Storytelling and Visualization

Gonzalez and Cleotilde define animation as a series of varying images presented dynamically according to user actions, in ways that help the user to perceive a continuous change over time and develop a more appropriate mental model of the task. The results of their study show that decision making performance is highly contingent on the properties of the animation user interface such as image realism, transition smoothness, and interactivity style, and also sensitive to the task domain and the user's experience. Values of accuracy, time, ease of use, and enjoyability for the two types of images, transitions, and interactivity styles indicated that realistic images, gradual transitions, and parallel interactivity produced better decisions. Decision making accuracy, time, ease of use, and enjoyability in animated interfaces are influenced by the form of image representation, the transition effects, and the form of interactivity. This research supports the idea that to be an effective decision support tool, animation must be smooth, simple, interactive, and explicitly account for the appropriateness of the user's mental model of the task. Gonzalez and Cleotilde review selected empirical investigations from the literature in education, psychology, and HCI which suggest that animation may make interfaces easier, more enjoyable and understandable, and study the effect of animation on decision making.

Animated Transitions for Linear Storytelling

The literature in this sub-section focuses on animated transitions using automatic, or semi-automatic approaches (as opposed to interactive techniques to animated transitions).

Heer and Robertson investigate the effectiveness of animated transitions in traditional statistical data graphs, such as bar charts, pie charts, and scatter plots. A visualisation framework called DynaVis is created to test the effectiveness of animation on the user's preference and information retention. Graph animations are used to keep viewers engaged and to promote creative thinking about the data. See Figure 27.

Figure 27. Heer and Robertson show the process of transition for a scatter plot to a bar chart. The top path starts by stretching the points to size and then moving to the right location, whereas the bottom path moves the dots first, then resizes and reshapes them.

The software displays animated transitions of statistical data graphs. Sorting and filtering animation provide the user insight into the composition of the data. All transitions take place over a time frame rather than instantaneously so the user can see exactly how the visualisation has changed. Animations between different graph types are implemented by morphing the data from one shape and size to another. Statistically significant differences in user preference were found between static graphs and animated graphs. Animated transitions can improve graphical perception. This is reflected in the findings of the user experiments testing recall and understanding. However, not all transition scenarios are found to be significantly different.

Heer and Robertson is based on the previous work of Bederson and Boltman  but builds upon it by testing different transitional events.

Animated Transitions for User-Directed and Interactive Storytelling

The literature in this subsection focuses on interactive, user-driven transitions. The user or users create animated transitions interactively (as opposed to automatically as in the previous section). Bederson and Boltman examine how animating a viewpoint change in a spatial information system affects a user's ability to build a mental map of the information in the space. Based on a user-study involving a spatial map of a family tree, animation is found to improve subjects' ability to learn the spatial position of family members within the tree without a speed penalty.

Two different family trees of nine individuals are presented to two groups people with animation and without animation. The subjects were given three kinds of tasks; navigation of family trees, exploratory family trees, and reconstruction of family trees. The speed and accuracy of performance are recorded. In this experiment, there is a statistically significant improvement in accuracy of the reconstruction task over that of other tasks. Animation resulted in fewer task errors. See Figure 28.

Figure 28. Bederson and Boltman show the ordering effects when presenting an animated and non-animated graphic. If the animated graphic is shown first then there is little difference in recall error, however, if the animation graphic is shown second then the recall error is significantly higher for the non-animated graphic. Image courtesy of Bederson and Boltman.

Bederson and Boltman is based on Gonzalez and Donskoy and Kaptelinin which address the relationship between animation and users' understanding. Compared to previous work, Bederson and Boltman focus on animation of the viewpoint. The design of the experiment is to change from a single in-between frame to several in-between frames.

Akiba et al. introduce an animation tool named: AniVis for scientific visualization exploration and communication. This tool can turn the results of data exploration and visualization into animation content and the users can create a complex animation sequence by combining several simple effects.

Parameter-space blending operator creates intermediate frames between two instances of frames I₁ and I₂ by interpolating their respective parameters. If I₁ and I₂ do not overlap in time, they generate intermediate frames by interpolating the parameters of the last frame of I₁ and the first frame of I₂. Otherwise, they generate intermediate frames by interpolating the parameters of their corresponding frames.

An image-space blending operator creates the animation content between I₁
and I₂ by interpolating their respective image frames. Similarly to parameter-space blending, if I₁ and I₂ don't overlap in time, they generate intermediate frames by blending the last frame of I₁ and the first frame of I₂. The effect is that the last frame of I₁ gradually fades out as the first frame of I₂ gradually fades in. If I₁ and I₂ overlap, they generate intermediate frames by blending.

A playback operator lets users repeatedly loop through one or more consecutive instances of interest.

A MRI head data case study focuses on highlighting a brain tumor. The animation is comprised of four pieces of dynamic content. The first is a spatial overview that rotates the volume data 360 degrees along the y-axis. The second piece is a spatial exploration in which the user customizes the view. The third is a parameter-space blending between a spatial exploration and a slicing, which reveals a tumor's inner structure. The parameter-space blending highlights a tumor by varying the opacity while zooming in on the region of interest. See Figure 29. A hurricane data case study has five components. The first is a caption showing the animation's content, blended with a spatial exploration that zooms in on the data. The second piece is a temporal exploration to show early time steps. The third is a variable overview that browses through three data attributes: vapor, wind speed, and cloud. The fourth piece is a temporal exploration to show later time steps. The fifth is a spatial exploration that zooms in on the hurricane's eye.

Figure 29. Akiba et al. show the AniVis animation tool displaying MRI scan data. By blending the two layers of data together, a new layer of information is revealed (middle image).

Akiba et al. is based on previous animation support and an animation enhanced system and develops template-based visualization tools for animation.

To explore the challenge of gradually moving from interest to insight, Nagel et al. propose the term staged analysis. Invoking temporal and theatrical notions, they define staged analysis as a carefully choreographed process of breaking up a complex whole into its component parts and purposefully preparing the manner of their appearance. In the context of visualization, the concept of staging typically refers to animated transitions broken up to be more easily observed. They build on top of this notion of staging and extend it to a guided analysis process.

As we can see, the literature on transitions is spread amongst information and scientific visualization. Table 2 shows an alternative classification of the literature divided up into information, scientific, and geo-spatial visualization. We can see that most of the storytelling research focuses on information visualization.