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A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data

文献类型：期刊论文

作者Chen, Yuehong2; Xu, Congcong2; Ge, Yong1; Zhang, Xiaoxiang2; Zhou, Ya'nan2
刊名EARTH SYSTEM SCIENCE DATA
出版日期2024-08-16
卷号16期号:8页码:3705-3718
DOI10.5194/essd-16-3705-2024
产权排序2
文献子类Article
英文摘要China has undergone rapid urbanization and internal migration in the past few years, and its up-to-date gridded population datasets are essential for various applications. Existing datasets for China, however, suffer from either outdatedness or failure to incorporate data from the latest Seventh National Population Census of China, conducted in 2020. In this study, we develop a novel population downscaling approach that leverages stacking ensemble learning and big geospatial data to produce up-to-date population grids at a 100 m resolution for China using seventh census data at both county and town levels. The proposed approach employs stacking ensemble learning to integrate the strengths of random forest, XGBoost, and LightGBM through fusing their predictions in a training mechanism, and it delineates the inhabited areas from big geospatial data to enhance the gridded population estimation. Experimental results demonstrate that the proposed approach exhibits the best-fit performance compared to individual base models. Meanwhile, the out-of-sample town-level test set indicates that the estimated gridded population dataset (R-2=0.8936) is more accurate than existing WorldPop (R-2=0.7427) and LandScan (R-2=0.7165) products for China in 2020. Furthermore, with the inhabited area enhancement, the spatial distribution of population grids is intuitively more reasonable than the two existing products. Hence, the proposed population downscaling approach provides a valuable option for producing gridded population datasets. The estimated 100 m gridded population dataset of China holds great significance for future applications, and it is publicly available at https://doi.org/10.6084/m9.figshare.24916140.v1 (Chen et al., 2024b).
WOS关键词GLOBAL POPULATION ; NIGHTTIME LIGHT ; DENSITY ; LEVEL
WOS研究方向Geology ; Meteorology & Atmospheric Sciences
WOS记录号WOS:001292301700001
出版者COPERNICUS GESELLSCHAFT MBH
源URL[http://ir.igsnrr.ac.cn/handle/311030/206906]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Ge, Yong
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Hohai Univ, Coll Geog & Remote Sensing, Nanjing 211100, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Yuehong,Xu, Congcong,Ge, Yong,et al. A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data[J]. EARTH SYSTEM SCIENCE DATA,2024,16(8):3705-3718.
APA Chen, Yuehong,Xu, Congcong,Ge, Yong,Zhang, Xiaoxiang,&Zhou, Ya'nan.(2024).A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data.EARTH SYSTEM SCIENCE DATA,16(8),3705-3718.
MLA Chen, Yuehong,et al."A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data".EARTH SYSTEM SCIENCE DATA 16.8(2024):3705-3718.

入库方式： OAI收割

来源：地理科学与资源研究所

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