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Analysis of Spatiotemporal Characteristics and Influencing Factors for the Aid Events of COVID-19 Based on GDELT

Author

Listed:
  • Yunxing Yao

    (School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Yinbao Zhang

    (School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Jianzhong Liu

    (School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Yanpei Li

    (School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Xiaopei Li

    (School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China)

Abstract

The uncertainty of COVID-19 and the spatial inequality of anti-pandemic materials have made international aid an important means for many countries to cope with this global public health crisis. It is of far-reaching significance to analyze the spatiotemporal characteristics and influencing factors of international aid events for the global joint fight against COVID-19 and the sustainability of global public health business. The data on aid events from 23 January 2020 to 31 October 2021, were from the GDELT database. China, the United States, the United Kingdom, and Canada were selected as the study objects because they provided more aid. Their spatiotemporal characteristics of main aid flows, the response characteristics of the aid requests, and the characteristics of verbal aid to cash in were studied using spatial statistical analysis methods. The influencing factors of aid allocation also were studied by regression analysis. The results found that: the international aid flow of each country was consistent in spatial distribution, mainly to countries with severe pandemics and neighboring countries. However, there were differences in the recipients. China mainly aided developing countries, while the United States, the United Kingdom, and Canada mainly aided developed countries. Relatively speaking, China was more responsive to aid requests and more aggressive in cashing in on verbal aid. The countries considered the impact of their economic interests when they planned to aid. At the same time, there were obvious “bandwagon effect” and “small country tendency” on the aid events.

Suggested Citation

  • Yunxing Yao & Yinbao Zhang & Jianzhong Liu & Yanpei Li & Xiaopei Li, 2022. "Analysis of Spatiotemporal Characteristics and Influencing Factors for the Aid Events of COVID-19 Based on GDELT," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12522-:d:930893
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    References listed on IDEAS

    as
    1. Fengcai Qiao & Pei Li & Xin Zhang & Zhaoyun Ding & Jiajun Cheng & Hui Wang, 2017. "Predicting Social Unrest Events with Hidden Markov Models Using GDELT," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-13, May.
    2. Marko Gregl Klavdij Logožar, 2017. "The Impact of Development Aid on the International Migrations in the African, Caribbean, and Pacific Group of States," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 20(1), pages 101-112, May.
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    Cited by:

    1. Chao Wang & Zhijun Li & Haifeng Chen & Meibo Wang, 2023. "Comprehensive Evaluation of Agricultural Water Resources’ Carrying Capacity in Anhui Province Based on an Improved TOPSIS Model," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    2. Innocensia Owuor & Hartwig H. Hochmair, 2023. "Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions," Geographies, MDPI, vol. 3(3), pages 1-26, September.

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