Computer Science Technical Reports
CS at VT

GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection

Chen, Feng and Lu, Chang-Tien and Boedihardjo, Arnold P. (2010) GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection. Technical Report TR-10-03, Computer Science, Virginia Tech.

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Local based approach is a major category of methods for spatial outlier detection (SOD). Currently, there is a lack of systematic analysis on the statistical properties of this framework. For example, most methods assume identical and independent normal distributions (i.i.d. normal) for the calculated local differences, but no justifications for this critical assumption have been presented. The methods’ detection performance on geostatistic data with linear or nonlinear trend is also not well studied. In addition, there is a lack of theoretical connections and empirical comparisons between local and global based SOD approaches. This paper discusses all these fundamental issues under the proposed generalized local statistical (GLS) framework. Furthermore, robust estimation and outlier detection methods are designed for the new GLS model. Extensive simulations demonstrated that the SOD method based on the GLS model significantly outperformed all existing approaches when the spatial data exhibits a linear or nonlinear trend.

Item Type:Departmental Technical Report
Keywords:Spatial Outlier Detection, Spatial Gaussian Random Field
Subjects:Computer Science > Numerical Analysis
ID Code:1110
Deposited By:Administrator, Eprints
Deposited On:17 March 2010