SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows
文献类型：期刊论文
| 作者 | Liu, Qiliang2; Yang, Jie2; Deng, Min2; Song, Ci1; Liu, Wenkai2 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000629498200001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 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 |
| 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.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Cent South Univ, Dept Geoinformat, Changsha, Hunan, 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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