Multidimensional Feature Explorer for Unbalanced Spatiotemporal Data

Abstract

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 Niño–Southern Oscillation and El Niño–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.

Synchronized Entry-Traffic Flow Prediction for Regional Expressway System Based on Multidimensional Tensor

“…CP decomposition is one of the popular tensor decomposition methods, which can generate a concise result of dimensional features. In addition, CP decomposition has also been used to extract the temporal features in other studies ( 31 ). Therefore, we use CP decomposition to extract the temporally changing features of the entry-traffic flow tensor.…”

Global Land Surface Dry/Wet Conditions Mining Based on Spatial‐Temporal Association Rules

“…With the rapid development of earth‐observing technologies, large amounts of earth science data have been collected (Z. He et al., 2020; D. Li et al., 2019; T. Wu et al., 2008). A great deal of information and knowledge is hidden in these spatial‐temporal data (Chai et al., 2011; Xu et al., 2017), such as geographic association patterns among severe dry/wet conditions.…”

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With the rapid development of earth-observing technologies, large amounts of earth science data have been collected (Z. He et al, 2020;D. Li et al, 2019;T.

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