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.
@cite_8
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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. 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... 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. 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.