Crime Hotspot Detection: A Computational Perspective

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Author(s): Emre Eftelioglu (University of Minnesota, USA), Shashi Shekhar (University of Minnesota, USA)and Xun Tang (University of Minnesota, USA)
Copyright: 2020
Pages: 30
Source title: Improving the Safety and Efficiency of Emergency Services: Emerging Tools and Technologies for First Responders
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2535-7.ch010

Keywords: Business Science Reference / Crisis Response and Management / Emergency & Disaster Management / Government & Law

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Abstract

Given a set of crime locations, a statistically significant crime hotspot is an area where the concentration of crimes inside is significantly higher than outside. The motivation of crime hotspot detection is twofold: detecting crime hotspots to focus the deployment of police enforcement and predicting the potential residence of a serial criminal. Crime hotspot detection is computationally challenging due to the difficulty of enumerating all potential hotspot areas, selecting an interest measure to compare these with the overall crime intensity, and testing for statistical significance to reduce chance patterns. This chapter focuses on statistical significant crime hotspots. First, the foundations of spatial scan statistics and its applications (i.e. SaTScan) to circular hotspot detection are reviewed. Next, ring-shaped hotspot detection is introduced. Third, linear hotspot detection is described since most crimes occur along a road network. The chapter concludes with future research directions in crime hotspot detection.

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Crime Hotspot Detection: A Computational Perspective

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