Shekhar, ShashiLu, Chang-tienZhang, Pusheng2020-09-022020-09-022001-12-10https://hdl.handle.net/11299/215495Spatial outliers represent locations which are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Identification of spatialoutliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. In this paper, we first provide a general definition of $S$-outliers for spatial outliers. This definition subsumes the traditional definitions of spatial outliers. Second, we characterize the computation structure of spatial outlier detection methods andpresent 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) traffic data set.en-USReport