The relation between visualization size, grouping, and user performance

Abstract

In this paper we make the following contributions: (1) we describe how the grouping, quantity, and size of visual marks affects search time based on the results from two experiments; (2) we report how search performance relates to self-reported difficulty in finding the target for different display types; and (3) we present design guidelines based on our findings to facilitate the design of effective visualizations. Both Experiment 1 and 2 asked participants to search for a unique target in colored visualizations to test how the grouping, quantity, and size of marks affects user performance. In Experiment 1, the target square was embedded in a grid of squares and in Experiment 2 the target was a point in a scatterplot. Search performance was faster when colors were spatially grouped than when they were randomly arranged. The quantity of marks had little effect on search time for grouped displays ("pop-out"), but increasing the quantity of marks slowed reaction time for random displays. Regardless of color layout (grouped vs. random), response times were slowest for the smallest mark size and decreased as mark size increased to a point, after which response times plateaued. In addition to these two experiments we also include potential application areas, as well as results from a small case study where we report preliminary findings that size may affect how users infer how visualizations should be used. We conclude with a list of design guidelines that focus on how to best create visualizations based on grouping, quantity, and size of visual marks.

Design Spaces in Visual Analytics Based on Goals: Analytical Behaviour, Exploratory Investigation, Information Design & Perceptual Tasks

“…For instance, different bar chart designs impact accuracy [8], the ratio of line-charts and use of dual-line charts effect the choices people make under uncertainty [1] and visual perception can constrain the use of colour in visualisations [9], particularly in maps [10]. In addition, the grouping, quantity, and size of visual marks has been shown to affect 'visual search' perceptual efficiency [11].…”

Representation Effects and Loss Aversion in Analytical Behaviour: An Experimental Study into Decision Making Facilitated by Visual Analytics

“…Volume, variety, and velocity are attributes of 'big data' [8,22] that make it impossible for a (human) dataproduct designer to account for all possible future visual representations. For example, increasing the dots shown in a scatter plot reduces the accuracy of visual search tasks [13]. With this in mind, the effectiveness of a scatterplot may change over time given changes in the volume of data.…”

Data Visualization in Support of Executive Decision Making

“…Do data visualization design elements, such as color, size, and data grouping affect user performance? To test such a premise, an experiment was conducted whereby participants were asked to find a target within a visual (Gramazio, Schloss, & Laidlaw, 2014). Two experiments were conducted, one using a scatterplot and one depicting squares within a grid.…”

“…The results of the scatterplot and grid experiments concluded that color layout, quantity, and size of the marks impact on visual search time, impacting user performance (Gramazio et al, 2014;Ware, 2012).…”

“…Secondly, spatial ordering of marks in relation to the number and size of visual marks also impacts user performance (Gramazio et al, 2014).…”