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 language||English (US)|
|Number of pages||6|
|Journal||Proceedings - IEEE International Conference on Data Mining, ICDM|
|State||Published - Jan 26 2015|
|Event||14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China|
Duration: Dec 14 2014 → Dec 17 2014