Enhancing bivariate spatial association analysis of network-constrained geographical flows: An incremental scale-based method
文献类型：期刊论文
| 作者 | Liu, Wenkai1,3; Cai, Haonan3; Zhang, Weijie3; Hu, Sheng3; Tan, Zhangzhi3; Cai, Jiannan4; Xing, Hanfa2,3 |
| 刊名 | SPATIAL STATISTICS
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 63页码:100852 |
| 关键词 | Geographical flow Spatial association Road network Bivariate flow Cross-K function |
| DOI | 10.1016/j.spasta.2024.100852 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Measuring bivariate spatial association plays a key role in understanding the spatial relationships between two types of geographical flow (hereafter referred to as flow). However, existing studies usually use multiple scales to analyze bivariate associations of flows, leading to the results at larger scales can be strongly affected by the results at smaller scales. Moreover, the planar space assumption of most existing studies is unsuitable for network-constrained flows. To solve these problems, a network incremental flow cross K-function ( NIFK ) is developed in this study by extending the cross K-function for points into a flow context. Specifically, two versions of NIFK were developed in this study: the global version to check whether bivariate associations exist in the whole study area and the local version to identify specific locations where associations occur. Experiments on three simulated datasets demonstrate the advantages of the proposed method over an available alternative method. A case study conducted using Xiamen taxi and ride-hailing service datasets demonstrates the usefulness of the proposed method. The detected bivariate spatial association provides deep insights for understanding the competition between taxi services and ride-hailing services. |
| WOS关键词 | ORIGIN-DESTINATION FLOWS ; COLOCATION QUOTIENT ; CLUSTERS ; PATTERN |
| WOS研究方向 | Geology ; Mathematics ; Remote Sensing |
| WOS记录号 | WOS:001295183500001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206878]  |
| 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 | Xing, Hanfa |
| 作者单位 | 1.State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.South China Normal Univ, Sch Geog & Environm Sci, Guangzhou, Peoples R China 3.South China Normal Univ, Beidou Res Inst, Foshan, Guangdong, Peoples R China 4.Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Wenkai,Cai, Haonan,Zhang, Weijie,et al. Enhancing bivariate spatial association analysis of network-constrained geographical flows: An incremental scale-based method[J]. SPATIAL STATISTICS,2024,63:100852. |
| APA | Liu, Wenkai.,Cai, Haonan.,Zhang, Weijie.,Hu, Sheng.,Tan, Zhangzhi.,...&Xing, Hanfa.(2024).Enhancing bivariate spatial association analysis of network-constrained geographical flows: An incremental scale-based method.SPATIAL STATISTICS,63,100852. |
| MLA | Liu, Wenkai,et al."Enhancing bivariate spatial association analysis of network-constrained geographical flows: An incremental scale-based method".SPATIAL STATISTICS 63(2024):100852. |
入库方式： OAI收割
来源：地理科学与资源研究所
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