Mechanisms influencing the factors of urban built environments and coronavirus disease 2019 at macroscopic and microscopic scales: The role of cities

Abstract

In late 2019, the coronavirus disease 2019 (COVID-19) pandemic soundlessly slinked in and swept the world, exerting a tremendous impact on lifestyles. This study investigated changes in the infection rates of COVID-19 and the urban built environment in 45 areas in Manhattan, New York, and the relationship between the factors of the urban built environment and COVID-19. COVID-19 was used as the outcome variable, which represents the situation under normal conditions vs. non-pharmacological intervention (NPI), to analyze the macroscopic (macro) and microscopic (micro) factors of the urban built environment. Computer vision was introduced to quantify the material space of urban places from street-level panoramic images of the urban streetscape. The study then extracted the microscopic factors of the urban built environment. The micro factors were composed of two parts. The first was the urban level, which was composed of urban buildings, Panoramic View Green View Index, roads, the sky, and buildings (walls). The second was the streets' green structure, which consisted of macrophanerophyte, bush, and grass. The macro factors comprised population density, traffic, and points of interest. This study analyzed correlations from multiple levels using linear regression models. It also effectively explored the relationship between the urban built environment and COVID-19 transmission and the mechanism of its influence from multiple perspectives.

A Review of Research on Urban Playability from a Social Justice Perspective

“…Although COVID-19 has spread and faded at different rates across the globe, national and regional governments have mainly employed blockades to prevent its spread [40] and have established rules of social distancing that restrict social, commercial and school life. As a result of reliable state regulation, patterns of everyday behaviour have changed, and the range of activities of urban dwellers has been greatly reduced, making them increasingly dependent on the environment around their homes, the natural urban environment and the built urban environment [41]. The multiple effects of these practices have exerted important global implications for mental health, social relations and the economy [42].…”

Exploration of street space architectural color measurement based on street view big data and deep learning—A case study of Jiefang North Road Street in Tianjin

“…Scholars and research institutions have done a lot of exploration on quantitative urban research based on street view images [10,11]. Zhang used Google Street View in combination with various open source data to identify mechanisms that affect the built environment of cities [12], Han used Google Street View images combined with machine learning techniques to make predictions about the perception of stress in city streets [13], Wang explored different perception conditions of urban streets using Baidu Street View images combined with spatial syntax [14], Yao explores six different perceptions of urban street conditions by combining street view images with human-machine adversarial models [7]. Using big data and deep learning technology of street view images has overcome the difficulties of using traditional data for street space research to a certain extent, and has led to changes in the perspective, conditions, scales, and methods for street research [15][16][17][18][19].…”