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. INTRODUCTION: TREND VISUALIZATIONInformally, 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 ...

Many factors can shape the flow of visual data‐driven stories, and thereby the way readers experience those stories. Through the analysis of 80 existing stories found on popular websites, we systematically investigate and identify seven characteristics of these stories, which we name “flow‐factors,” and we illustrate how they feed into the broader concept of “visual narrative flow.” These flow‐factors are navigation input, level of control, navigation progress, story layout, role of visualization, story progression, and navigation feedback. We also describe a series of studies we conducted, which shed initial light on how different visual narrative flows impact the reading experience. We report on two exploratory studies, in which we gathered reactions and preferences of readers for stepper‐ vs. scroller‐driven flows. We then report on a crowdsourced study with 240 participants, in which we explore the effect of the combination of different flow‐factors on readers’ engagement. Our results indicate that visuals and navigation feedback (e.g., static vs. animated transitions) have an impact on readers’ engagement, while level of control (e.g., discrete vs. continuous) may not.