A hybrid approach to detect spatio-temporal outliers

A spatial outlier is a spatial referenced object whose non-spatial attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents locations that are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Here we adopt this definition to spatio-temporal domain and define a spatialtemporal outlier (STO) to be a spatial-temporal referenced object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighborhood. Identification of STOs can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial-temporal outliers has been seldom discussed. In this paper we propose a hybrid approach which integrates several data mining methods such as clustering, aggregation and comparisons to detect the STOs by evaluating the change between consecutive spatial and temporal scales.