A Storyline-based Visualization Technique for Consecutive Numerical Time-varying Data

Abstract

Information visualization is an effective approach to analyze time-varying data in our daily lives. We commonly represent time-varying values applying polyline charts or heatmaps; however, it is difficult to simultaneously observe short-term features of time-varying values and cluster transitions while applying either polyline charts or heatmaps. This paper proposes a storyline-based visualization technique for consecutive numerical time-varying data. Storyline is a visualization technique to show associative features among elements over time. Our technique first measures similarity of elements in each time-step, and divides the elements into clusters. The technique then defines the cluster layout by matching corresponding clusters between two adjacent time-steps, and draws similar elements as proximity storyline. Reflecting transparency on storyline as a visual variable, the technique also emphasizes the amount of line changes. Moreover, the technique provides a user interface so that users can interactively select interesting parts on storyline, and explore the numerical values by observing a polyline-based visualization. We believe it is important to focus on elements which switch their clusters frequently. We suppose that by making the appearances of numerical changes prominent based on the amount of changes, a user would be able to effortlessly pay his/her attention to where those changes occurred. This easy recognition of numerical changes would lead to further focused investigation on the causality through examination of the original numerical values and other associated information.