Quantifying the spatial heterogeneity of geographical flows
文献类型：期刊论文
| 作者 | Shu, Hua4; Pei, Tao1,2,3; Song, Ci1,2; Guo, Sihui1,2; Chen, Jie1; Liu, Yaxi1,2; Wang, Xi1,2; Chen, Xiao1,2; Zhou, Chenghu1 |
| 刊名 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 出版日期 | 2025-10-10 |
| 卷号 | N/A |
| 关键词 | OD flow spatial heterogeneity spatial statistics nearest-neighbor distance-based statistics |
| ISSN号 | 1009-5020 |
| DOI | 10.1080/10095020.2025.2559952 |
| 产权排序 | 2 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Geographical flows describe the movements and connections of materials, energy, and information among locations and are commonly represented by origin-destination (OD) flows (flows for short). The spatial heterogeneity of such flows is characterized by their inhomogeneous distributions and offers a novel perspective for revealing the global spatial pattern of relevant geographical phenomena. In practice, comparing the spatial heterogeneity of different flow datasets is essential for gaining deeper insights into their global spatial patterns. This requires reliable quantification of the spatial heterogeneity of flows, a challenge that has been largely overlooked in previous studies. To address this gap, we first define the spatial heterogeneity of flows as the degree of deviation from complete spatial randomness (CSR). Based on this definition, we propose a benchmark spatial heterogeneity metric for flows called the normalized level of flow heterogeneity (NLFH*). Additionally, we propose nine nearest-neighbor (NN) distance-based statistics for flows by extending relevant methods for points. Simulation experiments and case studies involving tropical cyclone tracks and taxi OD data demonstrate that statistic NLFH*, along with two NN distance-based statistics of flows (FA-w and FH-xw), outperforms other statistics in quantifying the spatial heterogeneity of flows. Among them, FA-w and FH-xw are recommended for practical use due to their powerful performance and computational efficiency. |
| URL标识 | 查看原文 |
| WOS关键词 | POINT PATTERN-ANALYSIS ; ORIGIN ; PREDICTABILITY ; TRENDS |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001590135700001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217515]  |
| 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, Peoples R China; 2.Univ Chinese Acad Sci, Beijing, Peoples R China; 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China 4.Hubei Univ, Sch Comp Sci, Wuhan, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Shu, Hua,Pei, Tao,Song, Ci,et al. Quantifying the spatial heterogeneity of geographical flows[J]. GEO-SPATIAL INFORMATION SCIENCE,2025,N/A. |
| APA | Shu, Hua.,Pei, Tao.,Song, Ci.,Guo, Sihui.,Chen, Jie.,...&Zhou, Chenghu.(2025).Quantifying the spatial heterogeneity of geographical flows.GEO-SPATIAL INFORMATION SCIENCE,N/A. |
| MLA | Shu, Hua,et al."Quantifying the spatial heterogeneity of geographical flows".GEO-SPATIAL INFORMATION SCIENCE N/A(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。