Spatial association measures for time series with fixed spatial locations
文献类型：期刊论文
| 作者 | Guo, Jinzhao1,3; Zhang, Haiping1; Ye, Xiang2,3; Wang, Haoran2,3; Yang, Yu1; Tang, Guoan3 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2024-12-24 |
| 卷号 | N/A |
| 关键词 | Spatial association spatial time series spatiotemporal dynamics spatial statistics |
| DOI | 10.1080/13658816.2024.2445185 |
| 产权排序 | 2 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Spatial time series (STS), which refers to time-series data collected at fixed spatial locations, is crucial for understanding the spatiotemporal dynamics of geographical phenomena. Measuring the spatial association based on STS similarity provides valuable insights into the exploratory analysis of spatiotemporal data. However, existing methods are not effective in accurately quantifying such spatial association. To address this gap, this study proposes a conceptual model and a statistical method for identifying spatial clusters that exhibit significantly similar time-varying characteristics within a set of STS data. Conceptually, three representative patterns are defined: positive, negative, and no associations. A positive pattern occurs when spatially adjacent STSs show similar time-varying characteristics, while a negative pattern occurs when they show dissimilar ones. Technically, this study introduces a distance metric to measure similarities among STSs. The spatial association of STS at global and local scales is quantified according to the spatial concentration of these similarities. The validity and applicability of the proposed statistics are verified through synthetic and real-world examples, demonstrating their potential as effective tools for understanding spatiotemporal dynamics from a new perspective. |
| WOS关键词 | EXTENDING MORANS INDEX ; SPATIOTEMPORAL AUTOCORRELATION ; SEGMENTATION |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| WOS记录号 | WOS:001383524600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210416]  |
| 专题 | 区域可持续发展分析与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhang, Haiping |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Reg Sustainable Dev Modeling, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 3.Nanjing Normal Univ, Sch Geog, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Jinzhao,Zhang, Haiping,Ye, Xiang,et al. Spatial association measures for time series with fixed spatial locations[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2024,N/A. |
| APA | Guo, Jinzhao,Zhang, Haiping,Ye, Xiang,Wang, Haoran,Yang, Yu,&Tang, Guoan.(2024).Spatial association measures for time series with fixed spatial locations.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A. |
| MLA | Guo, Jinzhao,et al."Spatial association measures for time series with fixed spatial locations".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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