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

Abstract

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.

A novel classification regression method for gridded electric power consumption estimation in China

“…The VIIRS/DNB NTL data provided by the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NOAA/NCEI, https://ngdc.noaa.gov/eog/download.html ) were used in this study. This dataset has widely used for socioeconomic indicators spatialization owing to its fine resolution 21 , 31 , 35 . However, the data are vulnerable to stray light, resulting in emergence of distorted pixels in the mid and high latitude areas, especially in the summer 32 , 34 , 36 , 37 .…”

LMedS-Based Power Regression: An Optimal and Automatic Method of Radiometric Intercalibration for DMSP-OLS NTL Imagery

“…OLS night-time light (NTL) imageries have been widely and successfully implemented in studying human activity [5], e.g., the evolution of urban spatial patterns [6]- [12], the evaluation of the economic development level [13]- [19], the estimation of population density [20]- [24], the estimation of power consumption [25], [26], the assessment of carbon emission [27]- [29], and the study of the heat island effect [30]- [32], etc.…”

Quantitative Estimation of Missing Value Interpolation Methods for Suomi-NPP VIIRS/DNB Nighttime Light Monthly Composite Images

“…The outdoor anthropogenic nighttime light (NTL) data derived from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Orbiting Partnership Satellite (Suomi-NPP) offer a unique view for observation of the Earth's surface. Nighttime light data are suitable for human activity detection in the long term and large scale and are widely used in monitoring the dynamic characteristics of population growth [1][2][3], greenhouse gas emissions [4][5][6], economic development [7][8][9], industrial manufacturing [10,11], war damage [12][13][14] and other related fields [15][16][17][18][19]. Compared with the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) data, the VIIRS/DNB NTL data, which are released by the National Oceanic and Atmospheric Administration (NOAA), have higher spatial resolution, wider radiation detection range and improved data quality [20,21] with the use of onboard data calibration [22].…”