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A semi-parametric geographically weighted (S-GWR) approach for modeling spatial distribution of population
Huang, Yaohuan1,2; Zhao, Chuanpeng1,2; Song, Xiaoyang3; Chen, Jie1; Li, Zhonghua1,2
2018-02-01
Source PublicationECOLOGICAL INDICATORS
ISSN1470-160X
Volume85Pages:1022-1029
Corresponding AuthorZhao, Chuanpeng(zhaocp.15s@igsnrr.ac.cn) ; Song, Xiaoyang(songxiaoyang.good@163.com)
AbstractSpatial Distribution of Population (SDP) has been recognized as a fundamental indicator of various studies including ecosystem assessment. To estimate SDP with fine resolution at a regional scale, an S-GWR model approach based on a land use map was developed. The model enhances SDP estimation accuracy by considering geo-spatial variation of population density and absolute accuracy in a demographic statistics unit that might introduce significant biases. The model is applied in estimating SDP of Shandong province, China, in 2000 with a resolution of 1 km. It was validated against census data and two common datasets for GPWv3 and CGPD both at the prefecture scale and sub-prefecture scale. The validation revealed that the mean absolute percentage error of SDP based on the S-GWR model (GSDP) is approximately 0 at the prefecture scale, which shows better performance than the other two datasets. The validation at the sub-prefecture scale in Tancheng county shows a mean absolute percentage error of 12.79% for GSDP in 17 townships, which is less than that of CGPD (15.37%) and GPWv3 (18.76%). Furthermore, spatial analysis of the error indicated that the S-GWR model spread the error into the region of Tancheng with the least percentage of towns (35.29%) with a percentage error larger than 15%, where the percentage of CGPD and the percentage of GPWv3 are 47.06% and 58.82%, respectively. The findings from the study demonstrated the great potential and value of the S-GWR model for regional SDP estimation.
KeywordPopulation Spatial distribution Semi-parametric geographically weighted regression Land use
DOI10.1016/j.ecolind.2017.11.028
WOS KeywordWATER-USE EFFICIENCY ; TUHAI-MAJIA BASIN ; SATELLITE IMAGERY ; CHINA ; DATABASE ; SCALE
Indexed BySCI
Language英语
Funding ProjectNational key Research and Development Program of China[2017YFB0503005] ; National key Research and Development Program of China[2016YFC0401404]
Funding OrganizationNational key Research and Development Program of China
WOS Research AreaBiodiversity & Conservation ; Environmental Sciences & Ecology
WOS SubjectBiodiversity Conservation ; Environmental Sciences
WOS IDWOS:000430634500101
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54805
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhao, Chuanpeng; Song, Xiaoyang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.China Univ Min & Technol, Beijing 100083, Peoples R China
Recommended Citation
GB/T 7714
Huang, Yaohuan,Zhao, Chuanpeng,Song, Xiaoyang,et al. A semi-parametric geographically weighted (S-GWR) approach for modeling spatial distribution of population[J]. ECOLOGICAL INDICATORS,2018,85:1022-1029.
APA Huang, Yaohuan,Zhao, Chuanpeng,Song, Xiaoyang,Chen, Jie,&Li, Zhonghua.(2018).A semi-parametric geographically weighted (S-GWR) approach for modeling spatial distribution of population.ECOLOGICAL INDICATORS,85,1022-1029.
MLA Huang, Yaohuan,et al."A semi-parametric geographically weighted (S-GWR) approach for modeling spatial distribution of population".ECOLOGICAL INDICATORS 85(2018):1022-1029.
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