Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000

doi:10.3390/su12031231

	Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000
	Wang, Fahao 1,2; Lu, Weidong 4; Zheng, Jingyun 2,3; Li, Shicheng 5; Zhang, Xuezhen 2,3
刊名	SUSTAINABILITY
	2020-02-01
卷号	12 期号:3 页码:16
关键词	historical period random forest regression model population density prediction Gansu Province
DOI	10.3390/su12031231
通讯作者	Zhang, Xuezhen(xzzhang@igsnrr.ac.cn)
英文摘要	This study established a random forest regression model (RFRM) using terrain factors, climatic and river factors, distances to the capitals of provinces, prefectures (Fu, in Chinese Pinyin), and counties as independent variables to predict the population density. Then, using the RFRM, we explicitly reconstructed the spatial distribution of the population density of Gansu Province, China, in 1820 and 2000, at a resolution of 10 by 10 km. By comparing the explicit reconstruction with census data at the township level from 2000, we found that the RFRM-based approach mostly reproduced the spatial variability in the population density, with a determination coefficient (R-2) of 0.82, a positive reduction of error (RE, 0.72) and a coefficient of efficiency (CE) of 0.65. The RFRM-based reconstructions show that the population of Gansu Province in 1820 was mostly distributed in the Lanzhou, Gongchang, Pingliang, Qinzhou, Qingyang, and Ningxia prefecture. The macro-spatial pattern of the population density in 2000 kept approximately similar with that in 1820. However, fine differences could be found. The 79.92% of the population growth of Gansu Province from 1820 to 2000 occurred in areas lower than 2500 m. As a result, the population weighting in the areas above 2500 m was similar to 9% in 1820 while it was greater than 14% in 2000. Moreover, in comparison to 1820, the population density intensified in Lanzhou, Xining, Yinchuan, Baiyin, Linxia, and Tianshui, while it weakened in Gongchang, Qingyang, Ganzhou, and Suzhou.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040101] ; National Key Research and Development Program of China[2017YFA0603300] ; Key Research Program from CAS[QYZDB-SSW-DQC005] ; Key Research Program from CAS[ZDRW-ZS-2017-4]
WOS关键词	INTERPOLATION METHODS ; QILIAN MOUNTAINS ; HUMAN SETTLEMENT ; RELIEF DEGREE ; LAND-SURFACE ; RECONSTRUCTION ; CLIMATE ; IMAGERY ; CARBON ; PERIOD
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
语种	英语
出版者	MDPI
WOS记录号	WOS:000524899604012
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; Key Research Program from CAS
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/133811]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Xuezhen
作者单位	1.Shandong Normal Univ, Coll Geog & Environm, Jinan 250358, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Fudan Univ, Ctr Hist Geog Studies, Shanghai 200433, Peoples R China 5.China Univ Geosci, Sch Publ Adm, Dept Land Resource Management, Wuhan 430074, Peoples R China
推荐引用方式 GB/T 7714	Wang, Fahao,Lu, Weidong,Zheng, Jingyun,et al. Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000[J]. SUSTAINABILITY,2020,12(3):16.
APA	Wang, Fahao,Lu, Weidong,Zheng, Jingyun,Li, Shicheng,&Zhang, Xuezhen.(2020).Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000.SUSTAINABILITY,12(3),16.
MLA	Wang, Fahao,et al."Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000".SUSTAINABILITY 12.3(2020):16.