Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data
文献类型：期刊论文
| 作者 | Yan, Xiaorui1; Pei, Tao1,2; Shu, Hua1; Song, Ci1; Wu, Mingbo1; Fang, Zidong1; Chen, Jie |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2023 |
| 关键词 | Geographical flow spatiotemporal L-function flow cluster spatiotemporal statistics |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2023.2204345 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | A geographical flow (hereafter flow) is defined as a movement between locations at two different times. A group of spatiotemporal flows can be viewed as a cluster if their origins and destinations are both spatiotemporally concentrated. Identifying spatiotemporal flow clusters may help reveal underlying spatiotemporal mobility trends or intensive relationships between regions. Despite recent advances in flow clustering methods, most only consider spatial attributes and ignore temporal information, and may fail to differentiate space-close but time-separated clusters. To this end, we derive global and local versions of the Spatiotemporal Flow L-function, extended from the classical L-function for points, and thereby construct a clustering method. First, the global version is utilized to check whether flow data contain clusters and estimate the spatial and temporal scales of the clusters. The local version is then employed to extract the clusters with the estimated scales. Experiments of simulated data demonstrate that our method outperforms three state-of-the-art methods in identifying spatiotemporal flow clusters with arbitrary shapes and different densities and reducing subjectivity in the parameter selection process. A case study with taxi data shows that our method reveals residents' spatiotemporal moving patterns, including rush-hour commuting and whole-daytime transferring among railway stations. |
| 学科主题 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| WOS关键词 | K-FUNCTION ; TIME ; SPACE |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193434]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 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.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Xiaorui,Pei, Tao,Shu, Hua,et al. Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2023. |
| APA | Yan, Xiaorui.,Pei, Tao.,Shu, Hua.,Song, Ci.,Wu, Mingbo.,...&Chen, Jie.(2023).Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE. |
| MLA | Yan, Xiaorui,et al."Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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