Abstract
This paper re-examines the relationships between night-time light (NTL) and gross domestic product (GDP), population, road networks, and carbon emissions in China and India. Two treatments are carried out to those factors and NTL, which include simple summation in each administrative region (total data), and summation normalized by region area (density data). A series of univariate regression and multiple regression experiments are conducted in different countries and at different scales, in order to find the changes in the relationship between NTL and every parameter in different situations. Several statistical metrics, such as R2, Mean Relative Error (MRE), multiple regression weight coefficient, and Pearson's correlation coefficient are given special attention. We found that GDP, as a comprehensive indicator, is more representative of NTL when the administrative region is relatively comprehensive or highly developed. However, when these regions are unbalanced or undeveloped, the representation of GDP becomes weak and other factors can have a more important influence on the multiple regression. Differences in the relationship between NTL and GDP in China and India can also be reflected in some other factors. In many cases, regression after normalization with the administrative area has a higher R2 value than the total regression. But it is highly influenced by a few highly developed regions like Beijing in China or Chandigarh in India. After the scale of the administrative region becomes fragmented, it is necessary to adjust the model to make the regression more meaningful. The relationship between NTL and carbon emissions shows obvious difference between China and India, and among provinces and counties in China, which may be caused by the different electric power generation and transmission in China and India. From these results, we can know how the NTL is reflected by GDP and other factors in different situations, and then we can make some adjustments.
Highlights
In the 1970s, the United States launched the Defense Meteorological Satellite Pro-gram (DMSP)
Many studies have discussed the relationship between night-time light (NTL) and various factors, and showed that NTL is significantly related to socioeconomic data such as gross domestic product (GDP) and population, urban development data such as road network density, urban building area, and energy consumption data such as carbon emissions [2,3,4,5,6,7,8, 19,20,21,22,23,24]
It can be seen that the population and road network play a major role in a linear multiple regression, while the importance of GDP has dropped greatly compared to Table 10
Summary
In the 1970s, the United States launched the Defense Meteorological Satellite Pro-gram (DMSP). We perform regression and multiple regression experiments on NTL and several important economic parameters in China and India at multiple scales We compare these experimental results to discuss the characteristic change in the relationship between NTL and various elements in different types of regions, and find common reasons for these changes. 2015 annual average VIIRS data in China and India This dataset is based on the national county-level population and GDP statistics, combined with the data of land use and residential land density, and followed by weighted spatialization at a resolution of 1 km. Because carbon emissions can reflect the economic structure and energy consumption, many studies have revealed that carbon emissions are one of the important factors affecting NTL [9, 20, 26]
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