Learning to Detect Patterns of Crime

Wang, Tong; Rudin, Cynthia; Wagner, Daniel S.; Sevieri, Rich

doi:10.1007/978-3-642-40994-3_33

Cited by 87 publications

(52 citation statements)

References 16 publications

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“…The people-centric perspective has mostly been used for individual or collective criminal profiling. Wang et al [42] proposed Series Finder, a machine learning approach to the problem of detecting specific patterns in crimes that are committed by the same offender or group of offenders. In [31], it is proposed a biased random walk model built upon empirical knowledge of criminal offenders behavior along with spatio-temporal crime information to take into account repeating patterns in historical crime data.…”

Section: Related Workmentioning

confidence: 99%

Once Upon a Crime

Bogomolov

Lepri

Staiano

et al. 2014

Proceedings of the 16th International Conference on Multimodal Interaction

208

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In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.

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Section: Related Workmentioning

confidence: 99%

Once Upon a Crime

Bogomolov

Lepri

Staiano

et al. 2014

Proceedings of the 16th International Conference on Multimodal Interaction

208

View full text Add to dashboard Cite

show abstract

“…For example, Wang et al [39,40] have recently conducted a series of work on applying machine learning techniques to detect specific patterns of criminal activities committed by the same offender (or group of offenders). Although the people-centric studies reveal interesting patterns of criminal activity, they often do not consider the environmental or context factors that influence criminal activities, which is the main focus of environmental criminology [8].…”

Section: Related Workmentioning

confidence: 99%

CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

et al. 2017

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Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, Published in "World Wide Web doi: 10.1007/s11280-017-0515-4, 2017" which should be cited to refer to this work.we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope.

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“…Wang et al propose a pattern detection algorithm called Series Finder [14]. This algorithm grows a pattern of discovered crimes from within a database by starting from a seed of few crimes.…”

Section: Literature Reviewmentioning

confidence: 99%