Hello everyone, my name is Vanessa Peña Araya, and I'm going to present our work
with Anastasia Bezerianos and Emmanuel Pietriga
called a comparison of geographical propagation visualizations
Propagation phenomena occur in many domains, for
example in epidemiology we like to see when a virus starts affecting
people how did it spread to different cities, for example, or countries, etc.
Or sometimes when analyzing social networks we would like to know how fake news
spread over a set of users, for example.
Or sometimes we would like to see how a
package was replicated in a network of computers
And in many cases geography is key.
So for example when analyzing the Ebola propagation in
West Africa between 2013 and 2016 researchers found that, although there
were a lot of propagation between the affected countries, a lot of them were
actually within each country, and understanding this and understanding how
measures impact the propagation of a virus can influence the decision for
future epidemics, like the one we are facing now with the COVID-19.
How closing borders or quarantines impact on the spread of the virus
And we were wondering, for this work, how can we visualize geo-temporal
propagation patterns for the analysis of this data and not only for presentation
And we found little empirical data from the literature.
So, given the most commonly used visualizations for this case are
animation and a small multiples, a lot of work is dedicated to these
From those that are focused on spatial tasks, we found one that
is about an abstract spatial task
that focus on detecting a moving cluster, and another one that
focused on extracting data from flow maps and that is a qualitative study
And from this type of work and some others that focus on
other domains, like graphs for example, one of the main conclusions, of course
there are more, but one of the main conclusions is that animation is mainly
better for detecting sudden changes between two time steps, and in
particular for this study the researchers found that small multiples
is better to extract more information in longer period of times
There are other studies that focus in other visualizations like the one you
are seeing here, that is actually our work that we did in 2018, however these
works do not consider movement so they are not good for the kind of
question that we are asking now
So we still have the question, how can we
visualize your temporal propagation patterns and we break this question
in three sub questions
The first one is how to characterize propagation patterns
What are the questions that researchers ask when trying to analyze this kind of data
The second is, from the broad spectrum of visualizations, which
are the most appropriate ones to represent this data
And third, how can we create realistic scenarios to actually evaluate and design a study
for the selected visualizations
So I'm going to start with the first one, and to answer this question we found two main sources of information
The first one is a very complete taxonomy of general movement
patterns and we complement this with research work in propagation from
other domains, for example, epidemiology and social networks, these two mainly
We found that for example, in the past, several human diseases spread like a
wave fashion, like between contiguous regions like the animation you are seeing
now and this is the case of the Black Death in the 14th century
but also for diseases among animals.
However humans, we're much more connected now and we have means of fast travel like for
example trains and planes, and this means that for example we can see propagation
like the pattern you are seeing now with hops, with jumps
So here it starts in the bottom and then jumps to here, and then jumps to other places in the north
This kind of categorization, this kind of difference between
patterns without hops and with hops, we collect them all and ended up
selecting five dimensions to characterize a propagation pattern
The first dimension is scope: does a propagation spread to a complete, for
example, country or does it focus on a particular city
The second is direction: does a virus spread for example only to the north or
only to the west, or does it go to multiple directions
The next one is arrival: how much time does it take for the virus to propagate to a particular region
The fourth is hops, like the example that I was showing you before
Does it happen between adjacent locations or does it have geographical jumps
And finally Peaks: when a propagation reaches its maximum value in a single region
Something important to mention is that, the first three we
extracted from this taxonomy of of general movements and we
complemented them with these two last from works that focus especially in propagation
So once we had this, we needed to select the appropriate visualizations
and we ended up selecting three representative visualizations
The first two is animation and a small multiples because they are
the most common used and we complemented with glyphs over maps, that is also used in
other contexts and in this case it provides the advantages of the first two
Similar to small multiples it gives an overview at once
of the complete pattern and also, in terms of geography, it can
use the whole space to show the map
So just a small summary: in small multiples
what we did was to use color brightness to indicate the number of occurrences of
each location and one choropleth for each time step
In this case we see that
we have time step 0 with this pattern, time step one, etc, etc
In the beginning there wasn't a lot of occurrences because there was mainly
white, but in the last time step one of the location has a lot of occurrences
of these hypothetical virus as it has a very dark blue
Animation on the other hand uses the complete space
to depict the map and again we use color
brightness to indicate the numbers of occurrences of each location
Participants were able to see the pattern by either clicking play to see it
automatically, or to select a particular time step manually in the timeline
Finally, glyphs over maps will look like this, in which each location has this
grid that represents the temporal pattern: the first cell will
represent the first time step, the second will represent the second which in this case is 1
etc, etc
And the time step that has a darker border is the one that the participant
selected in the timeline
Again we used color brightness to indicate the number
of occurrences, so in this case for this location for example, we see that in the
first cell there wasn't a lot of occurrences, but at the end of the pattern there was
a lot of occurrences because of this dark blue color
A little detail to mention is that, in order to make these visualizations comparable
we tried to make the interactions as equal as possible and you can see more details
about this in the paper
So, ok, yes! Now we have the dimensions we would like to analyze,
the visualizations to represent the data,
so with this we can make hypotheses, yay!
