Decision Support for Smart City

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    Social Activity Networks Shaping St. Petersburg
    ( 2021-01-05) Landsman, David ; Kats, Philipp ; Nenko, Aleksandra ; Kudinov, Sergei ; Sobolevsky, Stanislav
    Cities are complex systems, and understanding their structure is critical for multiple applications. However, traditional urban planning is challenged by the dynamics of the urban system. Fortunately, in recent years, multiple datasets reflecting human activity in nearly real-time have become available. This paper leverages geo-tagged data from VKontakte, Google Places social media and Nash Petersburg urban issue-reporting portal for building a multi-layered social activity network and revealing the structure of the city through the community structure in this network. The ability of this structure to capture meaningful socio-economic patterns across the city is evaluated. Results will aid urban, transportation, infrastructural planning, policy-making, real estate and socio-economic development initiatives.
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    Investigating user satisfaction of university online learning courses during the COVID-19 epidemic period
    ( 2021-01-05) Zuo, Yiting ; Cheng, Xusen ; Bao, Ying ; Zarifis, Alex
    Online learning has been expanding for some time but the forced move to it due to the outbreak of COVID-19 has created new issues. This study set out to investigate the impact mechanism of online learning user satisfaction from the perspective of cognitive load in the era of COVID-19 and explore ways to optimize cognitive load in teaching practice. Semi-structured interviews were conducted for the empirical analysis. The coding process of the interviews yielded several antecedents of cognitive load in the online learning process. We also proposed a theoretical model based on the literature review and data analysis. Findings of the qualitative analysis indicate that the antecedents of cognitive load are multi-dimensional and the user's satisfaction with the online learning platform mainly consists of the expected confirmation of the information system and the perceived usefulness. These findings can help us think backward about optimizing user satisfaction with online learning in the context of COVID-19 breakout.
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    Case Study on Dynamic Scenario Model for Smart City
    ( 2021-01-05) Zhang, Lin ; Liu, Cheng ; Yan, Qiang ; Lu, Junqiang ; Qu, Li
    Disaster resilience is important for a smart city. Analyzing disaster cases within a scenario-based analytical framework provides an effective way to acquire specific experience in disaster prevention. This study proposes a dynamic scenario model, which consists of developing scenario representation and developing scenario sequence. Developing scenario representation provides a normalized representation framework for disaster scenarios. Meanwhile, how to make the representation contributes to severity evaluation is discussed. Developing scenario sequence establishes dependencies among disaster scenarios to show the whole disaster evolution of disaster cases. A disaster case of a crude oil tank is taken as an example to give a better understanding of the dynamic scenario model. The result shows that the dynamic scenario model can establish a more structurally and normalized representation for disaster scenarios. Furthermore, the model also provides an intuitive way for evaluation of disaster consequence severity, which helps in improving the situation awareness of the disaster.
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    Introduction to the Minitrack on Decision Support for Smart City
    ( 2021-01-05) Xu, Wei ; Ma, Jian ; Sun, Jianshan