A Uni ed Approach to Spatial Outliers Detection

Spatial outliers represent locations which are signiicantly diierent from their neighborhoods even though they may not be signiicantly diierent from the entire population. Identiication of spatial outliers can lead to the discovery of unexpected , interesting, and implicit knowledge, such as local instability. In this paper, we rst provide a general deenition of S-outliers for spatial outliers. This deeni-tion subsumes the traditional deenitions of spatial outliers. Second, we characterize the computation structure of spatial outlier detection methods and present scalable algorithms. Third, we provide a cost model of the proposed algorithms. Finally, we provide experimental evaluations of our algorithms using a Minneapolis-St. Paul(Twin Cities) traac data set.