Statistical Modeling of Spatially Stratified Heterogeneous Data
文献类型：期刊论文
| 作者 | Wang, Jinfeng1,2; Haining, Robert3; Zhang, Tonglin4; Xu, Chengdong; Hu, Maogui5; Yin, Qian5; Li, Lianfa5; Zhou, Chenghu1,2; Li, Guangquan6; Chen, Hongyan7,8 |
| 刊名 | ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
 |
| 出版日期 | 2023-12-14 |
| 页码 | 21 |
| ISSN号 | 2469-4452 |
| 关键词 | confounding inference sample bias spatial causality spatially stratified heterogeneity |
| DOI | 10.1080/24694452.2023.2289982 |
| 通讯作者 | Wang, Jinfeng(wangjf@Lreis.ac.cn) |
| 英文摘要 | Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another "layer" of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis. |
| WOS关键词 | ASSOCIATION ; INTERPOLATION ; CLIMATE ; TIME |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Geography |
| 语种 | 英语 |
| 出版者 | ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:001158200800001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202562]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Jinfeng |
| 作者单位 | 1.Chinese Acad Sci, LREIS, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Univ Cambridge, Dept Geog, Cambridge, England 4.Purdue Univ, Dept Stat, W Lafayette, IN USA 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, LREIS, Beijing, Peoples R China 6.Northumbria Univ, Dept Math Phys & Elect Engn, Newcastle Upon Tyne, England 7.Univ Lancaster, Lancaster Environm Ctr, Lancaster, England 8.UK Ctr Ecol & Hydrol, Lancaster, England |
| 推荐引用方式 GB/T 7714 | Wang, Jinfeng,Haining, Robert,Zhang, Tonglin,et al. Statistical Modeling of Spatially Stratified Heterogeneous Data[J]. ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,2023:21. |
| APA | Wang, Jinfeng.,Haining, Robert.,Zhang, Tonglin.,Xu, Chengdong.,Hu, Maogui.,...&Chen, Hongyan.(2023).Statistical Modeling of Spatially Stratified Heterogeneous Data.ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,21. |
| MLA | Wang, Jinfeng,et al."Statistical Modeling of Spatially Stratified Heterogeneous Data".ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS (2023):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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