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林爱文、博士生钟洋的论文在REMOTE SENSING 刊出
发布时间:2021-06-30 17:27:35     发布者:易真     浏览次数:

标题: Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

作者: Zhong, Y (Zhong, Yang); Lin, AW (Lin, Aiwen); Xiao, CW (Xiao, Chiwei); Zhou, ZG (Zhou, Zhigao)

来源出版物: REMOTE SENSING : 13 : 6 文献号: 1150 DOI: 10.3390/rs13061150 出版年: MAR 2021

摘要: In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China's Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran's I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of "east-west" spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

入藏号: WOS:000651980600001

语言: English

文献类型: Article

作者关键词: YREB; three urban agglomerations; DMSP; OLS night light data; EPC; spatiotemporal dynamics; Geographically weighted regression (GWR) model; random forest algorithm

地址: [Zhong, Yang; Lin, Aiwen; Zhou, Zhigao] Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Peoples R China.

[Zhong, Yang; Lin, Aiwen; Zhou, Zhigao] Wuhan Univ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China.

[Xiao, Chiwei] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.

通讯作者地址: Lin, AW (通讯作者)Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Peoples R China.

Lin, AW (通讯作者)Wuhan Univ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China.

电子邮件地址: zhongyang9093@whu.edu.cn; awlin@whu.edu.cn; xiaocw@igsnrr.ac.cn; leehong@whu.edu.cn

影响因子:4.509

 

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