Emre Eftelioglu
ABSTRACT
Given a set of points in two dimensional space, statistically significant hotspot detection aims to detect locations where the concentration of points inside the hotspot is much higher than outside. Statistically significant hotspot detection is an important task in application domains such as epidemiology, ecology, criminology, etc. where it may reveal interesting information for domain experts. However, significant hotspot detection is challenging because of a lack of a generic technique for different hotspot patterns (i.e. shapes) and thus a large number of candidate hotspots to be enumerated and tested. Previous hotspot detection techniques focus on specific shapes (e.g. circles, rectangles, ellipses) to identify hotspot areas, but they cannot be used interchangeably which cause a vast variety of complicated and sometimes confusing techniques for each individual hotspot pattern. For example, a circular hotspot detection technique can not be used to discover a rectangular or an elliptical hotspot. In this paper, we propose a generic dual grid based pruning approach for hotspot detection that can be used for different hotspot patterns. We also present a cost analysis, a simplified experiment on the dataset size and a case study on a synthetic dataset to show the applicability of our proposed approach to circular hotspots.
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Digital Library
- E. Eftelioglu, X. Tang, and S. Shekhar. Geographically robust hotspot detection: A summary of results. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pages 1447--1456. IEEE, 2015. Google ScholarDigital Library
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- S. Shekhar et al. Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3):193, 2011.Google ScholarCross Ref
Index Terms
- A generic dual grid pruning approach for significant hotspot detection
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