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Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example

Author

Listed:
  • Yile Chen

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China)

  • Liang Zheng

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China)

  • Junxin Song

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China)

  • Linsheng Huang

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China)

  • Jianyi Zheng

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China)

Abstract

The COVID-19 pandemic has led to a re-examination of the urban space, and the field of planning and architecture is no exception. In this study, a conditional generative adversarial network (CGAN) is used to construct a method for deriving the distribution of urban texture through the distribution hotspots of the COVID-19 epidemic. At the same time, the relationship between urban form and the COVID-19 epidemic is established, so that the machine can automatically deduce and calculate the appearance of urban forms that are prone to epidemics and may have high risks, which has application value and potential in the field of planning and design. In this study, taking Macau as an example, this method was used to conduct model training, image generation, and comparison of the derivation results of different assumed epidemic distribution degrees. The implications of this study for urban planning are as follows: (1) there is a correlation between different urban forms and the distribution of epidemics, and CGAN can be used to predict urban forms with high epidemic risk; (2) large-scale buildings and high-density buildings can promote the distribution of the COVID-19 epidemic; (3) green public open spaces and squares have an inhibitory effect on the distribution of the COVID-19 epidemic; and (4) reducing the volume and density of buildings and increasing the area of green public open spaces and squares can help reduce the distribution of the COVID-19 epidemic.

Suggested Citation

  • Yile Chen & Liang Zheng & Junxin Song & Linsheng Huang & Jianyi Zheng, 2022. "Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example," Sustainability, MDPI, vol. 14(21), pages 1-31, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14341-:d:961187
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    References listed on IDEAS

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    1. Tom Rosenström & Markus Jokela & Sampsa Puttonen & Mirka Hintsanen & Laura Pulkki-Råback & Jorma S Viikari & Olli T Raitakari & Liisa Keltikangas-Järvinen, 2012. "Pairwise Measures of Causal Direction in the Epidemiology of Sleep Problems and Depression," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-13, November.
    2. David Goodman-Meza & Akos Rudas & Jeffrey N Chiang & Paul C Adamson & Joseph Ebinger & Nancy Sun & Patrick Botting & Jennifer A Fulcher & Faysal G Saab & Rachel Brook & Eleazar Eskin & Ulzee An & Misa, 2020. "A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-10, September.
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