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An Innovative Index for Evaluating Urban Vulnerability on Pandemic Using LambdaMART Algorithm

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  • Yuming Lin

    (Division of Environmental Design, Kanazawa University, Kanazawa 920-1192, Japan)

  • Zhenjiang Shen

    (Division of Environmental Design, Kanazawa University, Kanazawa 920-1192, Japan)

Abstract

The COVID-19 pandemic has significantly changed urban life and increased attention has been paid to the pandemic in discussions of urban vulnerability. There is a lack of methods to incorporate dynamic indicators such as urban vitality into evaluations of urban pandemic vulnerability. In this research, we use machine learning to establish an urban Pandemic Vulnerability Index (PVI) that measures the city’s vulnerability to the pandemic and takes dynamic indicators as an important aspect of this. The proposed PVI is constructed using 140 statistic variables and 10 dynamic variables, using data from 47 prefectures of Japan. Factor Analysis is used to extract factors from variables that may affect city vulnerability, and the LambdaMART algorithm is used to aggregate factors and predict vulnerability. The results show that the proposed PVI can predict the relative seriousness of the COVID-19 pandemic in two weeks with a precision of more than 0.71, which is meaningful for taking controlling measures in advance and shaping the society’s response. Further analysis revealed the key factors affecting urban pandemic vulnerability, including city size, transit station vitality, and medical facilities, emphasizing precautions for public transport systems and new planning concepts such as the compact city. This research explores the application of machine learning techniques in the indicator establishment and incorporates dynamic factors into vulnerability assessments, which contribute to improvements in urban vulnerability assessments and the planning of sustainable cities while facing the challenges of the COVID-19 pandemic.

Suggested Citation

  • Yuming Lin & Zhenjiang Shen, 2022. "An Innovative Index for Evaluating Urban Vulnerability on Pandemic Using LambdaMART Algorithm," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5053-:d:800032
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    References listed on IDEAS

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    1. Mishra, Swasti Vardhan & Gayen, Amiya & Haque, Sk. Mafizul, 2020. "COVID-19 and urban vulnerability in India," SocArXiv 523r8, Center for Open Science.
    2. Jorge Salas & Víctor Yepes, 2019. "VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    3. Peter Boettke & Benjamin Powell, 2021. "The political economy of the COVID‐19 pandemic," Southern Economic Journal, John Wiley & Sons, vol. 87(4), pages 1090-1106, April.
    4. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
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    Cited by:

    1. Selene Perazzini & Rodolfo Metulini & Maurizio Carpita, 2024. "Statistical indicators based on mobile phone and street maps data for risk management in small urban areas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1051-1078, September.

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