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

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

doi:10.1016/j.jag.2022.102942

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

Int J Appl Earth Obs Geoinf. 2022 Aug:112:102942. doi: 10.1016/j.jag.2022.102942. Epub 2022 Aug 5.

Authors

Yecheng Zhang¹, Qimin Zhang², Yuxuan Zhao¹, Yunjie Deng¹, Hao Zheng³

Affiliations

¹ College of Architecture & Art, Hefei University of Technology, Hefei, China.
² School of Mechanical Engineering, Hefei University of Technology, Hefei, China.
³ Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, United States.

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.

Keywords: Coronavirus disease; Deep learning; Design improvement; Incidence prediction; Spatial risk factors.