Spatiotemporal analysis of economy in China’s primary cities affected by the COVID-19 pandemic based on remote sensing of night light

doi:10.6046/zrzyyg.2023257

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Abstract

The Corona Virus Disease 2019 (COVID-19) pandemic significantly affected China’s economy. This study investigated China’s five cities that witnessed large-scale COVID-19 outbreaks based on NPP-VIIRS night light (NTL) data. A fitting model between the NTL index and GDP statistics was established. This model can reflect the monthly economic variations, yielding the spatial distribution of GPD. Finally, this study analyzed the trend in the spatial variations of the economy in the five cities during the COVID-19 pandemic by analyzing the differences in monthly GDP density. The results indicate that the GDP predicted using the GDP spatialization based on the NTL index exhibited relatively small errors and can reflect the impacts of the COVID-19 pandemic on the urban economy in an intuitive and clear manner. Under the influence of mobility policies, the marginal areas of most of the cities experienced economic recession in the early and late stages of the pandemic, with economic growth observed in the middle stage of the pandemic. In contrast, the central areas of the cities experienced economic recession in the middle stage of the pandemic, were subjected to minor impacts in its early stage, and witnessed a rapid economic recovery in its late stage. Additionally, the economy in the central areas of the cities was more resistant to the impacts of the pandemic than that in their marginal areas.

Keywords night light index COVID-19 major cities in China GDP spatialization spatiotemporal economic changes

ZTFLH:	TP79
	P237

Issue Date: 17 February 2025

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	Ruikai LI
	Zongze ZHAO
	Xiaojie TANG
	Jiayun ZHANG
	Guan WANG
	Lijuan ZHANG

Cite this article:

Ruikai LI,Zongze ZHAO,Xiaojie TANG, et al. Spatiotemporal analysis of economy in China’s primary cities affected by the COVID-19 pandemic based on remote sensing of night light[J]. Remote Sensing for Natural Resources, 2025, 37(1): 243-251.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023257 OR https://www.gtzyyg.com/EN/Y2025/V37/I1/243

Tab.1 Research cities and time periods

Tab.2 Data sources

Fig.1-1 NTL images of the study areas

Fig.1-2 NTL images of the study areas

Tab.3 The spatial model of GDP in the study area

Tab.4 Relative error of GDP forecast values in the study area (%)

Fig.2 GDP density map in the study area

Tab.5 Accuracy verification of GDP spatialization

Fig.3 Changes of economic space in Wuhan

Fig.4 GDP trend of Wuhan districts and counties

Fig.5 Changes of economic space in Nanjing

Fig.6 GDP Trend of Nanjing districts and counties

Fig.7 Changes of economic space in Xi’an

Fig.8 GDP trend of Xi’an districts and counties

Fig.9 Changes of economic space in Changchun

Fig.10 GDP trend of Changchun districts and counties

Fig.11 Changes of economic space in Shanghai

Fig.12 GDP trend of Shanghai districts and counties

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