How Information Visualization Novices Construct Visualizations

Abstract

It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.

Exploration Views: Understanding Dashboard Creation and Customization for Visualization Novices

“…Our work extends the above in the following ways: Using guidelines from [12,15], we build a fully functional dashboard visualization system, and observe how novice users use the interface to create dashboards (without a human mediator [12]). We compare our observations of novices to reactions from experts, and derive additional design guidelines for visualization dashboards that target both user groups.…”

“…They also suggest that visualization tools for novices should: allow user-friendly data input for common data formats; automatically provide visualizations or reasonable defaults based on data types; and use contextual information to clarify the displayed data and encoding. Grammel et al [12] investigate how novices create single charts, using a human mediator to interact with the creation interface. They found that the main activities performed by novices (apart from communicating their specifications to the mediator) are data attribute selections, visual template selections, and viewing and refinements of the above.…”

“…An easy creation process is essential for novices to experiment with dashboard designs fast. As seen in [12], visualization novices often have partial mental specifications for their visualization needs and tend to refine and change their designs. To ensure a user-friendly dashboard creation, the sequence of steps needs to be simple, with continuous visual feedback on the final outcome of user choices.…”

“…To ensure a user-friendly dashboard creation, the sequence of steps needs to be simple, with continuous visual feedback on the final outcome of user choices. Moreover, as novice users may have no previous knowledge of what visual templates and representations are possible for different data types, EV must provide chart suggestions and templates to choose from (also suggested by [12,15]). Finally, EV should support common data formats (recommendation in [15]), and users should not be restricted by data storage architecture, but be able to use data from multiple data-sources (e.g.…”

“…This is reflected in advances in easily customizable dashboard visualization systems (e.g. [28,29]), it coincides with advances in easily accessible information visualization environments for novices (such as ManyEyes [31]), and is also reflected in the increasing focus of the InfoVis research community on how to support novice users [12,15]. Nevertheless, there are still many questions about how novice users interact with actual information visualization systems.…”