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Open AccessArticle

Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

1
School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
2
Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 839; https://doi.org/10.3390/rs12050839
Received: 8 December 2019 / Revised: 28 February 2020 / Accepted: 3 March 2020 / Published: 5 March 2020
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
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 R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 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. View Full-Text
Keywords: gross domestic product (GDP); prefecture level; eigenvector spatial filtering regression; spatial autocorrelation; nighttime light gross domestic product (GDP); prefecture level; eigenvector spatial filtering regression; spatial autocorrelation; nighttime light
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Cao, J.; Chen, Y.; Wilson, J.P.; Tan, H.; Yang, J.; Xu, Z. Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods. Remote Sens. 2020, 12, 839.

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