Ring-Shaped Hotspot Detection: A Summary of Results

Emre Eftelioglu, Shashi Shekhar, Dev Oliver, Xun Zhou, Michael R. Evans, Yiqun Xie, James M. Kang, Renee Laubscher, Christopher Farah

Research output: Contribution to journalConference articlepeer-review

22 Scopus citations

Abstract

Given a collection of geo-located activities (e.g., Crime reports), ring-shaped hotspot detection (RHD) finds rings, where concentration of activities inside the ring is much higher than outside. RHD is important for the applications such as crime analysis, where it may focus the search for crime source's location, e.g. The home of a serial criminal. RHD is challenging because of the large number of candidate rings and the high computational cost of the statistical significance test. Previous statistically significant hotspot detection techniques (e.g., Sat Scan) identify circular/rectangular areas, but can not discover rings. This paper proposes a dual grid based pruning (DGP) approach to detect ring-shaped hotspots. A case study on real crime data confirms that DGP detects novel ring-shaped regions, regions that go undetected by Sat Scan. Experiments show that DGP improves the computational cost of a naive approach substantially.

Original languageEnglish (US)
Article number7023406
Pages (from-to)815-820
Number of pages6
JournalProceedings - IEEE International Conference on Data Mining, ICDM
Volume2015-January
Issue numberJanuary
DOIs
StatePublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014

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