Crime Hotspot Detection: A Computational Perspective
2016-09-01
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Crime Hotspot Detection: A Computational Perspective
Authors
Published Date
2016-09-01
Publisher
Type
Report
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.
Keywords
Description
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Eftelioglu, Emre; Tang, Xun; Shekhar, Shashi. (2016). Crime Hotspot Detection: A Computational Perspective. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215995.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.