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Effectiveness of Animation in Trend Visualization
George Robertson, Roland Fernandez, Danyel Fisher, Bongshin Lee, and John Stasko
Abstract— Animation 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 Terms—Information visualization, animation, trends, design, experiment.
1 INTRODUCTION: TREND VISUALIZATION
Informally, the term trend means to have a general tendency
(Webster’s Dictionary). A trend in data is an observed general
tendency. The most common way to see a trend in data is to plot a
variable’s 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.
2 M
ULTI-DIMENSIONAL TRENDS: GAPMINDER TRENDALYZER
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, Rwanda’s 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
• George Robertson, Roland Fernandez, Danyel Fisher, Bongshin Lee are
with Microsoft Research, E-Mail: {ggr, rfernand, danyelf,
bongshin}@microsoft.com.
• John Stasko is with Georgia Institute of Technology, E-Mail:
stasko@cc.gatech.edu.
Manuscript received 31 March 2008; accepted 1 August 2008; posted online
27 October 2008. For information on obtaining reprints of this article, please
send e-mail to: tvcg@computer.org.