Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example(training set data)

Published: 18 November 2022| Version 2 | DOI: 10.17632/pykbtg7wn5.2


This is a training set for machine learning.The content of this training set is related to the published paper "Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example".This research is based on the image synthesis technology of machine learning, and an urban morphology map is generated through the footprint heat map of the COVID-19 epidemic.First, the heatmap of the COVID-19 epidemic footprint is used as training set A, and the corresponding urban morphology map of Macau Peninsula is used as training set B. Then, a conditional generative adversarial network (CGAN) is implemented for training. In the image translation using training set A and training set B, the generator and the discriminator are allowed to play against each other, thus improving the quality of the generated pictures and realizing the ability to generate urban morphology maps . The experimental materials are shown in Figure 1. In the processing of the Macau map, in order to simplify the data, various elements in the map were represented in different colors and presented in the form of color pictures. In this study, five colors were used to represent the elements on the map: roads and squares are red (R = 255, G = 0, B0), green spaces are green (R = 200, G = 215, B = 158), water is blue (R = 158, G = 188, B = 216), buildings are white (R = 255, G = 255, B = 255), and land is black (R = 0, G = 0, B = 0). These five colors represent most of the content in the city map. The footprint hotspot data of the COVID-19 epidemic were generated by researchers from the statistics of the footprint report of a total of 500 patients in Macau (from mid-June to early July 2022), which was fully disclosed by the Macau Health Bureau. The addresses of the footprints were mainly registered according to the building of residence. Despite the longest residence time and the highest risk of carrying the virus, due to personal privacy, some private itineraries were not officially announced. Therefore, this study could only screen the footprints of a total of 3265 confirmed patients on the Macau Peninsula. Then, the addresses were converted into latitude and longitude coordinates using Google Maps API Web Services, before being input into ArcGIS Pro to generate hotspot data. Since CGAN requires paired datasets for training, in order to make the data correspond one-to-one, they were uniformly corrected into the Observatorio Meteorologico 1965 Macau Grid.


Steps to reproduce

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.


Macau University of Science and Technology


Machine Learning, Urban Form Model, Sustainable Urban Design, Macao, COVID-19


Specialized Subsidy Scheme for Higher Education Fund of the Macau SAR Government in the Area of Research in Humanities and Social Sciences (and Specialized Subsidy Scheme for Prevention and Response to Major Infectious Diseases)

No. HSS-MUST-2020-09