We present a dynamic approach for revealing the underlying information in locally cluttered areas within a geo-spatial connected graph while maintaining global edge trends. Two time series data-flow visualization approaches at both local and global scales are proposed respectively: a stream model focuses on data flows within the local area while a hub model addresses the relations between groups of nodes across the graph. The computational complexity and quantitative performance analysis on three different datasets were conducted to examine the scalability of the visualization model. The simulation results show that the central algorithms in our approach are able to achieve acceptable performance in real world test cases. Finally, our model’s effectiveness is demonstrated by two significant case studies in different application fields.

中文翻译：

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在地理空间网络可视化中呈现本地和全局信息的动态方法

我们提出了一种动态方法，用于揭示地理空间连接图中局部混乱区域的基础信息，同时保持全球边缘趋势。分别提出了两种在本地和全局范围内的时间序列数据流可视化方法：流模型专注于局部区域内的数据流，而集线器模型解决了整个图上节点组之间的关系。在三个不同的数据集上进行了计算复杂性和定量性能分析，以检查可视化模型的可伸缩性。仿真结果表明，我们的方法中的中心算法能够在现实世界的测试案​​例中实现可接受的性能。最后，通过在不同应用领域的两个重要案例研究证明了我们模型的有效性。