visualization optimization – vialab | Dr. Christopher Collins

Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

Contributors:

We report the results of a crowdsourced experiment that measured the accuracy of motion outlier detection in multivariate, animated scatterplots. The targets were outliers either in speed or direction of motion and were presented with varying levels of saliency in dimensions that are irrelevant to the task of motion outlier detection (e.g., colour, size, position). We found that participants had trouble finding the outlier when it lacked irrelevant salient features and that visual channels contribute unevenly to the odds of an outlier being correctly detected. Direction of motion contributes the most to the accurate detection of speed outliers, and position contributes the most to accurate detection of direction outliers. We introduce the concept of saliency deficit in which item importance in the data space is not reflected in the visualization due to a lack of saliency. We conclude that motion outlier detection is not well supported in multivariate animated scatterplots.

This research was given an honourable mention at CHI 2019.

Materials used to conduct this research are available for download here.

Publications

Acknowledgements

Balancing Clutter and Information in Large Hierarchical Visualizations

Contributors:

Rafael Veras and Christopher Collins

In this paper, we propose a new approach for adjusting the level of abstraction of hierarchical visualizations as a function of display size and dataset. Using the Minimum Description Length (MDL) principle, we efficiently select tree cuts that feature a good balance between clutter and information. We present MDL formulae for selecting tree cuts tailored to treemap and sunburst diagrams and discuss how the approach can be extended to other types of multilevel visualizations. In addition, we demonstrate how such tree cuts can be used to enhance drill-down interaction in hierarchical visualizations by enabling quick exposure of important outliers. The paper features applications of the proposed technique on treemaps of the Directory Mozilla (DMOZ) dataset (over 500,000 nodes), and on the Docuburst text visualization tool (over 100,000 nodes).

Validation is done with the feature congestion measure of clutter in views of a subset of the current DMOZ web directory. The results show that MDL views achieve near-constant clutter levels across display resolutions. We also present the results of a crowdsourced user study where participants were asked to find targets in views of DMOZ generated by our approach and a set of baseline aggregation methods. The results suggest that, in some conditions, participants are able to locate targets (in particular, outliers) faster using the proposed approach.

Check out our GitHub Repository for source code related to this project.

The slides from our VIS 16 presentation are available here.