Sayaka Yagi’s research while affiliated with Ochanomizu University and other places

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.

We commonly represent time-varying values as polyline charts or heatmaps; however, both type of techniques are difficult to simultaneously observe short-term features of time-varying values and cluster transitions. This poster proposes storyline-based visualization technique for consecutive numerical time-varying data. Storyline is a visualization technique to show associative feature among elements over time. Our technique measures similarity of each elements and draw similar elements as proximity storyline. The technique also reflects differential values on storyline as a visual variable to emphasize the amount of line changes.

Most time-varying data in our daily life is multi-variate. Moreover, most of such time-varying data contains both numeric and categorical values. It is often meaningful to visualize both of them as they are often correlated. We aim to visualize every value in such time-varying data in a single display space so that we can discover interesting relationships among the values of the time-varying data. This paper presents a heat map-based time-varying data visualization technique which displays both numeric and categorical values in a single display space. The technique assigns time to the horizontal axis of the display space, and vertically arranges the series of colored belts corresponding to the time-sequence values. It generates one belt for a numeric value, and multiple belts for a categorical value. It clusters the belts according to the similarity of color sequences, and re-arranges the belts based on the clustering result. This paper shows an example of the visualization result applying a time-varying multi-variate marketing dataset.

We have various interesting time-varying data in our daily life, such as weather data (e.g., temperature and air pressure) and stock prices. Such time-varying data is often associated with other information: for example, temperatures can be associated with weather, and stock prices can be associated with social or economic incidents. Meanwhile, we often draw large-scale time-varying data by multiple polylines in one space to compare the time variation of multiple values. We think it should be interesting if such time-varying data is effectively visualized with their associated information. This paper presents a technique for polyline-based visualization and level-of-detail control of tagged time-varying data. Supposing the associated information is attached as tags of the time-varying values, the technique generates clusters of the time-varying values grouped by the tags, and selects representative values for each cluster, as a preprocessing. The technique then draws the representative values as polylines. It also provides a user interface so that users can interactively select interesting representatives, and explore the values which belong to the clusters of the representatives.