# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 5 Discussion
### Analysis of Influencing Factors  
The preprocessing of the NPP/VIIRS annual average data has a great impact on the estimated GDP, due to the many types of landforms in the world, such as volcanoes, dry riverbeds, deserts, etc., as well as the influence of human activities, such as the exploitation and burning of fossil fuels, major engineering construction, and other factors, resulting in the abnormal enhancement of night lights and the formation of noise values [28]. This paper adopts the noise masking method, taking the highest value of Shanghai, Beijing, and Guangzhou, the three most economically active and developed cities in mainland China, as the threshold, masking the abnormal values above the threshold, and smoothing the image to make it appear as a reasonable value. This method is easy to operate, the DN value obtained is relatively reasonable, and the accuracy of the function regression model is high. However, for abnormally high values in a small part of the area, such as abnormally high values that are lower than the highest threshold but higher than the economic activities of the city that do not conform to the status quo, there is no way to deal with them. There are many models for estimating the function of the GDP. By comparing the time regression model and the spatial regression model, this paper finds that the time regression model is more suitable for inverting the GDP data within the research period. Therefore, a linear regression model is used to fit the functional relationship between the MNL and GDP, and the new GDP value is calculated and spatially simulated. The results can reflect the spatial information of the GDP in the whole mainland of China and fill the gap in the GDP spatial data. However, considering the large area of mainland China and the complexity of the population, social, economic, and natural conditions, it is difficult to simulate its economic development with a fixed model at a fine spatial scale. Therefore, it is necessary to consider the differences in different research areas according to local conditions, so as to improve and enhance the accuracy of the GDP spatial model [19]. Some scholars also divide the GDP into the first (agriculture), second (industry), and third (service) industries, classify and simulate the GDP of each industry in space, and then obtain the overall GDP data according to the corresponding proportion. However, this method is currently only used for the spatialization of the GDP in cities with a smaller range, and there are certain limitations [38]. In terms of temporal and spatial change trend analysis, we used the Sen + MK trend analysis method. This method combines the Theil-Sen and Mann-Kendall methods, and is suitable for the trend analysis of multi-year time series. It takes into account both linear and nonlinear changes in time-series data, has high computational efficiency, and is insensitive to measurement errors and outliers. This method is more suitable for the spatial simulation of GDP, and is relatively rare in the literature on the spatial simulation of GDP, which is one of the innovations of this paper. NPP/VIIRS began recording NTL data in 2012, and DMSP/OLS recorded NTL data from 1992 to 2013. The two sets of data are not compatible. If a longer time-series GDP spatial simulation is to be performed, it is necessary to clarify the error sources of the two data sources and select the appropriate processing methods to make the two sets of data compatible. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **Province** & **City** & **The Area of Decline/(km2)** \\ \hline
1 & Shaanxi & Yan’an & 1411.25 \\
2 & Heilongjiang & Daqing & 530.54 \\
3 & Shaanxi & Yulin & 507.01 \\
4 & Xinjiang & Aksu & 444.25 \\
5 & Hebei & Tangshan & 400.75 \\
6 & Tianjin & Tianjin & 378.75 \\
7 & Shanxi & Xinzhou & 363.07 \\
8 & Inner Mongolia & Ordos & 303.22 \\
9 & Shanxi & Lvliang & 280.90 \\
10 & Gansu & Qingyang & 266.51 \\ \hline \hline \end{tabular}
\end{table}