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 ...
Unit visualizations are a family of visualizations where every data item is represented by a unique visual mark-a visual unit-during visual encoding. For certain datasets and tasks, unit visualizations can provide more information, better match the user's mental model, and enable novel interactions compared to traditional aggregated visualizations. Current visualization grammars cannot fully describe the unit visualization family. In this paper, we characterize the design space of unit visualizations to derive a grammar that can express them. The resulting grammar is called ATOM, and is based on passing data through a series of layout operations that divide the output of previous operations recursively until the size and position of every data point can be determined. We evaluate the expressive power of the grammar by both using it to describe existing unit visualizations, as well as to suggest new unit visualizations.
Figure 1: Refinery allows exploration of large, heterogeneous networks by visualizing subgraphs of items relevant to user queries. Here, querying for a paper in a publication network has led this user to a conference session of possible interest. AbstractBrowsing is a fundamental aspect of exploratory information-seeking. Associative browsing represents a common and intuitive set of exploratory strategies in which users step iteratively from familiar to novel bits of information. In this paper, we examine associative browsing as a strategy for bottom-up exploration of large, heterogeneous networks. We present Refinery, an interactive visualization system informed by guidelines for associative browsing drawn from literature on exploratory information-seeking. These guidelines motivate Refinery's query model, which allows users to simply and expressively construct queries using heterogeneous sets of nodes. This system computes degree-of-interest scores for associated content using a fast, random-walk algorithm. Refinery visualizes query nodes within a subgraph of results, providing explanatory context, facilitating serendipitous discovery, and stimulating continued exploration. A study of 12 academic researchers using Refinery to browse publication data demonstrates how the system enables discovery of valuable new content, even within existing areas of expertise.
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