During the epidemics of COVID-19, the whole world is experiencing a serious crisis on public health and economy. Detailed information in the behavior of residents during epidemic benefits emergency management when one tries to design intervention strategies and resilience measures. From the perspective of movements, the widely used Bike Sharing System (BSS) in China is capable of characterizing the behavior patterns over time & space within big cities . Although share bikes are playing an important role as data sources in data mining, they could be of help but are rarely reported in pandemic-related researches. Based on the share bike records in Beijing, we constructed a behavior pattern analysis framework, then analyzed the spatiotemporal behavior patterns of residents, figured out  the key time nodes of different pandemic periods, and demonstrated the variations in mobility due to the onset of the COVID-19 threat and administrative restrictions. The impact of the pandemic was assessed by performing co-location analysis between share bike usage and POIs (Point Of Interest). Classification of POIs is helpful to distinguish the behavior patterns generated by various the productive and social activities . Discussions with evolving confirmed COVID-19 cases suggest the effectiveness and necessity of the public interventions in the COVID-19 containment during the resilience period . These findings provide critical information on how to respond to the current COVID-19 outbreak and other epidemic events  .
During the epidemics of COVID-19, the whole world is experiencing a serious crisis on public health and economy. Understanding human mobility during the pandemic helps one to design intervention strategies and resilience measures. The widely used Bike Sharing System (BSS) can characterize the activities of urban dwellers over time & space in big cities but is rarely reported in epidemiological research. In this paper, we present a human mobility analyzing framework based on BSS data, which examines the spatiotemporal characteristics of share bike users, detects the key time nodes of different pandemic stages, and demonstrats the evolution of human mobility due to the onset of the COVID-19 threat and administrative restrictions. We assessed the net impact of the pandemic by using the result of co-location analysis between share bike usage and POIs (Point Of Interest). Our results show the pandemic reduced the overall bike usage by 64.8\%, then an average increase (15.9\%) in share bike usage appeared afterwards, suggesting that productive and residential activities have partially recovered but far from the ordinary days . These findings could be a reference for epidemiological researches and inform policymaking in the context of the current COVID-19 outbreak and other epidemic events at city-scale .
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Classification of POIs is helpful to distinguish the behavior patterns generated by various the productive and social activities . Discussions with evolving confirmed COVID-19 cases suggest the effectiveness and necessity of the public interventions in the COVID-19 containment during the resilience period
Our results show the pandemic reduced the overall bike usage by 64.8\%, then an average increase (15.9\%) in share bike usage appeared afterwards, suggesting that productive and residential activities have partially recovered but far from the ordinary days
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