A MULTISCALE APPROACH TO DETECT SPATIAL-TEMPORAL OUTLIERS
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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 spatial-temporal 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 multiscale approach to detect the STOs by evaluating the change between consecutive spatial and temporal scales.
[1] Vijayalakshmi Atluri,et al. Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets , 2004, SAC '04.
[2] Jiawei Han,et al. Geographic Data Mining and Knowledge Discovery , 2001 .
[3] W. R. Buckland. Outliers in Statistical Data , 1979 .
[4] Xuefeng Ya. Research issues in spatio-temporal data mining , 2003 .
[5] Raymond T Ng,et al. Detecting outliers from large datasets , 2001 .