems in a city. We identify inconsistencies that pertain to a given node where a facility of interest is currently placed; the results introduce ways to assist in the urban planning and design processes. Our techniques are discussed through the analysis of a real-world city. In a real context, the methods provide basis for analyzing and improving the placement of facilities. Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness, straightness and information, are compared over 18 1-square-mile samples of different world cities. Samples are represented by primal geographic graphs, i.e., valued graphs defined by metric rather than topologic distance where intersections are turned into nodes and streets into edges. The spatial behavior of centrality indices over the networks is investigated graphically by means of color-coded maps. The results indicate that a spatial analysis, that we term multiple centrality assessment, grounded not on a single but on a set of different centrality indices, allows an extended comprehension of the city structur... 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. 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. We present a method for automatically identifying and validating predictive relationships between the visual appearance of a city and its non-visual attributes (e.g. crime statistics, housing prices, population density etc.). Given a set of street-level images and (location, city-attribute-value) pairs of measurements, we first identify visual elements in the images that are discriminative of the attribute. We then train a predictor by learning a set of weights over these elements using non-linear Support Vector Regression. To perform these operations efficiently, we implement a scalable distributed processing framework that speeds up the main computational bottleneck (extracting visual elements) by an order of magnitude. This speedup allows us to investigate a variety of city attributes across 6 different American cities. We find that indeed there is a predictive relationship between visual elements and a number of city attributes including violent crime rates, theft rates, housing prices, population density, tree presence, graffiti presence, and the perception of danger. We also test human performance for predicting theft based on street-level images and show that our predictor outperforms this baseline with 33 higher accuracy on average. Finally, we present three prototype applications that use our system to (1) define the visual boundary of city neighborhoods, (2) generate walking directions that avoid or seek out exposure to city attributes, and (3) validate user-specified visual elements for prediction. 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. 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." Modeling a city poses a number of problems to computer graphics. Every urban area has a transportation network that follows population and environmental influences, and often a superimposed pattern plan. The buildings appearances follow historical, aesthetic and statutory rules. To create a virtual city, a roadmap has to be designed and a large number of buildings need to be generated. We propose a system using a procedural approach based on L-systems to model cities. From various image maps given as input, such as land-water boundaries and population density, our system generates a system of highways and streets, divides the land into lots, and creates the appropriate geometry for the buildings on the respective allotments. For the creation of a city street map, L-systems have been extended with methods that allow the consideration of global goals and local constraints and reduce the complexity of the production rules. An L-system that generates geometry and a texturing system based on texture elements and procedural methods compose the buildings. 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. Planners and social psychologists have suggested that the recognizability of the urban environment is linked to people's socio-economic well-being. We build a web game that puts the recognizability of London's streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram's). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where "web science" meets the "smart city" agenda. 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 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. Suppose that a traveler arrives to the City of Toronto, and wants to walk to the famous CN-Tower, one of the tallest free-standing structures in the world. Assume now that our visitor, lacking a map of Toronto, is standing at a crossing from which he can see the CN-tower, and several streets S1, . . . , Sm that he can choose to start his walk. A natural (and most likely safe assumption), is that our visitor must choose to walk first along the road that points closest in the direction of the CN-tower, see Figure 1. A close look at maps of numerous cities around the world, show us that the previous way to explore a new, and unknown city will in general yield walks that will be close enough to the optimal ones to travel from one location to another. In mathematical terms, we can model the map of many cities by geometric graphs in which street intersections are represented by the vertices of our graphs, and streets by straight line segments. Compass routing on geometric networks, in its most elemental form yields the following algorithm: Human observers make a variety of perceptual inferences about pictures of places based on prior knowledge and experience. In this paper we apply computational vision techniques to the task of predicting the perceptual characteristics of places by leveraging recent work on visual features along with a geo-tagged dataset of images associated with crowd-sourced urban perception judgments for wealth, uniqueness, and safety. We perform extensive evaluations of our models, training and testing on images of the same city as well as training and testing on images of different cities to demonstrate generalizability. In addition, we collect a new densely sampled dataset of streetview images for 4 cities and explore joint models to collectively predict perceptual judgments at city scale. Finally, we show that our predictions correlate well with ground truth statistics of wealth and crime. 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 Position-based routing, as it is used by protocols like Greedy Perimeter Stateless Routing (GPSR) [5], is very well suited for highly dynamic environments such as inter-vehicle communication on highways. However, it has been discussed that radio obstacles [4], as they are found in urban areas, have a significant negative impact on the performance of position-based routing. In prior work [6] we presented a position-based approach which alleviates this problem and is able to find robust routes within city environments. It is related to the idea of position-based source routing as proposed in [1] for terminode routing. The algorithm needs global knowledge of the city topology as it is provided by a static street map. Given this information the sender determines the junctions that have to be traversed by the packet using the Dijkstra shortest path algorithm. Forwarding between junctions is then done in a position-based fashion. In this short paper we show how position-based routing can be aplied to a city scenario without assuming that nodes have access to a static street map and without using source routing. Threats of distributed denial of service (DDoS) attacks have been increasing day-by-day due to rapid development of computer networks and associated infrastructure, and millions of software applications, large and small, addressing all varieties of tasks. Botnets pose a major threat to network security as they are widely used for many Internet crimes such as DDoS attacks, identity theft, email spamming, and click fraud. Botnet based DDoS attacks are catastrophic to the victim network as they can exhaust both network bandwidth and resources of the victim machine. This survey presents a comprehensive overview of DDoS attacks, their causes, types with a taxonomy, and technical details of various attack launching tools. A detailed discussion of several botnet architectures, tools developed using botnet architectures, and pros and cons analysis are also included. Furthermore, a list of important issues and research challenges is also reported. Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services. 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.