干旱区生态环境知识资源中心(Arid): Understanding and extending the geographical detector model under a linear regression framework

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DOI	10.1080/13658816.2023.2266497
	Understanding and extending the geographical detector model under a linear regression framework
	Zhang, Hang; Dong, Guanpeng; Wang, Jinfeng; Zhang, Tong-Lin; Meng, Xiaoyu; Yang, Dongyang; Liu, Yong; Lu, Binbin
通讯作者	Dong, GP
来源期刊	INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN	1365-8816
EISSN	1362-3087
出版年	2023
卷号	37 期号:11 页码:2437-2453
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.
英文关键词	Spatial autocorrelation geographical detector model variable importance decomposition
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E ; SSCI
WOS记录号	WOS:001078067200001
WOS关键词	DESERTIFICATION ; SATISFACTION
WOS类目	Computer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS研究方向	Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397001
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],2023,37(11):2437-2453.
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,37(11),2437-2453.
MLA	Zhang, Hang,et al."Understanding and extending the geographical detector model under a linear regression framework".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 37.11(2023):2437-2453.