Discovering Spatial Regions of High Correlation
Prerna Agarwal, Richa Verma, Venkata M. V. Gunturi · 2016
Given a set of events of two different types (e.g. locations of crime incidents/road accidents) in geographic space and minimum density and area thresholds, spatial regions of high correlation discovery (RHC) aims to determine rectangular-shaped areas of high correlation between two event types. RHC discovery is important to many fields like transportation engineering, criminology, and epidemiology. Designing a scalable algorithm for RHC discovery is challenging mainly because of non-monotonicity of popular spatial statistical interest measures of association between events, one such measure being the cross-K function. This challenge makes Apriori-based pruning algorithms inapplicable. The large enumeration space is another challenge. To address these limitations, we propose a cross-K inspired interest measure and using that, a novel algorithm for RHC discovery. Real crime data is used for a case study to present the output of our algorithm. Experimental evaluation is done to show that the proposed algorithm cuts down on computation substantially as compared to the naive approach.