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.