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Mapping the Population Density in Mainland China Using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model
Wang, Yunchen1,2; Huang, Chunlin1; Zhao, Minyan3; Hou, Jinliang1; Zhang, Ying1; Gu, J(顾娟)4
2020-11
Source PublicationREMOTE SENSING   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
EISSN2072-4292
Volume12Issue:21
page numbers22
AbstractUnderstanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m x 100 m grid in mainland China in 2015 (hereafter referred to as 'Popi'). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14,839 and 7218, respectively, for Popi, and 18,014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP).
Keywordpopulation density NPP VIIRS POI random forest China
PublisherMDPI
DOI10.3390/rs12213645
Indexed BySCIE ; SSCI
Language英语
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences[XDA19040500]
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000589355100001
PublisherMDPI
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/441517
Collection兰州大学
资源环境学院
Corresponding AuthorHuang, Chunlin
Affiliation
1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100000, Peoples R China
3.Univ Coll Dublin, UCD Sch Civil Engn, Dublin D04 V1W8, Ireland
4.Lanzhou Univ, Key Lab Western Chinas Environm Syst, Minist Educ, Lanzhou 730000, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yunchen,Huang, Chunlin,Zhao, Minyan,et al. Mapping the Population Density in Mainland China Using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model[J]. REMOTE SENSING,2020,12(21).
APA Wang, Yunchen,Huang, Chunlin,Zhao, Minyan,Hou, Jinliang,Zhang, Ying,&Gu, Juan.(2020).Mapping the Population Density in Mainland China Using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model.REMOTE SENSING,12(21).
MLA Wang, Yunchen,et al."Mapping the Population Density in Mainland China Using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model".REMOTE SENSING 12.21(2020).
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