A trimmed mean approach to finding spatial outliers

Outlier detection concerns discovering some unusual data whose behavior is exceptional compared to other data. In contrast to non-spatial outliers which only consider non-spatial attributes, spatial outliers are defined to be those sites which are very different from its neighbors defined in terms of spatial attributes, i.e., locations. In this paper, we propose a local trimmed mean approach to evaluating the spatial outlier factor which is the degree that a site is outlying compared to its neighbors. The structure of our approach strictly follows the general spatial data model, which states spatial data consist of trend, dependence and error. We empirically demonstrate trimmed mean is more outlier-resistant than median in estimating sample location and it is employed to estimate spatial trend in our approach. In addition to using the 1st order neighbors in computing error, we also use higher order neighbors to estimate spatial trend. With true outlier factor supposed to be given by the spatial error model, we compare our approach with spatial statistic and scatter plot. Experimental results on two real datasets show our approach is significantly better than scatter plot, and slightly better than spatial statistic.