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Measuring of the COVID-19 Based on Time-Geography

Author

Listed:
  • Zhangcai Yin

    (School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Wei Huang

    (School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Shen Ying

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430070, China)

  • Panli Tang

    (School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Ziqiang Kang

    (School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Kuan Huang

    (School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract

At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy.

Suggested Citation

  • Zhangcai Yin & Wei Huang & Shen Ying & Panli Tang & Ziqiang Kang & Kuan Huang, 2021. "Measuring of the COVID-19 Based on Time-Geography," IJERPH, MDPI, vol. 18(19), pages 1-19, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:10313-:d:647176
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    References listed on IDEAS

    as
    1. Mu Li & Yunyang Shi & Wenkai Duan & Aiqi Chen & Nan Wang & Jinmin Hao, 2019. "Spatiotemporal Decoupling of Population, Economy and Construction Land Changes in Hebei Province," Sustainability, MDPI, vol. 11(23), pages 1-20, November.
    2. Li, Ran & Tong, Daoqin, 2016. "Constructing human activity spaces: A new approach incorporating complex urban activity-travel," Journal of Transport Geography, Elsevier, vol. 56(C), pages 23-35.
    3. van Wee, Bert & Witlox, Frank, 2021. "COVID-19 and its long-term effects on activity participation and travel behaviour: A multiperspective view," Journal of Transport Geography, Elsevier, vol. 95(C).
    4. Chengji Han & Guogang Wang & Yongxiang Zhang & Lili Song & Lizhi Zhu, 2020. "Analysis of the temporal and spatial evolution characteristics and influencing factors of China’s herbivorous animal husbandry industry," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-14, August.
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    Cited by:

    1. Marcin Mazur & Jerzy Bański & Wioletta Kamińska, 2024. "The Geographical Conditioning of Regional Differentiation Characterising the COVID-19 Pandemic in European Countries," IJERPH, MDPI, vol. 21(10), pages 1-20, October.
    2. Chidinma U. Iheanetu & Kelly A. Maguire & Valéria Moricová & Roman Tandlich & Sergio Alloggio, 2022. "Utilitarian Qubit, Human Geography, and Pandemic Preparedness in the 21st Century," Sustainability, MDPI, vol. 15(1), pages 1-27, December.

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