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

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.

本数据集为机器学习训练集，其研究内容与已发表论文《基于机器学习揭示城市形态对COVID-19的影响——以澳门为例》相关。本研究依托机器学习图像合成技术，通过COVID-19疫情足迹热力图生成城市形态图谱。首先将COVID-19疫情足迹热力图作为训练集A，以澳门半岛对应城市形态图谱作为训练集B，随后搭建条件生成式对抗网络（Conditional Generative Adversarial Network, CGAN）开展训练。在利用训练集A与训练集B进行图像翻译的过程中，通过让生成器与判别器互相博弈，提升生成图像的质量，实现城市形态图谱的生成能力。实验材料如图1所示。在澳门地图的处理环节中，为简化数据，地图内各类要素以不同色彩区分并以彩色图像形式呈现。本研究采用五种色彩表征地图要素：道路与广场为红色（R=255, G=0, B=0），绿地为绿色（R=200, G=215, B=158），水体为蓝色（R=158, G=188, B=216），建筑物为白色（R=255, G=255, B=255），陆地为黑色（R=0, G=0, B=0）。上述五种色彩覆盖城市地图的绝大多数内容。COVID-19疫情足迹热点数据由研究人员基于澳门卫生局完整公开的2022年6月中旬至7月初共500名患者的足迹报告统计生成，足迹地址主要以居住楼宇进行登记。尽管患者居所为停留时间最长、病毒携带风险最高的场所，但出于个人隐私保护，部分私人行程未被官方披露，因此本研究仅筛选出澳门半岛共计3265名确诊患者的足迹信息。随后通过Google Maps API Web Services将地址转换为经纬度坐标，再输入ArcGIS Pro生成热点数据。由于CGAN需要配对数据集开展训练，为实现数据一一对应，所有数据均统一校正至澳门气象观测局1965年澳门网格（Observatorio Meteorologico 1965 Macau Grid）。