Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea

doi:10.3390/ijgi6070210

	Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea
	Yang, Feng 1,2; Wu, Guofeng 1,3,4,5; Du, Yunyan 2; Zhao, Xiangwei 2,6
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2017-07-01
卷号	6 期号:7 页码:22
关键词	tropical cyclone data mining South China Sea trajectory clustering
ISSN号	2220-9964
DOI	10.3390/ijgi6070210
通讯作者	Wu, Guofeng(guofeng.wu@szu.edu.cn) ; Du, Yunyan(duyy@lreis.ac.cn)
英文摘要	The 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.
资助项目	National Science Foundation of China[41671445] ; National Science Foundation of China[41421001]
WOS关键词	WESTERN NORTH PACIFIC ; LARGE-SCALE CIRCULATION ; TYPHOON TRACKS ; PART II ; ENSO ; ATLANTIC ; VARIABILITY ; LANDFALL ; IDENTIFICATION ; TRANSITION
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000407506900027
资助机构	National Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/61527]
专题	中国科学院地理科学与资源研究所
通讯作者	Wu, Guofeng; Du, Yunyan
作者单位	1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone, Natl Adm Surveying Mapping & Geoinformat, Shenzhen 518060, Peoples R China 4.Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China 5.Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China 6.Shandong Univ Sci & Technol, Geomat Coll, Qingdao 266590, Peoples R China
推荐引用方式 GB/T 7714	Yang, Feng,Wu, Guofeng,Du, Yunyan,et al. Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(7):22.
APA	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 INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(7),22.
MLA	Yang, Feng,et al."Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.7(2017):22.