摘要：Heterogeneity is an essential characteristic of the geographic phenomenon. However, most existing researches concerning heterogeneity are based on the matrix. The bidimensional nature of the matrix cannot well support the multidimensional analysis of spatiotemporal field data. Here, we introduce an improved tensor‐based feature analysis method for spatiotemporal field data with heterogeneous variation, by utilizing the similarity measurement in multidimensional space and feature capture of tensor decomposition. In this method, the heterogeneous spatiotemporal field data are reorganized first according to the similarity and difference within the data. The feature analysis by integrating the spatiotemporal coupling is then obtained by tensor decomposition. Since the reorganized data have a more consistent internal structure than original data, the feature analysis bias caused by heterogeneous variation in tensor decomposition can be effectively avoided. We demonstrate our method based on the climatic reanalysis field data released by the National Oceanic and Atmospheric Administration. The comparison with conventional tensor decomposition showed that the proposed method can approximate the original data more accurately both in global and local regions. Especially in the area influenced by the complex modal aliasing and in the period time of the climatic anomaly events, the approximation accuracy can be significantly improved. The proposed method can also reveal the zonal variation of temperature gradient and abnormal variations of air temperature ignored in the conventional tensor method. Plain Language Abstract The heterogeneity and the multidimensionality are essential characteristics of spatiotemporal data. However, few existing works incorporate both characteristics simultaneously in the process of feature analysis. In this paper, an improved tensor‐based method for the multidimensional analysis of spatiotemporal field data with heterogeneous variation was introduced. Specially, the local consistency of data and multidimensional feature captured by tensor decomposition are considered. The experiments verify the correctness and the advantages of our idea. We hope that our approach will provide you with an alternative method that deserves further study.
