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Density-based clustering for bivariate-flow data

文献类型：期刊论文

作者Shu, Hua1; Pei, Tao1,2,3; Song, Ci1,3; Chen, Jie1; Chen, Xiao1,3; Guo, Sihui1,3; Liu, Yaxi1,3; Wang, Xi1,3; Wang, Xuyang1; Zhou, Chenghu1
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2022-05-17
页码21
关键词Origin-destination flow bivariate flow density-based clustering spatial statistics
ISSN号1365-8816
DOI10.1080/13658816.2022.2073595
通讯作者Pei, Tao(peit@lreis.ac.cn)
英文摘要Geographical flows reflect the movements, spatial interactions or connections among locations and are generally abstracted as origin-destination (OD) flows. In this context, clustering is a spatial pattern describing a group of flows with adjacent O and D points. For data composed of two types of flows (bivariate-flow data), a bivariate-flow cluster is a cluster comprising two types of flows, at least one of which exhibits a clustering pattern. In a bivariate-flow cluster, varying flow density combinations imply different meanings. For instance, a cluster with high-density travel flows on both weekdays (type A) and weekends (type B) may be associated with entertainment, whereas high-density flows on weekdays and sparse flows on weekends may reveal work-related travel. However, identifying bivariate-flow clusters with different flow density combinations is still an unsolved problem. To this end, we extend a bivariate-point clustering method and propose a density-based clustering method for bivariate flows. The simulation experiments verify model robustness. In a case study, we apply this method to extract clusters of bivariate-flow data comprising Beijing taxi OD flows of different periods, and identify clusters of work-related, entertainment, tourism, or egress and return travels. These results demonstrate the capability of our method in detecting bivariate-flow clusters.
WOS关键词ORIGIN-DESTINATION FLOWS ; K-FUNCTION ; DECOMPOSITION ; DOMAIN
资助项目National Natural Science Foundation of China[42101431] ; National Natural Science Foundation of China[42071436] ; China Postdoctoral Science Foundation[2020M680656] ; National Key R&D Program of China[2017YFB0503604]
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种英语
WOS记录号WOS:000797377400001
出版者TAYLOR & FRANCIS LTD
资助机构National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; National Key R&D Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/177079]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Pei, Tao
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Res, Nanjing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Shu, Hua,Pei, Tao,Song, Ci,et al. Density-based clustering for bivariate-flow data[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2022:21.
APA Shu, Hua.,Pei, Tao.,Song, Ci.,Chen, Jie.,Chen, Xiao.,...&Zhou, Chenghu.(2022).Density-based clustering for bivariate-flow data.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,21.
MLA Shu, Hua,et al."Density-based clustering for bivariate-flow data".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2022):21.

入库方式： OAI收割

来源：地理科学与资源研究所

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