Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea
AbstractThe equal division of tropical cyclone (TC) trajectory method, the mass moment of the TC trajectory method, and the mixed regression model method are clustering algorithms that use space and shape information from complete TC trajectories. In this article, these three clustering algorithms were applied in a TC trajectory clustering analysis to identify the TCs that affected the South China Sea (SCS) from 1949 to 2014. According to their spatial position and shape similarity, these TC trajectories were classified into five trajectory classes, including three westward straight-line movement trajectory clusters and two northward re-curving trajectory clusters. These clusters show different characteristics in their genesis position, heading, landfall location, TC intensity, lifetime and seasonality distribution. The clustering results indicate that these algorithms have different characteristics. The equal division of the trajectory method provides better clustering result generally. The approach is simple and direct, and trajectories in the same class were consistent in shape and heading. The regression mixture model algorithm has a solid theoretical mathematical foundation, and it can maintain good spatial consistency among trajectories in the class. The mass moment of the trajectory method shows overall consistency with the equal division of the trajectory method. View Full-Text
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Yang, F.; Wu, G.; Du, Y.; Zhao, X. Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea. ISPRS Int. J. Geo-Inf. 2017, 6, 210.
Yang F, Wu G, Du Y, Zhao X. Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea. ISPRS International Journal of Geo-Information. 2017; 6(7):210.Chicago/Turabian Style
Yang, Feng; Wu, Guofeng; Du, Yunyan; Zhao, Xiangwei. 2017. "Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea." ISPRS Int. J. Geo-Inf. 6, no. 7: 210.
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