We ended up having 5 hypotheses, one for each of
these dimensions, that became tasks, and we classified these hypotheses in three categories
The first one is tasks that require participants to make comparisons
between consecutive time steps and this is the case of the task direction
You need to know what was before to see how a path moves, if it follows a direction or not
and this is the same for tasks with hops, you need to know where the
propagation was before to know if there was a jump or not
And given the work
that we found in the literature, we expected that, as animation is better for
detecting sudden changes, this will be the preferred, the most effective for
these kind of tasks
The second category is those that required to make a search
over a long period of time, this is the case of scope that requires
participants to know if the propagation is spread to a complete
region or not
And arrival, when the virus arrived to a particular region
And given that glyph maps gives this overview, we expected that it will
be more effective for this kind of tasks
Finally, the third kind of tasks, requires to do
both processes: first search over all the time interval and then
make local comparisons
So this is the task of peaks, you need to find when more
or less is the peak and then to finally select you need to compare the
consecutive time steps
And we expected that small multiples will be better
for this kind of tasks
So once we wanted to evaluate, the first question
we asked ourselves was how can we create realistic scenarios
And we had two options here
the first one was to consider real-world data for example
the Ebola data or other epidemic that was at that time available
or generate our own data sets
We ended up considering the second option because
with real world data, the problem is that, one it doesn't really give us enough
flexibility to create different patterns for each of the tasks, and second it
was hard to ensure that the difficulty between the tasks because of the datasets
will be equal
So in the end we generated simulations with a particular
Python library and we created our own datasets
So in the end, we create datasets for each task and 6 variations to make repetitions
for each of the tasks
Two important things to remark is that we used maps
that participants were unlikely to know, and also that we used time intervals of
35 time steps and this is important because it means that participants had to
make analysis for long periods of time, not just two or three time steps
The study itself, was a within subjects study that lasted about an hour and that included
training and consent form and all the required stuff
And we recruited 18 participants
An example of the interface that we used, you can see here in
the right side of the slide
Okay, so we have all the ingredients, right?
What we would like to analyze, which visualizations, and we conducted the study
Now we can analyze the results
So when we analyzed the effectiveness of
the visualizations over all of the tasks, we found that small multiples is
actually faster than the other visualizations
however we did not find difference in error rate
On the other hand we found that participants
feel much more confident using animation and actually when we asked them to rank
them at the end of the study, animation was also the preferred visualization
over all the others
So in this case we found that the objective and
subjective metrics were not aligned in our study
When we analyzed results more focused on
the hypotheses, on these three categories of hypotheses, we found that
for the first type, in which we have these tasks that required comparison
of consecutive time steps
For the task direction we found that, yes, actually
animation is better for this case, however not for the task hops, so there
is no evidence that for this task animation is better for detecting sudden changes
For the second category, in which participants had to search in large time intervals
we found that for the task arrival, glyph maps, the overview that glyph maps
offer, is actually supporting this task but this only
happened when there is one single region, which is the case for arrival
However, for the task scope, in which participants had to search over
a large interval for multiple regions, this advantage disappears
so we did not found evidence to support this hypothesis
Finally, for the third type of task that
participants had to make a global search followed by the local comparison
we found that in this case, yes, there is evidence that small multiples is better
for this type of tasks
So, in summary we showed you our process to get
insights to reply to the question which visualization is better for analyzing
spatio temporal propagation patterns
And our results indicate that the effectiveness indeed
depends on the task, but overall small multiples are faster
and animation is preferred
If you would like to get more information of how we
generate the datasets, some examples of the visualizations and the analysis of
the results, you can go at the link in the bottom of the slide
Thank you very much for listening and bye!