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.

中文翻译：

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大规模时空数据的可视化级联分析


许多时空事件都可以被视为传染。这些事件通过遵循级联模式、扩大其影响并生成涉及多个位置的事件级联，隐式地跨空间和时间传播。分析此类级联过程对各种城市应用（例如交通规划和污染诊断）具有重要意义。由于现有方法在挖掘和解释级联模式方面能力有限，我们提出了一种称为 VisCas 的可视化分析系统。 VisCas 将推理模型与交互式可视化相结合，使分析师能够推断和解释时空环境中的潜在级联模式。为了开发 VisCas，我们解决了三个主要挑战：1）广义模式推理； 2）隐性影响可视化； 3）多方面级联分析。对于第一个挑战，我们将最先进的级联网络推理技术应用于一般城市场景，可以从大规模时空数据中可靠地推断出级联模式。针对第二个和第三个挑战，我们组装了一套有效的可视化来支持位置导航、影响力检查和级联探索，并促进深入的级联分析。我们设计了一种基于三重优化策略的新颖影响视图，用于分析推断模式的隐式影响。我们与领域专家一起对现实世界的交通拥堵和空气污染数据集进行了两个案例研究，展示了 VisCas 的功能和有效性。