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Trajectory Data Mining via Cluster Analyses for Tropical Cyclones That Affect the South China Sea
Yang, Feng1,2; Wu, Guofeng1,3,4,5; Du, Yunyan2; Zhao, Xiangwei2,6
2017-07-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume6Issue:7Pages:22
Corresponding AuthorWu, Guofeng(guofeng.wu@szu.edu.cn) ; Du, Yunyan(duyy@lreis.ac.cn)
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.
Keywordtropical cyclone data mining South China Sea trajectory clustering
DOI10.3390/ijgi6070210
WOS KeywordWESTERN NORTH PACIFIC ; LARGE-SCALE CIRCULATION ; TYPHOON TRACKS ; PART II ; ENSO ; ATLANTIC ; VARIABILITY ; LANDFALL ; IDENTIFICATION ; TRANSITION
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation of China[41671445] ; National Science Foundation of China[41421001]
Funding OrganizationNational Science Foundation of China
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000407506900027
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/61528
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Guofeng; Du, Yunyan
Affiliation1.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
Recommended Citation
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.
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