基于螺旋图的时间序列数据可视化

Visualization of time series data based on spiral graph

阶段性时间序列数据是一种生活中常见数据形式,它描述一个包含多个状态转换的事件,每个状态都具有时间属性,且状态之间的转换存在多条路径。针对现有的可视化方法不能有效展示各个状态之间变化以及状态间路径的时间属性信息的问题,提出一个全新的基于螺旋图的可视化模型。该模型使用一个圆环表示一个状态,一个事件的多个状态采用一组同心圆表示,状态间可达路径采用螺旋线表示,螺旋线的起始位置由其起始时间与起始状态圆环决定,终止位置由其结束时间与结束状态圆环决定。针对路径过多导致路径覆盖较为严重的问题,该模型采用基于长尾函数的透明算法,根据一条路径与其他路径的交叉数,为每条路径分配透明度。该模型还提供了路径的筛选、高亮、查看详细信息、缩放等功能,实现了灵活的交互操作。通过对中国铁路列车站点数据进行实验,实验结果表明该模型能够在有限的空间中有效地展示站点间运行任意时长的列车,并且在列车较多的情况下降低了螺旋线交叉重叠带来的杂乱度,同时有效地保留了列车的运行信息,能够为用户路线的选择提供决策支持,说明了该模型对阶段性时间序列数据可视化的有效性。

Phased time series data is common in daily life. It describes an event that contains a number of state transitions. Each state has a time attribute, and there are multiple paths between state transitions. Aiming at the problem that the existing visualization techniques are not sufficient in visualizing the transition of each phase or the time variation of paths between states, a novel visualization model based on spiral graph was proposed. In the proposed model, each state was represented by a circle and the states of an event were represented by a set of concentric circles, and the reachable paths between neighboring states were represented by spirals. The start point of each spiral depended on its start time and the start states, and the end point of each spiral depended on its end time and the end states. To solve the overlapping problem caused by large amount of paths, the transparency adjustment algorithm based on long-tailed function was applied on the paths. The transparency of each path was assigned according to the number of intersections of this path and other paths. Flexible interactive facilities such as path filtering, highlighting, bomb box and zooming were provided to support efficient data exploration. The proposed model was implemented on China railway data, the experimental result shows that the model can effectively display trains of any running duration in limited space and is able to reduce the chaos caused by paths overlapping when confronted with large amount of trains as well as keep the information of trains and provide decision support for the user route choice, which validates the effectiveness of the proposed model in visualizing phased time series data.