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It is common to observe the epidemic risk perception (ERP) and a decline in subjective well-being (SWB) in the context of public health events, such as Corona Virus Disease 2019 (COVID-19). However, there have been few studies exploring the impact of individuals’ ERP within living space on their SWB, especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic. In this paper, we conducted a study with 789 participants in urban China, measuring their ERP within living space and examining its influence on their SWB using path analysis. The results indicated that individuals’ ERP within their living space had a significant negative effect on their SWB. The density of certain types of facilities within their living space, such as bus stops, subway stations, restaurants, fast food shops, convenience shops, hospitals, and public toilets, had a significantly negative impact on their SWB, mediated by their ERP within living space. Additionally, participation in out-of-home work and other activities not only increased individuals’ ERP within living space, but also strengthened its negative effect on their SWB.

A tremendous amount of research use questionnaires to obtain individuals’ fear of crime and aggregate it to the neighborhood level to measure the spatial distribution of fear of crime. However, the cost of using questionnaires to measure the large-scale spatial distribution of fear of crime is high. The built environment is known to influence people’s perceptions, including fear of crime. This study develops a machine learning model to link built environment extracted from street view images to fear of crime obtained from questionnaires, and then applies this model to extrapolate fear of crime for neighborhoods without the questionnaires. Using massive street view images and a survey among 1,741 residents in 80 neighborhoods in Guangzhou, China, this study developed a novel systematic approach to measuring large-scale spatial fear of crime at the neighborhood level for 1,753 neighborhoods. This is the first study to measure fear of crime at the neighborhood level for a metropolitan area of nearly 20 million people. The integration of survey data and street view images provides an opportunity to develop a more effective way to measure the spatial distribution of fear of crime. This approach could be applied to map other types of perceptions at a spatial resolution of the neighborhood level.