Hi, I'm John Thompson and today I will be presenting Data Animator
a novel authoring tool for expressive animated data graphics.
This work was advised by John Stasko and Leo Liu.
Animated transitions help to clarify changes between two data visualizations.
For example, this project from Nadieh Bremer shows the rapid population growth
in East Asian cities from 2000 to 2010.
Creative programmers, like Nadieh, use programming to create engaging animations.
However, creating such animations requires programming knowledge.
For designers, artists, and journalists without programming experience
creating expressive, animated data visualizations is tedious in design tools
or lacks flexibility in visualization tools.
With Data Animator, designers can author custom animated data visualizations without code or templates.
Data Animator takes static visualizations from Data Illustrator as input.
They then appear here as a vis board in our storyboard interface.
Connecting two vis boards generates an animated transition,
and the matching algorithm automates the minutia of matching objects based on underlying data.
I’ll use this example about East Asia Urbanization. The dataset consists of hundreds of cities.
Here are all the cities, their population is encoded by area, growing from 2000 to 2010.
Now we overlay circles to show the population in 2000 vs 2010.
A bar chart shows the total population increase.
Let’s drill down to show population growth by country.
And we can also show growth as a slope chart.
Now we see distribution as a histogram, with median of about 50%.
And zoom in to focus on this bar chart by country.
And finally compare population density with a dumbbell plot.
I’ll use this example to highlight the fundamental concepts of Data Animator.
First, Data Animator introduces a novel object matching algorithm
that generates transitions by default between two visualizations.
In this transition, the circles between two static visualizations are matched by the algorithm.
This is a simple match of one-to-one, where the cardinality of these circles matches as well as their data scopes.
In the interface, we create a connection, and an animated transition is generated by default.
These ellipses appear as a layer on the timeline and their size and color tween.
There are times when the matching algorithm produces matches that are inconsistent with our desired design.
Instead, we hope to achieve this design.
Where the slope chart exits to the bottom, and the histogram enters up.
The Object Matcher allows us to view and manually specify matches.
I select the matched set of ellipses and select “un-match”, creating a separate set of exiting and entering ellipses.
All sets of ellipses now fade in and fade out by default.
We change these preset effects in the timeline editor to now Move Out to the bottom for the slope chart.
and Move Up from the bottom for the histogram, as desired.
Later I’ll clean up the timing of this animation.
There are also cases where no match is made between visualizations because of shape type.
Here is the desired design, where the rectangles morph to ellipses.
Again, we go to the Object Matcher to create this match.
We select both object sets, and then click “Match”.
Now the rectangles morph into ellipses for this animation.
Hierarchical keyframing combines pacing techniques such as staging and staggering in a familiar timeline interface.
In the timeline editor, we expand this set of ellipses.
And we set the sequencing to be staggered by the population quantity.
And when we play this back, the animating ellipses start times are staggered by population,
or their eventual y-position in the slope chart.
Staging allows us to coordinate the relative start and end keyframes of object sets.
We drag start and end keyframes, to successively stage the histogram ellipses after the slope chart ellipses and paths.
The entering and exiting ellipses in this staged animation no longer obfuscate each other anymore.
We evaluated Data Animator against our research goals of expressivity and usability. First expressivity.
We demonstrate the expressivity of Data Animator with a gallery of example animated data graphics.
Each example includes a final video of the animation,
Such as this case of world record swimming holders.
And of a re-ordering box plot from Stephen Few.
Next we evaluated if designers could learn and effectively author animations with Data Animator.
We conducted a re-creation study with 8 designers, with varying levels of experience.
And they were all given a tutorial on how to use Data Animator.
They were asked to re-create the following 6 animations, which increase in complexity.
Designers completed tasks on average within a few minutes.
Again this shows they can understand and quickly create animations with Data Animator.
Thank you for listening to my talk, we encourage you to visit our website and create animated data graphics with Data Animator for yourself!