Improving fine-grained population distribution prediction by considering region-distinctive geographical factors-A case of Pearl River Delta, China
文献类型：期刊论文
| 作者 | Gao, Ku1,2; Yang, Xiaomei1,2; Liu, Yueming1,2; Zhang, Qingyang1,2; Wang, Zhihua1,2 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 147页码:105168 |
| 关键词 | Urban population distribution Fine-grained predicting Machine learning Geographical factor Coastal zones |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2026.105168 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Predicting fine-grained population distribution is crucial for effective urban planning. However, existing models widely ignore Region-Distinctive Geographical Factors (RDGF) in regional population modeling. This omission may compromise prediction accuracy, particularly in coastal zones where over 50% of the global population. To address this gap, we proposed an RDGF-incorporated approach for fine-grained population prediction, using the coastal Pearl River Delta as a case study. Leveraging multi-source geospatial data, based on generalized geographical factors (GGF) (e.g., topography, POI density, nighttime light intensity, etc.), we supplemented multi-dimensional RDGF including ecology, agriculture and transportation, etc. derived from unique regional environments (e.g., distance to shoreline, aquaculture, ports, etc.). We employed an interpretable machine learning framework (Random Forest + SHAP) to model and explain factor contribution. Results demonstrate: (1) incorporating RDGF substantially improves prediction accuracy in both model performance (with the average R2 increasing by 6% under spatial cross-validation) and output (The relative error in densely populated areas can be reduced by up to 40%), thereby providing opportunity for more effective infrastructure planning and disaster risk management. (2) GGF still make the primary contribution to the model; however, RDGF are able to reveal local spatial heterogeneity and geographic decay patterns in population distribution, demonstrating greater potential for reducing prediction errors. This study provides region-specific insights for generating large-scale, fine-grained population map. |
| URL标识 | 查看原文 |
| WOS关键词 | REGRESSION |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001698162200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221334]  |
| 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 | Wang, Zhihua |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, 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; |
| 推荐引用方式 GB/T 7714 | Gao, Ku,Yang, Xiaomei,Liu, Yueming,et al. Improving fine-grained population distribution prediction by considering region-distinctive geographical factors-A case of Pearl River Delta, China[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2026,147:105168. |
| APA | Gao, Ku,Yang, Xiaomei,Liu, Yueming,Zhang, Qingyang,&Wang, Zhihua.(2026).Improving fine-grained population distribution prediction by considering region-distinctive geographical factors-A case of Pearl River Delta, China.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,147,105168. |
| MLA | Gao, Ku,et al."Improving fine-grained population distribution prediction by considering region-distinctive geographical factors-A case of Pearl River Delta, China".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 147(2026):105168. |
入库方式： OAI收割
来源：地理科学与资源研究所
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