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A Climate Classification of China through k-Nearest-Neighbor and Sparse Subspace Representation

Climate classification aims to divide a given region into distinct groups on the basis of meteorological variables, and it has important applications in fields such as agriculture and buildings. In this paper, we propose a novel spectral clustering–based method to partition 661 meteorological stations in China into several zones. First, the correlations are analyzed among five meteorological variables: daily average temperature, average relative humidity, sunshine hours, diurnal temperature range, and atmospheric pressure. The resulting weak linear correlation supports the classification under multiple views. Next, a similarity matrix/graph is constructed by combining the advantages of k-nearest-neighbor and sparse subspace representation. The blocking effect of the similarity matrix confirms the rationality of the classification. Then, we consider respectively the climate classification under a single view and multiple views. For the single view, atmospheric pressure has the highest imbalance degree, and sunshine hours and diurnal temperature range have the strongest classification consistency. The consistency rates are improved evidently in a multiple-view situation. Afterward, we propose a determining method for the number of clusters and make a sensitivity analysis on various parameters. Finally, we provide a further statistical analysis on the classification results and compare the consistency with another climate dataset. All experimental results show that the proposed classification method is feasible and effective in the climate zoning of China.