：提出一种时空一体化的时空异常探测方法，首先基于时空统计学与聚类分析构建一体化时空邻近域。进而， 发展兼顾时空相关与异质性的时空异常度量方法。最后，采用一种3步骤的策略探测时空异常。应用本文方法探测中国 陆地区域33年(1970年—2002年)的年平均气温时空数据中的时空异常，探测结果具有较好的可靠性，反映时空数据的时 空一体化特征。同时，对时空异常的产生机理与实际意义进行分析和解释。

A novel spatio-temporal outlier detection method within the space-time framework is proposed in this paper. Firstly, a unifi ed framework is developed for constructing spatio-temporal neighborhood, which is based on the space-time statistics and clustering analysis. Then, a spatio-temporal outlier measure involving space-time autocorrelation and heterogeneity is presented. Finally, a tree-step strategy is utilized to detect spatio-temporal outliers. Our method is employed to detect spatio-temporal outliers in Chinese annual temperature database (1970-2002). A meaningful analysis of the spatio-temporal outliers is also provided. Key words: spatio-temporal outlier detection, spatio-temporal neighborhood, space-time statistics, clustering analysis