Understanding and extending the geographical detector model under a linear regression framework
文献类型：期刊论文
| 作者 | Zhang, Hang1,2,3; Dong, Guanpeng1,2,3; Wang, Jinfeng4; Zhang, Tong-Lin5; Meng, Xiaoyu2; Yang, Dongyang2; Liu, Yong2; Lu, Binbin6 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2023-10-06 |
| 页码 | 17 |
| ISSN号 | 1365-8816 |
| 关键词 | Spatial autocorrelation geographical detector model variable importance decomposition |
| DOI | 10.1080/13658816.2023.2266497 |
| 通讯作者 | Dong, Guanpeng(gpdong@vip.henu.edu.cn) |
| 英文摘要 | The Geographical Detector Model (GDM) is a popular statistical toolkit for geographical attribution analysis. Despite the striking resemblance of the q-statistic in GDM to the R-squared in linear regression models, their explicit connection has not yet been established. This study proves that the q-statistic reduces into the R-squared under a linear regression framework. Under linear regression and moderate-to-strong spatial autocorrelation, Monte Carlo simulation results show that the GDM tends to underestimate the importance of variables. In addition, an almost perfect power law relationship is present between the percentage bias and the degree of the spatial autocorrelations, indicating the presence of fast uplifting bias in response to increasing levels of spatial autocorrelations. We propose an integrated approach for variable importance quantification by bringing together the spatial econometrics model and the game theory based-Shapley value method. By applying our proposed methodology to a case study of land desertification in African, it is found human activity tends to affect land desertification both directly and indirectly. However, such effects appear to be underestimated or undistinguished in the classic GDM. |
| WOS关键词 | DESERTIFICATION |
| 资助项目 | The authors much appreciate the comments from the reviewers and editors, which improve the quality of the paper greatly. |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:001078067200001 |
| 资助机构 | The authors much appreciate the comments from the reviewers and editors, which improve the quality of the paper greatly. |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/198524]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Dong, Guanpeng |
| 作者单位 | 1.Henan Univ, Climate Change & Carbon Neutral Lab, Kaifeng, Peoples R China 2.Henan Univ, Key Res Inst Yellow River Civilizat & Sustainable, Kaifeng, Peoples R China 3.Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow R, Kaifeng, Peoples R China 4.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 5.Purdue Univ, Dept Stat, W Lafayette, IN USA 6.Wuhan Univ, Sch Remote Sensing Informat Engn, Wuhan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Hang,Dong, Guanpeng,Wang, Jinfeng,et al. Understanding and extending the geographical detector model under a linear regression framework[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2023:17. |
| APA | Zhang, Hang.,Dong, Guanpeng.,Wang, Jinfeng.,Zhang, Tong-Lin.,Meng, Xiaoyu.,...&Lu, Binbin.(2023).Understanding and extending the geographical detector model under a linear regression framework.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,17. |
| MLA | Zhang, Hang,et al."Understanding and extending the geographical detector model under a linear regression framework".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2023):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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