A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China
文献类型：期刊论文
| 作者 | Xiong Junnan2,5; Li Kun2; Cheng Weiming1,4,5; Ye Chongchong2; Zhang Hao2,3 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2019 |
| 卷号 | 8期号:11页码:495 |
| 关键词 | population spatialization spatial stationarity geographically weighted regression DMSP/OLS land use |
| DOI | 10.3390/ijgi8110495 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Population 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. |
| 电子版国际标准刊号 | 2220-9964 |
| 语种 | 英语 |
| WOS记录号 | WOS:000502272600029 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/33569]  |
| 专题 | 中国科学院水利部成都山地灾害与环境研究所 |
| 通讯作者 | Li Kun |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 2.Southwest Petr Univ, Sch Civil Engn & Architecture, Chengdu 610500, Sichuan, Peoples R China; 3.Chinese Acad Sci, Inst Mt Disasters & Environm, Chengdu 610041, Sichuan, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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