Spatial outliers represent locations which are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Identification of spatial outliers 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 and present scalable algorithms. Third, we provide a cost model of the proposed algorithms. Finally, we experimentally evaluate our algorithms using a Minneapolis-St. Paul (Twin Cities) traffic data set.
Bibliographical noteFunding Information:
We are particularly grateful to Professor Vipin Kumar, and our Spatial Databse Group members, Weili Wu, Yan Huang, Xiaobin Ma, and Hui Xiong for their helpful comments and valuable discussions. We would also like to express our thanks to Kim Koffolt for improving the readability and technical accuracy of this paper. This work is supported in part by the Army High Performance Computing Research Center under the auspices of Department of the Army, Army Research Laboratory Cooperative agreement number DAAD19–01–2–0014, by the National Science Foundation under grant 9631539, and by the Minnesota Department of Transportation and the Center for Transportation Studies at the University of Minnesota under grant 1725–5216306
- Outlier detection
- Scalable algorithm for outlier detection
- Spatial data mining