# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 6 Conclusions  
This paper uses NPP/VIIRS data and takes mainland China as the research area to calculate NTL indices such as the MNL, TNL, and CNLI, and conducts temporal and spatial regression analysis with GDP-related data. The optimal fitting model is selected to estimate the GDP, and the GDP data are spatialized to produce spatial data products of China's GDP from 2013 to 2023, analyzing the temporal and spatial variation trend in China's GDP. The study found the following:  
1. In spatial regression, the correlation between the MNL and per capita GDP of the quadratic regression function model is the highest combination among the spatial regression models, with correlation coefficient values above 0.7, and the RMSE of the prediction results is between 0.91 and 1.95, with a small error; in temporal regression, the correlation between the MNL and GDP of the linear function model is the highest, with \(R^{2}\) = 0.972. Finally, this model was selected for the spatial simulation of the GDP. 2. From 2013 to 2023, the spatial distribution of the GDP in mainland China is centered on the high-value-GDP areas of provincial capitals, and continues to extend to the surrounding areas and gradually expand to the surrounding cities. The spatial connectivity between large cities continues to increase, forming multiple urban circles with high-GDP areas. The overall pattern of the GDP distribution is that the southeast coast is much larger than the northwest inland, the east is high, the middle is second, and the west is lowest. From the perspective of changes in various types of GDP areas, the area with a GDP of more than 100 million yuan has more than doubled, while the proportion of areas with an area of less than 5 million yuan has become increasingly smaller, with a decrease of 0.54% in 2023 compared with 2023. 3. Pixels with a rising GDP account for 98.56% of the total pixels, while pixels with a falling GDP account for only 0.99%. In addition, through area tabulation, it is found that the spots with a falling GDP are mainly distributed in heavy industrial cities that have developed and concentrated based on rich fossil fuel resources, such as Aksu Prefecture and Daqing City. These cities are facing industrial structure transformation and the contradiction between ecological protection and economic construction. 4. The difference between the official GDP data and the GDP obtained using NTL is small, with an RE of 0.04% to 1.95%, and the GDP data obtained using NTL are slightly higher than the official GDP data. From the perspective of provincial rankings, the ranking changes of most provinces are between \(\pm 2\), such as Jiangsu, Guangdong, Shandong, Gansu, Qinghai, and other provinces; and the rankings of a few provinces fluctuate greatly, such as Hubei, Hunan, Xinjiang, and other provinces. The reasons for the differences are related to the dominant industries, power generation capacity, etc. To sum up, NTL data can better reflect the spatial changes of the GDP, turn one-dimensional GDP point data into two-dimensional spatial continuous data, improve the readability of the GDP, and reflect the differences between regions. In the future, we will expand our research scope and time series to construct spatial product data of the GDP for Asian and even global NTL data. From the global perspective, the paper analyzes the spatial pattern of the GDP, and expects to forecast the trend in world economic development and make reference to the development orientation. Conceptualization, M.W. and Z.N. ; methodology, W.L. and M.W. ; software, W.L. ; validation, W.L., M.W. and Z.N. ; formal analysis, W.L. ; investigation, M.W. ; resources, Z.N. ; data curation, W.L. ; writing--original draft preparation, W.L. ; writing--review and editing, W.L. and M.W. ; visualization, W.L. ; supervision, M.W. and Z.N. All authors have read and agreed to the published version of the manuscript. The research was supported by the International Research Center of Big Data for Sustainable Development Goals (CBAS) (Grant No. E43Z05010T). Not applicable.