Feature analysis of weak nonlinear signals from geographic spatiotemporal data has received increasing attention. Most existing signal processing methods cannot effectively perform comprehensive feature analysis because of the multiple dimensions and unbalance of spatiotemporal data. We developed a divide–aggregate–explore method for the feature analysis of spatiotemporal data. In our method, strategies for dividing different dimensions are defined for multidimensional analysis, and the tensor–block structure is adopted to reorganize the original data and distinguish differences in dimensions. Then, information‐based data aggregation is used to weaken the impact of dimensional unbalance. Case studies based on climatic reanalysis field data released by the National Oceanic and Atmospheric Administration showed that the proposed method can effectively extract weak propagation signals such as the El Ni?o–Southern Oscillation and El Ni?o–Southern Oscillation Modoki. Our method can also reveal more detailed evolutionary characteristics of complex coupling systems in different dimensions compared with classical feature detection methods such as principal component analysis and tensor decomposition. Plain Language Summary The accurate extraction and pattern analysis of nonlinear, weak, and quasiperiodic signals from geographic spatiotemporal data is an important research direction. However, since most geographic data often has strong spatial heterogeneity, existing signal processing methods cannot effectively perform comprehensive feature analysis owing to the multiple and unbalance of dimensions. This paper introduces the information‐based data aggregation to weaken the impact of dimensional unbalance and uses tensor analysis to conduct the multidimensional analysis. The experiment results 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.
展开▼
