An Algorithm for Spatial Outlier Detection Based on Delaunay Triangulation

Spatial outliers represent spatial objects whose non-spatial attributes are significantly different from their spatial neighborhoods. Spatial outlier detection can provide user with unexpected, interesting and useful spatial patterns for further analysis and has received a lot of attention. However, many existing methods for spatial outlier mining use the k-neighborhood method to determine spatial neighborhood which depends on a priori parameter k, and don¿t consider spatial autocorrelation. As a result, it usually violates the true situation. So we propose a similarity measurement between spatial objects that based on Delaunay triangulation (DT_SOF), which captures spatial correlation and spatial neighborhood from the dataset itself. Furthermore, the measure takes in account the local behavior of a spatial object in its neighborhood. Finally, experimentations on a synthetic and a real-world ecological geochemical dataset demonstrate that our approach can effectively detect spatial outliers with a lower disturbance by human intervention.