TY - JOUR
T1 - Ring-Shaped Hotspot Detection
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
AU - Eftelioglu, Emre
AU - Shekhar, Shashi
AU - Oliver, Dev
AU - Zhou, Xun
AU - Evans, Michael R.
AU - Xie, Yiqun
AU - Kang, James M.
AU - Laubscher, Renee
AU - Farah, Christopher
PY - 2015/1/26
Y1 - 2015/1/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84936952770&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936952770&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2014.13
DO - 10.1109/ICDM.2014.13
M3 - Conference article
AN - SCOPUS:84936952770
SN - 1550-4786
VL - 2015-January
SP - 815
EP - 820
JO - Proceedings - IEEE International Conference on Data Mining, ICDM
JF - Proceedings - IEEE International Conference on Data Mining, ICDM
IS - January
M1 - 7023406
Y2 - 14 December 2014 through 17 December 2014
ER -