Hi! I'm Dev, an undergraduate student at University of Toronto, this work was done in collaboration
with professor Haijun Xia from UCSD, and my supervisor, professor Daniel Wigdor.
In VR, we have a limitless working area, free from any physical spatial constraints. But
most immersive visualization systems only make use of a tiny portion of that space.
So, we explore how to leverage all that space for visualizing multi-dimensional datasets.
Okay, let's walk through a quick demo. Put yourself in shoes of a person that would like
to explore a dataset of historical wildfires in US.
Hey! I've loaded it up in VR. We start off in this empty space. In the center is a panel
containing all of our points; each a single wildfire.
To transform these points, we begin by branching out from this root data panel
This creates a Filter, which takes the root datapanel as input, and outputs a transformation
on it. A filter is composed of actions on the left, and data attributes on the right,
which are used as input to the actions Let's group by the statistical cause, or the
reason for why the wildfire occured, things like Arson, Campfires, or Lightning are among
the categories. Points with same category are assigned a common color and so I wonder
how these cateogories are distributed on the map of US.
To do so, I can just chain together another Filter. In the second Filter, I'll distribute
by the longitude and latitude. But all the overlap makes it difficult to decipher the
color of the point; or why the wildfire occurred. So, let's go back to the previous filter, and
here I'll split up the groups, meaning that points with the same cause will be in their
own data panel. For example, here we have a datapanel with
all the wildfires caused by lightning. Now, the panels at the ends are difficult
to make out, since they are further away from me.
Let's instead set the layout as circular, and now it's much easier to parse through
them. There seems to be some place in North Dokota
that has a huge amount of Arson cases. Makes me wonder about how the wildfires vary state
to state. So let's branch out in a different direction.
This time, I'll split up by state, and lay them out vertically, putting each one of the states
on a seperate floor; and I've also sorted them by the number of wildfires.
To navigate to one of these states, I can just tap on it. Let's go over to California.
This'll envoke an elevator to take me all the way up to the 6th floor.
Okay, let's see how they are doing over time. Well, we can see seasonal spikes, and recent
years do seem to be a bit worse. But what about the fire sizes?
Now interestingly, even though most fires are red, or class B, there are quite a few
class F and G fires, the blue and purple ones! Those can be over 5,000 acres. I wonder how
this'll look for other states. So, I'll copy my analysis, and paste on a different floor
Let's go to New York. Okay, so even though there are a lot of wildfires in New York too,
nearly all of them are relatively small compared to California's.
Now I would love to continue to analyze and explore this dataset, but for now, let's loop
back to the paper.
So, to recap, DataHop uses the space afforded by VR to spatially lay out each step in one's
data analysis workflow. Now, we conducted an exploratory user study
with 6 participants and found that users were able to explore their datasets in novel, and
meaningful ways. For instance, using the elevator to maintain
the spatial linkages when navigating through the structure. Be sure to check out the paper
for more details.
This work opens up several interesting directions. First, being able share these structures can
be very powerful since they are a representation of one’s thought process, and so others
not only see the resulting visualization but can observe each step spatially to figure
out how it was derived. They also lend themselves nicely to collaboration;
by using the space to reason, discuss, and analyze data.
Right now, we enforce a consistent user scale, but it'll be interesting to use dynamic scales
and enable interactions with, individual datapoints, to groups of datapoints and then to even whole
datasets. This can open new forms of storytelling and analysis.
DataHop provides a glimpse into how future VR interfaces could use the space around us
and our spatial cognition skills for data exploration and visualization.
Thank you!