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 84 publications

(52 citation statements)

References 16 publications

(22 reference statements)

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“…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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Section: Related Workmentioning

confidence: 99%

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

et al. 2017

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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%

KNN Classifier and Naive Bayse Classifier for Crime Prediction in San Francisco Context

Abdulrahman¹,

Abedalkhader²

2017

IJDMS

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“…Crime pattern detection based on prisoner's data using unsupervised techniques and k means algorithm is presented by Nath (2006). Interesting analysis on finding similar modus operandi in crime and detecting crime pattern is discussed by Wang et al (2013). Crime pattern is defined with crime specific parameters and modus operandi parameters and a supervised learning approach to detect and crime pattern is suggested in the paper.…”

Section: Literature Surveymentioning

confidence: 99%