Visual Cascade Analytics of Large-scale Spatiotemporal Data

Zikun Deng, Di Weng, Yuxuan Liang, Jie Bao, Yu Zheng, T. Schreck, Mingliang Xu, Yingcai Wu

Research output: Contribution to journalArticlepeer-review


Many spatiotemporal events can be viewed as contagions. These events implicitly propagate across space and time by following cascading patterns, expanding their influence, and generating event cascades that involve multiple locations. Analyzing such cascading processes presents valuable implications in various urban applications, such as traffic planning and pollution diagnostics. Motivated by the limited capability of the existing approaches in mining and interpreting cascading patterns, we propose a visual analytics system called VisCas. VisCas combines an inference model with interactive visualizations and empowers analysts to infer and interpret the latent cascading patterns in the spatiotemporal context. To develop VisCas, we address three major challenges, 1) generalized pattern inference, 2) implicit influence visualization, and 3) multifaceted cascade analysis. For the first challenge, we adapt the state-of-the-art cascading network inference technique to general urban scenarios, where cascading patterns can be reliably inferred from large-scale spatiotemporal data. For the second and third challenges, we assemble a set of effective visualizations to support location navigation, influence inspection, and cascading exploration, and facilitate the in-depth cascade analysis. We design a novel influence view based on a three-fold optimization strategy for analyzing the implicit influences of the inferred patterns. We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts.

Original languageEnglish
Pages (from-to)2486-2499
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number6
Publication statusPublished - 1 Jun 2022


  • spatiotemporal phenomena
  • air pollution
  • probabilistic logic
  • adaptation models
  • visual analytics
  • time series analysis
  • sensors
  • pattern mining
  • spatiotemporal data
  • Spatial cascade

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Fields of Expertise

  • Information, Communication & Computing

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