Visualization Research

Visualization Research

When you hear the term “visualization research,” what comes to mind? Many people think of Tufte when they think about deep reflection on visualization. For example, ask any visualization researcher how many times their description of what they do has been met with a comment about how great Tufte’s books are. We agree, Tufte’s books are great resources! His guidelines, like maximizing the data-ink ratio or avoiding chart junk, are helpful maxims when you are starting to design a visualization and become aware of the large space of possibilities, even to show a simple data set. Yet Tufte’s suggestions can break down in many realistic design scenarios. Have you ever tried to follow Tufte’s advice to a T? You might end up with something like the “ghost” chart to the right. Is it really superior to the original chart on the left?

Visualization research actually cover

Understanding of the limits of different principles and guidelines for creating effective visualizations is one goal of visualization research. Started in the late 1980’s by computer scientists and others who were inspired by the possibilities for using interactive graphics to amplify cognition about data, visualization research is now a thriving community with a conference (IEEE VIS) that draws ~1200 people annually. Visualization researchers work on a number of problems that can help inform how anyone understands, designs, or evaluates visual representations of data:

Tools that make it easier to create visualizations

 Have you heard of Tableau Software, Spotfire, orD3? http://All three tools were proposed and developed by visualization researchers. A common research goal in the visualization community is to develop technology that makes it easier to create effective visualizations, so as to increase the amount of value that people can get from data even without advanced training in design or statistics. A recent trend toward declarative specification of visualizations, as realized in the Vega and Vega-lite grammars, makes specifying a visualization more concise and can reduce the amount of code one must write to develop visualization tools.

New encodings Visualization research produced encodings that are now commonplace in visualization tools, like treemaps, word trees, parallel sets or arc diagrams. Visualization research often considers the limits of conventional methods for representing data and how novel designs might surface new and interesting patterns in our data. 

 Visualization research be about

 If you stop and think about it, this is a pretty ambitious target! Visualization research covers an impressive breadth of topics from perception to memorability, from complex system design to theory about what comprises a graph. But as in all research communities, biases what we think a research contribution should look like in our field can limit the types of questions we consider worth pursuing. For example, while research on perception has played a role in much of the visualization research field’s history, we have not necessarily embraced cognition. Perhaps we are more comfortable with the “bottom-up” nature of perception because its more clearly tied to the visual encodings.

A focus on performance keeps visualization research relevant to the world, where people want to know which chart to use. But, that shouldn’t absolve researchers from trying to explain why a difference was found. This might take the form of proposing and testing for mechanisms in the visual system or cognitive strategies like heuristics. Considering the “why” in addition to the “what” can make it easier to reason about how a difference found between two specific visualizations might also be found under slightly different conditions.

Finally, we often draw a sharp line around representations of abstract data, considering other forms of diagrams or ways of contextualizing data to make it understandable outside our purview.