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

文献类型：期刊论文

作者Yang, Feng1,2; Wu, Guofeng1,3,4,5; Du, Yunyan2; Zhao, Xiangwei2,6
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期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
DOI10.3390/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.
WOS关键词WESTERN NORTH PACIFIC ; LARGE-SCALE CIRCULATION ; TYPHOON TRACKS ; PART II ; ENSO ; ATLANTIC ; VARIABILITY ; LANDFALL ; IDENTIFICATION ; TRANSITION
资助项目National Science Foundation of China[41671445] ; National Science Foundation of China[41421001]
WOS研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:000407506900027
出版者MDPI AG
资助机构National Science Foundation of China
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.

入库方式： OAI收割

来源：地理科学与资源研究所

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