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A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China
Xiong Junnan1,2; Li Kun1; Cheng Weiming2,3,4; Ye Chongchong1; Zhang Hao1,5
2019
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
EISSN2220-9964
Volume8Issue:11Pages:495
SubtypeArticle
Contribution Rank5
AbstractPopulation is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.
Keywordpopulation spatialization spatial stationarity geographically weighted regression DMSP/OLS land use
DOI10.3390/ijgi8110495
Indexed BySCI
Language英语
WOS IDWOS:000502272600029
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/33569
Collection中国科学院水利部成都山地灾害与环境研究所
Corresponding AuthorLi Kun
Affiliation1.Southwest Petr Univ, Sch Civil Engn & Architecture, Chengdu 610500, Sichuan, Peoples R China;
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China;
5.Chinese Acad Sci, Inst Mt Disasters & Environm, Chengdu 610041, Sichuan, Peoples R China
Recommended Citation
GB/T 7714
Xiong Junnan,Li Kun,Cheng Weiming,et al. A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(11):495.
APA Xiong Junnan,Li Kun,Cheng Weiming,Ye Chongchong,&Zhang Hao.(2019).A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,8(11),495.
MLA Xiong Junnan,et al."A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 8.11(2019):495.
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