Spatiotemporal Analysis of Economic Development in Sichuan Province: Insights from Night Light Data

Shenghan Liu, Xu Luo and Ruochong Zhu ()
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Shenghan Liu: Tianjin University, School of Civil Engineering
Xu Luo: Central South University, School of Mathematics and Statistics
Ruochong Zhu: Pittsburgh Institute, Sichuan University

A chapter in Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023), 2024, pp 427-436 from Springer

Abstract: Abstract Based on the night light data of 21 municipal units in Sichuan Province from 1992 to 2021, the article used the centroid, standard deviation ellipse, and spatial autocorrelation analysis methods to explore the spatiotemporal distribution characteristics of economic development in Sichuan Province, and combined multiple linear regression models to analyze related influencing factors. The study found: Overall, the urban economy of Sichuan Province presents a core-periphery structure, with provincial capital cities and some fast-developing cities becoming the core of economic growth, while some areas with poor geographical conditions and slow economic development are located on the periphery of economic development. Cities near Chengdu in the east are developing faster, while cities in the western mountainous areas are developing slowly. The economic center of Sichuan Province generally shows a stable trend with small offset, indicating that there are differences in the economic volume within the province, but the differences are gradually narrowing. Meanwhile, the standard deviation ellipse shows a relatively stable distribution pattern; From the perspective of spatial correlation, there is a clear positive correlation in the economic development of Sichuan Province. Among the positive influencing factors, GDP is the most important influencing factor, the number of primary and secondary school students is a secondary influencing factor, RMB deposits and loans of financial institutions and government expenditures are important influencing factors, the urbanization rate, the total GDP of secondary and tertiary industries are limiting factors.

Keywords: Night light data; spatial correlation mechanism method; regression factor analysis method (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-268-2_47

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