Visualization Papers at CHI 2013

by Enrico on May 9, 2013

in News

I just came back from CHI 2013, the premier conference on human-computer interaction (Paris was chilly and expensive. Yet, dramatically beautiful, as always). Here is a selection of interesting visualization papers I picked up from the program.

Using fNIRS Brain Sensing to Evaluate Information Visualization Interfaces. Interesting study from Tufts University on the feasibility of using brain scanning techniques to study mental workload in visualization.

Weighted Graph Comparison Techniques for Brain Connectivity Analysis. Excellent study on the ever-lasting battle between node-link graphs and matrices (to visualize weighted graphs in this case). Matrices win over node-links almost in every task. Very good example of exploration and evaluation of a specific design space. A lot to learn here.

The Challenges of Specifying Intervals and Absences in Temporal Queries: A Graphical Language Approach. Visual and interaction design study to allow end-users (doctors in this case) to specify complex temporal queries without writing a single line of code. It makes me think how visualization can and should be used not only as an output device but also a way to facilitate inputing data into a system.

Evaluating the Efficiency of Physical Visualizations. User study comparing 2D and 3D bar charts on a standard computer display to physical bar charts fabricated with a laser printer. Physical 3D is more effective than display 3D. Why? See the paper. (Side note: we featured this work in a Data Stories episode on Data Sculptures)

Contextifier: Automatic Generation of Annotated Stock Visualizations. Automatic annotation of stock market line graphs by extracting text from news articles. Annotation has been neglected for a while in vis (maybe because text is not considered part of the visualization?) but I think it’s super important. This is a great first step in the right direction.

Motif Simplification: Improving Network Visualization Readability with Fan, Connector, and Clique Glyphs. We all know how easily graphs can turn into hairballs. Motif simplification is a smart way to reduce the complexity of graphs by aggregating nodes into predefined glyphs.

Evaluation of Alternative Glyph Designs for Time Series Data in a Small Multiple Setting. User study on the comparison of icon-sized time-series visualizations. Two aspects are evaluated: layout (circular, linear) and value coding (length, color intensity). The study leads to a number of design guidelines (and hey … I am one of the co-authors here :))

I hope you’ll enjoy reading these papers. There is a lot of food for thoughts here. Comments, requests, criticism, always welcome.

Take care.

 

 

 

 

  • stephenfew

    Enrico,

    The first paper that you cite above–the one involving fNIRS brain scans–was horribly designed. Its claims are absurd and unfounded. I would encourage you to read my review of this study at http://www.perceptualedge.com/blog/?p=1492.

    Thanks,

    Stephen Few

  • FILWD

    Thanks Stephen for your pointer. I remember having scanned your review at some point and decided to stop and read the paper first (I didn’t want to be influenced). But I never actually managed to read the paper carefully. I just saw the presentation. I think the paper looks questionable if the focus is on the pie vs. bar kind of question (the task is unrealistic). But if the question is: can we detect interesting effects with brain scanning? Well … the paper looks very preliminar but interesting to me. Having talked with the authors (I know Remco very well) I believe their primary interest is in the fNIRS technique rather than in the charts.

  • stephenfew

    Enrico,

    I also know Remco and was impressed by some of the work that he did while earning his doctorate under the direction of Robert Kosara at the University of North Carolina. This CHI paper about the usefulness of fNIRS, however, is a mess. Not only are the claims that they make about bar graphs vs. pie charts absurd and unfounded, but their claims about fNIRS are equally suspect. While it is quite possible that fNIRS scans might be able to reveal useful information relevant to HCI and data visualization, what they did in this project did nothing to demonstrate this. They drew conclusions about the meaning of hemoglobin oxygenation levels in the brain–that brains were suffering from harmful cognitive load–when all we actually know is that higher oxygenation levels represent higher levels of brain activity. We cannot interpret the nature of brain activity based on fNIRS readings, nor can we hat more activity is bad. This is bad science. We cannot help the fields of human-computer interaction and information visualization research by endorsing poorly designed studies like this. In fact, by endorsing them or even merely listing them as interesting, and thus giving them positive exposure, we encourage these fields of study to remain pseudo-scientific.

  • Xeno Phundibulum

    > Matrices win over node-links almost in every [brain connectivity vis.] task.

    The thing is, matrices are not much of a visualization. Nodes and links work for small or very stylized graphs and can also be useful for showing a selection from a larger graph. A layout algorithm can be used to reveal structure of a new data set: is it one big hair ball (giant component) or bow tie, or ring-like, etc.

    • FILWD

      Xeno, I am not sure I understand what you mean when you say “matrices are not much of a visualization”, honestly it sounds weird to me. Actually, matrices are a quite powerful and have the benefit of avoiding overlap. There is another study form JD Fekete, some years ago at InfoVis, showing again the benefits of matrices over node-links. That said, I certainly believe node-links are very powerful too and have a very straightforward interpretation people get in a second.

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