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Many countries have started to reopen economic activities after the COVID-19 pandemic; however, due to the disease’s long incubation period and high infectivity, social distancing remains an essential measure despite the start of vaccinations. It is therefore necessary to understand the changes in human mobility from the outbreak’s initiation to deceleration to control the disease’s transmission and revitalize the economy. This study suggests a methodology for investigating the changes in human mobility and their influencing factors using a case study of Seoul Metropolitan City, Korea. First, the changing patterns of human mobility were investigated based on mobile big data, including the acceleration and deceleration stages. The results showed that it varied by area and travel distance. Second, we clarified the influence of sociodemographic factors such as employee density and land use proportion on the change pattern of human mobility by applying a machine learning method. This finding implies that the effectiveness of policies such as social distancing can vary with sociodemographic factors. For example, areas with more real estate, public administration, and health care employees showed rapid recovery and faced a transmission risk by reopening economic activities. The suggested methodology can help understand human mobility and explore exit strategies.

Human Mobility Change Pattern and Influencing Factors during COVID-19, from the Outbreak to the Deceleration Stage: A Study of Seoul Metropolitan City