Causal inference from cross-sectional earth system data with geographical convergent cross mapping
文献类型：期刊论文
| 作者 | Gao, Bingbo1; Yang, Jianyu1; Chen, Ziyue2; Sugihara, George3; Li, Manchun4; Stein, Alfred5; Kwan, Mei-Po; Wang, Jinfeng9 |
| 刊名 | NATURE COMMUNICATIONS
 |
| 出版日期 | 2023-09-21 |
| 卷号 | 14期号:1页码:5875 |
| DOI | 10.1038/s41467-023-41619-6 |
| 产权排序 | 10 |
| 文献子类 | Article |
| 英文摘要 | Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect. Temporal causation models perform poorly in causal inference for variables with limited temporal variations. This paper establishes a causal inference model, which can reveal the nonlinear complex casual associations based on cross-sectional Earth System data. |
| WOS关键词 | HEAVY-METAL POLLUTION ; POPULATION-DENSITY ; MODELS ; CHINA ; IDENTIFICATION ; APPORTIONMENT ; IMPACT ; SOILS |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001075884500017 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/198872]  |
| 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.China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China 2.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agr Disasters, Beijing, Peoples R China 3.Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China 4.Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA USA 5.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing, Peoples R China 6.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands 7.Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Peoples R China 8.Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Peoples R China 9.Univ Utrecht, Dept Human Geog & Spatial Planning, Utrecht, Netherlands 10.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 | Gao, Bingbo,Yang, Jianyu,Chen, Ziyue,et al. Causal inference from cross-sectional earth system data with geographical convergent cross mapping[J]. NATURE COMMUNICATIONS,2023,14(1):5875. |
| APA | Gao, Bingbo.,Yang, Jianyu.,Chen, Ziyue.,Sugihara, George.,Li, Manchun.,...&Wang, Jinfeng.(2023).Causal inference from cross-sectional earth system data with geographical convergent cross mapping.NATURE COMMUNICATIONS,14(1),5875. |
| MLA | Gao, Bingbo,et al."Causal inference from cross-sectional earth system data with geographical convergent cross mapping".NATURE COMMUNICATIONS 14.1(2023):5875. |
入库方式： OAI收割
来源：地理科学与资源研究所
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