AbstractAnimation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate. Index TermsInformation visualization, animation, trends, design, experiment. Informally, the term trend means to have a general tendency (Websters Dictionary). A trend in data is an observed general tendency. The most common way to see a trend in data is to plot a variables change over time on a line chart or bar chart. If there is a general increase or decrease over time, this is perceived as a trend up or down. If there is a general increase/decrease that reverses direction, it is perceived as a reversing trend (for up to a few reversals). If there are more than a few reversals, it appears to be cyclic or noisy data, and no trend is perceived. Plotting multiple variables on a timeline (as in a multiple line chart) sometimes allows the user to see counter-trends. For example, if most of the variables are generally increasing and a few are decreasing, the decreasing variables can pop out and be perceived as counter-trends. If there is not much variation for any variable, it is possible to fit a regression line or curve and plot it as a trend line or trend curve. More formally, trend estimation is a statistical technique for identifying these trend lines or trend curves [5] . For purposes of discussion in this paper, we will focus only on informal trends that can be perceived visually without statistical trend estimation. The simple approach described above only works for a number of variables along one dimension plotted against another dimension (usually time). What is the best way to see trends in two or three dimensions simultaneously? Gapminder Trendalyzer [8] is an animated bubble chart designed to show trends over time in three dimensions. Both the size and locations of bubbles smoothly animate as time passes. This technique appears to be very effective in presentations, where a presenter tells the observer where to focus attention. It makes the data come to life, and emphasizes the critical results of an analysis. This has been done with large screens and audiences, but is probably true even for an individual presenting results to another individual; the point is that the presenter knows what is about to happen and directs the observers attention to an area of interest. However, during analysis or data exploration, there is no presenter telling the analyst where to look. In practice, this means the analyst must replay the animation several times to identify anomalies in the trends. So, this approach may be less effective for analysis and data exploration. This paper proposes two alternatives to animated bubble charts for visualizing trends in multiple dimensions, and describes a user study that evaluates the three approaches for both presentation and analysis. We are interested in understanding how effective these visualizations are for users, both as observers of a presentation and as analysts. Gapminder Trendalyzer was created by Hans, Ola, and Anna Rosling in 2003 as a technique for using animation to illustrate trends in multi-dimensional data. Trendalyzer uses a bubble chart to show three dimensions of data, one for the X-axis, one for the Y-axis, and one for the bubble size, animated over changes in a fourth dimension (time). For example, when looking at UN statistics for various countries, the X-axis might show life expectancy, the Y-axis might show infant mortality rate, and the bubble size might show population size, with each bubble representing a country. Figure 1 shows three sample frames from an animated bubble chart similar to Gapminder Trendalyzer. The trend over time is shown as an animation over time, with the bubbles changing position and size to indicate the current data values for each country at a particular time. In the case illustrated in Figure 1 and Video Figure 1 , the animation shows a general trend for most countries to increase life expectancy while decreasing infant mortality rate. However, several anomalies pop out during the animation. For example, Rwandas life expectancy starts decreasing rapidly in 1990; this is shown in the fourth frame of Figure 1 with Rwanda highlighted. Hans Rosling used this technology to make presentations at TED (Technology, Entertainment, Design) 2006 [16] and TED 2007 [17], evoking a strong favourable response from the audiences. This technique allows the observer to see trends in the informal sense: they can observe the general direction of movement of data over time. That is, there is no formal trend estimation. This is a very dramatic way to show trends, especially in a presentation. When Hans Rosling uses it, he is telling a story about the data and at key points in the presentation primes the observer to look at a particular part of the bubble chart before some significant event occurs. The effect adds a sense of excitement to the data: the movement of the bubbles becomes a critical part of the story. Others have copied this approach. MicroStrategy has an Animated Bubble Chart [12] that adds the ability to collapse related bubbles into an aggregate bubble (e.g., show one bubble for a continent). This aggregation technique reduces clutter and occlusion, but anomalies of interest are potentially hidden from view. Report Portal has a Moving Bubble Chart [13] which adds the ability to identify which dimensions of a data cube to map to which axes. These techniques appear to work well for presentation of a modest number of data points (perhaps up to about 200), but several
Robertson et al. REF compare animation's effectiveness to that of small multiples and one other static approach for identifying trends in the evolution of Gapminder Trendalyzer [15] plots (3-dimensional data plotted on 2-D axes).
Effectiveness of Animation in Trend Visualization
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine