animation

Visualization techniques for time-oriented data can be divided into two main categories: dynamic and static visualizations. Dynamic visualizations map the time directly to the time and present the data of different time-points or -intervals successively.

This means the visualization changes its appearance over time. Compared to that, in static visualizations the factor time is mapped to space (e.g. a line chart where the time is projected to the x-axis). Static visualizations can be further differentiated in diagrams which present only states or diagrams also visualizing the development by e.g. some kind of arrows or lines. The aim of this thesis was an empirical comparison between a dynamic and two static visualizations which are suitable for analyzing relationships of two variables and their development over time. The chosen dynamic visualization presents the development of the data with an animated scatter plot. Within the static visualizations ("Small Multiples" and "Trace view") the time intervals and the data recorded in those time intervals are displayed in small pictures arranged side by side. Additionally data points belonging to an object are connected by lines in the "Trace view". Those connections between the data points are continued over all small pictures and therefore visualize the whole development of the parameters explicitly. To make the comparison possible it was necessary to extend the prototype TimeRider by the "Small Multiple" visualization with the possibility to activate the traces resulting in the previously described "Trace view". The quantitative study was carried out over the internet, where each of the 29 participants had to solve tasks with all visualizations. The tasks covered three topics: the examination of trends, the identification and analysis of outliers as well as the identification of clusters and their development over time. The analysis of the results showed that all visualizations caused similar success rates and completion times for tasks concerning outliers and clusters. Regarding data movements within clusters and small changes in the data the animation outperformed the other visualizations significantly in correctness. The results also showed that participants solved tasks concerning the analysis of specific data points and the comparison between them significantly faster and with higher success rates with the dynamic visualization. However for nearly every hypothesis the results were dataset-dependent and had to be analyzed split by dataset which reduces the significance of the statistical results. But allover the results indicate that animation is better or at least equal to the two static visualizations for the analysis of time-oriented data.