城市分析
项目简介：城市分析是应用驱动的可视分析的重要领域。城市分析旨在通过可视化的方式，帮助人们基于城市数据，对城市生活的方方面面进行分析。目前，FDU-VIS对城市分析的研究包括时空、交通、事件分析等等领域。在以城市分析为主题的IEEE VAST Challenge 2021中，我们的作品分别获得一等奖和二等奖。
Project Introduction: Urban analysis is an important field of application-driven visual analytics. Urban analysis aims to help people analyze all aspects of urban life based on urban data through visualization. At present, the research of FDU-VIS on urban analysis includes space-time analysis, traffic analysis, event analysis and other fields. In IEEE VAST Challenge 2021 with the theme of urban analysis, our works won the first prize and the second prize.
相关发表
  • IEEE VAST Challenge 2021 Winner: Visual Analytics for Spatial-temporal Situation Awareness.
    Siqi Shen, Xingui Lai, Siming Chen*, Qinghong Wang , Junting Gao.
    IEEE Computer Graphics and Applications (CG&A), Accepted, 2022.
  • Semantics-Space-Time Cube: A Conceptual Framework for Systematic Analysis of Texts in Space and Time.
    Jie Li, Siming Chen, Wei Chen, Gennady Andrienko, Natalia Andrienko.
    IEEE Transactions on Visualization and Computer Graphics (TVCG), 26(4):1789 - 1806, 2020.
    | Paper | pdf (4.8M) | Video | mp4 (9.9M)
  • Contextualized Analysis of Movement Events.
    Siming Chen, Gennady Andrienko, Natalia Andrienko, Christos Doulkeridis, Athanasios Koumparos.
    in Proceedings of EuroVA'19 (Best Paper Award), Pages 49-53, Porto, Portugal, Jun.3-7, 2019.
    | Paper | pdf (4.9M) | Slides | pptx (12M)
  • COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.
    Jie Li, Siming Chen, Kang Zhang, Gennady Andrienko, Natalia Andrienko.
    IEEE Transactions on Visualization and Computer Graphics (TVCG), 25(8): 2554-2567, 2019.
    | Paper | pdf (3.8M) | Video | mp4 (16.8M)
  • Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data.
    Siming Chen, Zuchao Wang, Jie Liang, Xiaoru Yuan.
    Journal of Visual Languages and Computing (ChinaVis'16, Honorable mention for Best Paper Award), 48: 187-198, 2018.
    | Paper | pdf (4.1M) | Slides | pdf (4.1M)