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Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of visualization effectiveness primarily focus on isolated, predefined, low-level tasks, such as estimating statistical quantities. This study more holistically explores visualization interpretation to examine the alignment between designers' communicative goals and what their audience sees in a visualization, which we refer to as their comprehension. We found that statistics people effectively estimate from visualizations in classical graphical perception studies may differ from the patterns people intuitively comprehend in a visualization. We conducted a qualitative study on three types of visualizations---line graphs, bar graphs, and scatterplots---to investigate the high-level patterns people naturally draw from a visualization. Participants described a series of graphs using natural language and think-aloud protocols. We found that comprehension varies with a range of factors, including graph complexity and data distribution. Specifically, 1) a visualization's stated objective often does not align with people's comprehension, 2) results from traditional experiments may not predict the knowledge people build with a graph, and 3) chart type alone is insufficient to predict the information people extract from a graph. Our study confirms the importance of defining visualization effectiveness from multiple perspectives to assess and inform visualization practices.
Systematically changing the size and opacity of points on scatterplots can be used to induce more accurate perceptions of correlation by viewers. Evidence points to the mechanisms behind these effects being similar, so one may expect their combination to be additive regarding their effects on correlation estimation. We present a fully-reproducible study in which we combine techniques for influencing correlation perception to show that in reality, effects of changing point size and opacity interact in a non-additive fashion. We show that there is a great deal of scope for using visual features to change
viewers’ perceptions of data visualizations. Additionally, we use our results to further interrogate the perceptual mechanisms at play when changing point size and opacity in scatterplots.
Touchscreen-based rendering of graphics using vibrations, sonification, and text-to-speech is a promising approach for nonvisual access to graphical information, but extracting trends from complex data representations nonvisually is challenging. This work presents the design of a multimodal feedback scheme with integrated spatial audio for the exploration of histograms and scatter plots on touchscreens. We detail the hardware employed and the algorithms used to control vibrations and sonification adjustments through the change of pitch and directional stereo output. We conducted formative testing with 5 blind or visually impaired participants, and results illustrate that spatial audio has the potential to increase the identification of trends in the data, at the expense of a skewed mental representation of the graph. This design work and pilot study are critical to the iterative, human-centered approach of rendering multimodal graphics on touchscreens and contribute a new scheme for efficiently capturing data trends in complex data representations.
Spatial data is best analyzed in situ, but existing mixed reality technologies can be bulky, expensive, or unsuitable for collaboration. We present VisTorch: a handheld device for projected situated analytics consisting of a pico-projector, a multi-spectrum camera, and a touch surface. VisTorch enables viewing charts situated in physical space by simply pointing the device at a surface to reveal visualizations in that location. We evaluated the approach using both a user study and an expert review. In the former, we asked 20 participants to first organize charts in space and then refer to these charts to answer questions. We observed three spatial and one temporal pattern in participant analyses. In the latter, four experts---a museum designer, a statistical software developer, a theater designer, and an environmental educator---utilized VisTorch to derive practical scenarios. Results from our study showcase the utility of situated visualizations for memory and recall.
Y-axis truncation is a well-known, much-debated visualization practice. Our work complements existing empirical work by providing a systematic analysis of y-axis truncation on grouped bar charts. Drawing upon theoretical frameworks such as Algebraic Visualization Design, we examine how structure-preserving modifications to visualization affect user performance by systematically dividing the space of possible truncations according to their monotonicity and the type of relations in the underlying data. Our results demonstrate that for comparing and estimating the difference between the lengths of two bars, truncating the y-axis does not affect task performance. For comparing or estimating the relative growth between two bars, truncating monotonically has similar performance to no truncation, while truncating non-monotonically is very likely to impair performance. We discuss possible extensions of our work and recommendations for y-axis truncation.
All supplementary materials are available at https://osf.io/k4hjd/?view_only=008b087fc3d94be7ba0ce7aea95012a7.