SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows

doi:10.1080/13658816.2021.1899184

	SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows
	Liu, Qiliang 1; Yang, Jie 1; Deng, Min 1; Song, Ci 2; Liu, Wenkai 1
刊名	INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
	2021-03-17
页码	27
关键词	Origin– destination flow shared nearest-neighbor inhomogeneous distribution clustering human mobility
ISSN号	1365-8816
DOI	10.1080/13658816.2021.1899184
通讯作者	Deng, Min(dengmin@csu.edu.cn)
英文摘要	Identifying clusters from individual origin-destination (OD) flows is vital for investigating spatial interactions and flow mapping. However, detecting arbitrarily-shaped and non-uniform flow clusters from network-constrained OD flows continues to be a challenge. This study proposes a shared nearest-neighbor-based clustering method (SNN_flow) for inhomogeneous OD flows constrained by a road network. To reveal clusters of varying shapes and densities, a normalized density for each OD flow is defined based on the concept of shared nearest-neighbor, and flow clusters are constructed using the density-connectivity mechanism. To handle large amounts of disaggregated OD flows, an efficient method for searching the network-constrained k-nearest flows is developed based on a local road node distance matrix. The parameters of SNN_flow are statistically determined: the density threshold is modeled as a significance level of a significance test, and the number of nearest neighbors is estimated based on the variance of the kth nearest distance. SNN_flow is compared with three state-of-the-art methods using taxicab trip data in Beijing. The results show that SNN_flow outperforms existing methods in identifying flow clusters with irregular shapes and inhomogeneous distributions. The clusters identified by SNN_flow can reveal human mobility patterns in Beijing.
资助项目	National Key Research and Development Foundation of China[2017YFB0503601] ; National Natural Science Foundation of China (NSFC)[41971353] ; National Natural Science Foundation of China (NSFC)[41730105] ; National Natural Science Foundation of China (NSFC)[42071435] ; Natural Science Foundation of Hunan Province[2020JJ40669]
WOS研究方向	Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000629498200001
资助机构	National Key Research and Development Foundation of China ; National Natural Science Foundation of China (NSFC) ; Natural Science Foundation of Hunan Province
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162092]
专题	中国科学院地理科学与资源研究所
通讯作者	Deng, Min
作者单位	1.Cent South Univ, Dept Geoinformat, Changsha, Hunan, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Qiliang,Yang, Jie,Deng, Min,et al. SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2021:27.
APA	Liu, Qiliang,Yang, Jie,Deng, Min,Song, Ci,&Liu, Wenkai.(2021).SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,27.
MLA	Liu, Qiliang,et al."SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2021):27.