太阳成8722(中国)有限公司-GREEN NO.1

标题: Modeling China's Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

作者: Cao, JP (Cao, Jiping); Chen, YM (Chen, Yumin); Wilson, JP (Wilson, John P.); Tan, HY (Tan, Huangyuan); Yang, JX (Yang, Jiaxin); Xu, ZQ (Xu, Zhiqiang)

来源出版物: REMOTE SENSING  卷: 12  期: 5  文献号: 839  DOI: 10.3390/rs12050839  出版年: MAR 2020  

摘要: Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R-2 = 0.98). The ordinary least squares (OLS) (mean R-2 = 0.86) and spatial error model (SEM) (mean pseudo R-2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China's prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

入藏号: WOS:000531559300094

语言: English

文献类型: Article

作者关键词: gross domestic product (GDP); prefecture level; eigenvector spatial filtering regression; spatial autocorrelation; nighttime light

地址: [Cao, Jiping; Chen, Yumin; Tan, Huangyuan; Yang, Jiaxin; Xu, Zhiqiang] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wilson, John P.] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA 90089 USA.

通讯作者地址: Chen, YM (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: caojiping@whu.edu.cn; ymchen@whu.edu.cn; jpwilson@usc.edu; tanhuangyuan@whu.edu.cn; yangjiaxin@whu.edu.cn; xuzq97@whu.edu.cn

影响因子：4.118