The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels

Abstract

Abstract:Nighttime light data offer a unique view of the Earth's surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area.

Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

“…It can be concluded that at the provincial level, all of the three models are reliable for modeling the long-term GDP dynamics using the extended temporal coverage data. In general, the power function model is the best-fitting model, with the minimum mean MARE value of 14.91%, which is supported by previous studies [17,26]. To further ascertain the most effective provincial-level models, the optimal models for each province were chosen through a comparison of the R 2 and MARE values of each model ( Figure 5).…”

“…Most of the previous studies that modeled the dynamics of the GDP or other socioeconomic parameters using nighttime light data were based on the construction of statistical relationships between TNL and a particular socioeconomic parameter [13,26,37,40,[43][44][45]. By examining the scattergram composed of TNL from the DMSP-OLS and NPP-VIIRS data at the provincial level of China from 2012 to 2013, which is the overlap period when the nighttime light data were collected by both sensors, we were able to observe sharply defined diagonal clusters of points ( Figure 1a).…”

“…Several studies have been carried out to generate a comparison between GDP modeling using DMSP-OLS data and modeling using NPP-VIIRS data [5,25,26]. However, few methods have been proposed to integrate the DMSP-OLS and NPP-VIIRS data to construct a consistent model, which is of great importance to understand the long-term economic development in China.…”

“…However, few methods have been proposed to integrate the DMSP-OLS and NPP-VIIRS data to construct a consistent model, which is of great importance to understand the long-term economic development in China. Furthermore, most of the previous studies have attempted to model GDP with nighttime light data using a single function model at all spatial levels [5,13,15,16,25] or using different function models at different spatial levels [17,26]. With an area of 9.6 million square kilometers, China has substantial regional variation with regard to natural environments and socioeconomic development patterns [27], so the quantitative relationship between nighttime lights and GDP also varies across different regions of China [15,24].…”

Regional Inequality in China Based on NPP-VIIRS Night-Time Light Imagery

“…Li et al [31] proposed a simple and approximate process for removing the confounding factors by using DMSP-OLS data. This method has been recognized by many scholars [13,27,30,42]. However, the spatial resolution of DMSP-OLS data and NPP-VIIRS data are quite different.…”