中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data

文献类型：期刊论文

作者Tu, Wenna3,4; Liu, Zhang3,5; Du, Yunyan3,4; Yi, Jiawei3,4; Liang, Fuyuan1; Wang, Nan3,4; Qian, Jiale3,4; Huang, Sheng3,4; Wang, Huimeng2
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2022-03-01
卷号107页码:13
关键词Dynamic population distribution Digital footprint Geospatial big data Ensemble learning Spatial dependence
ISSN号1569-8432
DOI10.1016/j.jag.2022.102709
通讯作者Du, Yunyan(duyy@lreis.ac.cn) ; Yi, Jiawei(yijw@lreis.ac.cn)
英文摘要Fine-scale population datasets are essential to many health and development applications. Quite a few population estimate approaches have been proposed and multiple gridded population datasets have been produced. However, it is still a challenge to accurately estimate daily and even hourly population dynamics. In this study, we present an ensemble learning approach to tackle the challenge through integrating a digital footprint dataset and multiple geospatial ancillary datasets to estimate population dynamics. More specifically, we used the geographically weighted regression model to integrate two aspatial tree-based learning models and generated preliminary hourly and daily gridded population estimates. We then adjusted the fine-scale population estimates based on the county-level estimates and their nonlinear relationship with the grid-level covariates. After sufficient model training and parameter tuning, we produced a series 0.01-degree gridded population maps (FinePop) of China for 2018, including a nationwide daily-average map and provincial hourly-average maps. The FinePop is more accurate than the WorldPop and LandScan datasets, as suggested by the highest R-2 (0.72) obtained from the comparison against township-level population census data. The root mean squared error of the township population density estimates for FinePop, WorldPop, and LandScan are 3162, 3327, and 3423, respectively. The FinePop also shows its advantages in unraveling transportation networks and the diurnal-nocturnal population migration patterns in both small and large cities.
WOS关键词URBAN-POPULATION ; LAND-COVER ; PREDICTION ; PLATEAU ; POVERTY
资助项目National Key Research and Development Program of China[2017YFC1503003] ; National Key Research and Development Program of China[2017YFB0503605] ; National Natural Science Foundation of China[41901395]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000830528400001
出版者ELSEVIER
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/181104]  
专题中国科学院地理科学与资源研究所
通讯作者Du, Yunyan; Yi, Jiawei
作者单位1.Western Illinois Univ, Dept Earth Atmospher & Geog Informat Sci, Macomb, IL 61455 USA
2.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Tencent Inc, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Tu, Wenna,Liu, Zhang,Du, Yunyan,et al. An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,107:13.
APA Tu, Wenna.,Liu, Zhang.,Du, Yunyan.,Yi, Jiawei.,Liang, Fuyuan.,...&Wang, Huimeng.(2022).An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,107,13.
MLA Tu, Wenna,et al."An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 107(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。