During the epidemics of COVID-19, not only China but the whole world is experiencing a serious crisis on public health and economy , and the epidemics have become pandemic. As estimating the overall loss and the effectiveness of preventive measures in China is a complex task, we try to reveal the behavior change of people over time & space in order to infer the impact on daily life of and overall economy. Spatiotemporal behavior patterns can be reflected by share bike usage because great majority of Chinese urban residents use share bikes to connect public transports and both ends of travel. Taking records of Bike Sharing Systems (BSSs) in Beijing, this paper characterizes not only temporal behavior patterns of share bike users: during ordinary days, holidays and the pandemic, but also spatial behavior patterns: around malls, metros, normal communities and infected communities, etc. This paper illustrates the evolution of share bikes usage during the pandemic and discover the critical time places showing the significant impact on the productive and social activities. The association of public health interventions with improved control of the COVID-19 outbreak was extrapolated by conducting co-location analysis temporally . These findings may provide critical information on how to respond to  other epidemic events.
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.
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not only China but
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, and the epidemics have become pandemic. As estimating the overall loss and the effectiveness of preventive measures
. 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)
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a complex task, we try to reveal the behavior change of people
capable of characterizing the behavior patterns
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in order to infer the impact on daily life of and overall economy. Spatiotemporal behavior patterns can be reflected by share bike usage because great majority of Chinese urban residents use share bikes to connect public transports and both ends of travel. Taking records of Bike Sharing Systems (BSSs)
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
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this paper characterizes not only temporal behavior patterns of share bike users: during ordinary days, holidays and the pandemic, but also spatial behavior patterns: around malls, metros, normal communities and infected communities, etc. This paper illustrates the evolution of share bikes usage during the pandemic
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,
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discover the critical time
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places showing the significant impact on the
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
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The association of public health interventions with improved control of
Discussions with evolving confirmed COVID-19 cases suggest the effectiveness and necessity of the public interventions in
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outbreak was extrapolated by conducting co-location analysis temporally
containment during the resilience period
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may
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the current COVID-19 outbreak and