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Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic

Author

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  • Yanjun Wang

    (Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Fei Teng

    (Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Mengjie Wang

    (Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Shaochun Li

    (Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Yunhao Lin

    (Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Hengfan Cai

    (Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
    School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

Abstract

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China’s major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China’s major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R 2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China’s major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Suggested Citation

  • Yanjun Wang & Fei Teng & Mengjie Wang & Shaochun Li & Yunhao Lin & Hengfan Cai, 2022. "Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(13), pages 1-29, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:8048-:d:853008
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    References listed on IDEAS

    as
    1. Zhaoxin Dai & Yunfeng Hu & Guanhua Zhao, 2017. "The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
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    5. Abhishek Singhal & Sohini Sahu & Siddhartha Chattopadhyay & Abhijit Mukherjee & Soumendra N Bhanja, 2020. "Using night time lights to find regional inequality in India and its relationship with economic development," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.
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    Cited by:

    1. Jun Liu & Shuang Lai & Ayesha Akram Rai & Abual Hassan & Ray Tahir Mushtaq, 2023. "Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace," IJERPH, MDPI, vol. 20(5), pages 1-24, February.

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