Hello everyone, my name is Yalong Yang
An assistant professor in the Department of Computer Science at Virginia Tech.
In this talk, I'm going to present two intensive user studies comparing
different interaction techniques for revealing embedded details in matrix visualizations.
This is a collaborative work with my amazing collaborators from Harvard and CMU
including Wenyu, Fritz, Carolina, Robert, and Hanspeter.
Matrix is a widely used visualization in various domains and applications.
It is a compact way to visualize a large amount of data
The very initial idea of this project came out from my first conference presentation at IEEE VIS.
I presented our work on visualizing origin-destination data by connecting two geographic maps to a matrix
Let us focus on the matrix part.
Like every other matrix visualization, we use colors to encode data values.
Every matrix cell shows one number
Here, we are looking at more than 2500 numbers in one view, which is quite impressive!
Then, there was a question in the Q&A, asking what if
there is more than one number in each cell, and how should visualize and interact with such a multivariate matrix?
This project aims to provide solutions to this problem.
The most straightforward way is to embed a visualization in each cell.
Let us zoom in to see how it looks like.
Here, we are using line charts as an example.
We are not the first one doing this. People have put different types of visualizations into matrix cells.
And embedding charts into every single cell works well for relative small matrices.
However, for large matrices the embedded charts will be too small to see.
Using an interactive lens to selectively enlarge a region of interest is a promising solution.
But there is another problem,
the standard magnification lens introduces distortions that break the regularity in a matrix
That is, you can see, the lines in the focal area are no longer straight
these irregular shapes introduce difficulties to embed visualizations
We then investigated the literature and identified four representative lenses suitable for matrix visualizations.
I will introduce them one by one.
Here, we use greyscale colors to ensure our studies were accessible to a larger population.
Our first lens is an adapted Fisheye lens.
It ensures the cells around the focal area are in regular grids.
And distorts the surrounding cells.
The cells in the context area are intact and in their original size.
The second one is the Cartesian lens, which determines the scaling factors according to the distance from the cursor to the cells.
It ensures that cells are still in the shape of rectangles.
However, the cells are all in different sizes.
We have two different variations of TableLenses.
They both enlarge a certain number of rows and columns around the cursor,
and compress the remaining rows and columns constantly.
The main difference is that
the cells are stretched to rectangles in the stretch version
while in the step variation, those cells are not affected by adding additional gaps between them
we are interested in how those different distortions can affect the effectiveness
of interactively revealing the embedded details in the matrix.
We started by thinking about the typical workflow of using those interaction techniques.
The user needs to first identify the targets.
Then move the cursor to the target to reveal the details.
And finally, interpret the details to make sense of the data.
Oftentimes, it's also important to switch between the focus and the context.
These steps can be considered as four components.
Wayfinding, Travel, Interpretation, and Context-switching.
We focused on the three interaction components in this study.
that is wayfinding, travel, and context-switching.
To systematically explore those components, we designed three fundamental tasks:
Locate, where participants need to click on a specific cell highlighted with an orange outline.
This tests the travel component
Search, where participants need to search for a cell with the highest single value in a region.
This tests both the wayfinding and travel components.
Context, where participants need to find the largest cluster as a time instance where a
given cell reaches its highest value.
This tests all three interaction components in one task.
We then run a within-subject study with 48 participants to compare their performance.
We found accuracy was high across all conditions, so we focused on analyzing the completion time.
In the Locate task, our participants were faster with Fisheye and Cartesian
than with the TableLenses.
Within TableLenses, the stretched was also faster than the step variation.
In a post-hoc analysis, we found one potential reason for the bad performance in TableLenses.
Like demonstrated in this figure, the distortions in TableLenses do not allow the users to
precisely land on the target column.
Please see our paper for more detailed analysis and discussion.
in the Search task, all conditions had a similar performance.
Cartesian was slower than the other three in the context task, with a small effect.
We believe the different cell sizes in the cartesian lens affects the performance of contact switching.
Lenses are not the only interaction techniques that can reveal details.
There are other two widely-used interaction techniques: Overview+Detail and Pan&Zoom.
In Overview+Detail, we allow the user to show the details in a separate view
In Pan&Zoom, the user can zoom in to reveal the hidden details and then can zoom out for an overview.
We then run another user study with the same tasks comparing the best performing lens from the first study
to those two different techniques.
In both the Locate and Search tasks, we found Pan&Zoom performed the best.
The Fisheye lens was also faster than the Overview+Detail.
Overall, Pan&Zoom has the best wayfinding and travel performance.
Which aligns with many other previous studies.
In the Context task, we found Overview+Detail outperformed the Fisheye lens.
and had a similar performance as Pan&Zoom.
We also found participants clearly did not like the lenses,
with the lowest reusability rating and highest mental and physical demand ratings.
In summary, we conducted a systematic evaluation of interaction techniques for
revealing the details in multivariate matrix visualizations.
We found, within the tested lenses, the Fisheye lens was the winner.
When comparing the best performing lens to Overview+Detail and Pan&Zoom,
we found Pan&Zoom had the best performance.
We believe our results can serve as empirical evidence when people need to select interaction techniques
for multivariate matrix visualizations.
Thank you very much for your attention!