Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

11 Scopus Citations
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Author(s)

  • Yecheng Zhang
  • Qimin Zhang
  • Yuxuan Zhao
  • Yunjie Deng
  • Hao Zheng

Detail(s)

Original languageEnglish
Article number102942
Journal / PublicationInternational Journal of Applied Earth Observation and Geoinformation
Volume112
Online published5 Aug 2022
Publication statusPublished - Aug 2022
Externally publishedYes

Link(s)

Abstract

From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.

Research Area(s)

  • Coronavirus disease, Spatial risk factors, Deep learning, Incidence prediction, Design improvement

Citation Format(s)

Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic. / Zhang, Yecheng; Zhang, Qimin; Zhao, Yuxuan et al.
In: International Journal of Applied Earth Observation and Geoinformation, Vol. 112, 102942, 08.2022.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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