22 July 2010

How Information Visualization Novices Construct Visualizations

Visualization for the masses is a topic that has gained a lot of attraction in the InfoVis community in recent years, e.g. in projects such as IBM ManyEyes. The goal is to enable a wide user population to leverage information visualization technology to understand large amounts of data. This could potentially help them make more informed decisions, and is especially promising as more and more data becomes available (see open data). However, there are still many challenges that need to be addressed so that visualization for the masses can become a reality, ranging from limited visual literacy to insufficient tool support.

Together with Melanie Tory and Margaret-Anne Storey, I investigated how information visualization novices construct visualizations in a laboratory setting. Our research paper "How Information Visualization Novices Construct Visualizations" was accepted for presentation at IEEE InfoVis 2010.

Here is the abstract of our paper:

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.

From our observations, we derived abstract models 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.

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