A model to identify causality for geographic patterns
文献类型：期刊论文
| 作者 | Zhang, Zuopei1,2; Wang, Jinfeng1,2 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2025-11-06 |
| 卷号 | N/A |
| 关键词 | Causal inference spatial cross-sectional data Geographical Pattern Causality (GPC) symbolic dynamics nonlinear systems |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2025.2581207 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Identifying causal relationships is essential for understanding the mechanisms through which natural and anthropogenic factors interact within Earth systems. However, in spatial cross-sectional data, the absence of temporal ordering poses significant challenges to traditional causal inference methods. This study proposes a novel Geographical Pattern Causality (GPC) model to detect positive, negative, dark causality and its strength between variables in spatial data. Grounded in dynamical systems theory and generalized embedding principles, the method transforms spatial neighbourhoods into lagged sequences, reconstructs the phase space, and compares symbolic trajectories to assess predictability and consistency in pattern changes-thereby inferring both the direction and type of causality. Case studies demonstrated that, compared to correlation analysis and Linear Non-Gaussian Acyclic Model (LiNGAM), the GPC model could reveal latent causal relationships among weakly correlated variables in geographical systems and capture diverse causal patterns. Despite limitations, such as sensitivity to noise and potential biases from proxy variables, the GPC model provides a novel framework for causal inference based on spatial observations, and it advances both the methodological and theoretical development of causality analysis in complex geographical systems. |
| URL标识 | 查看原文 |
| WOS关键词 | POPULATION-DENSITY ; IDENTIFICATION ; VARIABLES ; POLLUTION ; IMPACT ; CHINA ; SOILS |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001609162000001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217820]  |
| 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 | Wang, Jinfeng |
| 作者单位 | 1.Univ Chinese Acad Sci, 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; |
| 推荐引用方式 GB/T 7714 | Zhang, Zuopei,Wang, Jinfeng. A model to identify causality for geographic patterns[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2025,N/A. |
| APA | Zhang, Zuopei,&Wang, Jinfeng.(2025).A model to identify causality for geographic patterns.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A. |
| MLA | Zhang, Zuopei,et al."A model to identify causality for geographic patterns".INTERNATIONAL JOURNAL OF GEOGRAPHICAL 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。