Hi everyone!
My name is Kim Sauv'e9, and I'm a PhD student at Lancaster University in the UK and I'm
very proud to present our paper: A Change of Perspective: How User Orientation Influences
the Perception of Physicalizations.
First of all, what is a data physicalization?
It's a physical artifact whose geometry or material properties encode data.
There is already a lot of work out there in the field of physicalization, and two of the
key benefits of these kind of systems are that it allows for collaboration and it provides
opportunity for spontaneous interactions.
However, these benefits rely on the key assumption that physicalizations effectively communicate
information in all directions, and to all users equally.
But people might interpret data differently depending on what side of the physicalizations
they are viewing.
The consequences of this are profound as it could make physicalizations ambiguous and
could lead to incorrect interpretations.
We propose a systematic approach to create a better understanding of how the orientation
of an individual influences their perception of data.
The core research question we try to answer with our work is: How can we develop a better
understanding of the relation between user orientation and the perception of physicalizations?
To investigate this relation, we designed 6 example physicalizations, and the design
of these is informed by the well-known bar charts that are often used in this field.
Each of the physicalizations consists of 16 objects and vary in 4 lengths.
We did this to create a range of complexity, but also keep them similar in density.
The layouts of the 6 physicalizations were created by applying 3 different visual properties
that are known from 2D visualization.
First of all, we applied proximity.
This was used to create differences in internal and external distances between the objects.
And also to create two different types of spatial relations: either in a grid or linear
fashion.
Secondly, we used continuity.
We used that to create differences in perceived connectedness between the objects, either
by using objects of similar size or placing objects of increasing size in a consecutive
manner.
And lastly, atomic orientation, which was
used to create differences in the orientation of the individual objects, either in the x,
y or z plane.
For the purpose of our study, we intentionally created physicalizations containing edge cases
of different properties, so they were open to more than one interpretation.
For example, if we look at phys1: according to the proximity between objects it is one
cluster.
However, if you would look at continuity, it could either be 4 clusters of objects of
constant size or 4 clusters of consecutive size.
To evaluate the user responses across perspectives, we presented each physicalization from four
orientations and we called them North, East, South and West.
This was to cover the major viewing angles.
We recruited 20 participants to perform tasks for each orientation of each physicalization,
so in total they saw 24 physicalizations each.
To go a bit more into detail on the exact 3 tasks that the participants did, we applied
the information retrieval tasks that are known from 2D visualization.
First, we asked the participants to identify clusters of objects.
The terminology we used for this was: Can you identify any groups of objects?
Second, we asked them to indicate any anomalies and asked: What is the group that stood out
first to you?
Lastly, we wanted them to find any extremum that they saw by asking the question: Can
you point out the highest and lowest value or values?
For each of the tasks, we asked the participants to clearly point out the objects with their
hands to capture the exact location and structure of the identified clusters, anomalies and
extremum.
To be able to use our data to talk about the influence of orientation on the perception
of physicalizations we introduced the terminology Orientation Consistency or OC.
With OC we mean: the consistency of user responses to information retrieval tasks across different
orientations.
In this specific study, as we looked at 4 orientations, we categorized 4 different OC-values,
from 1 to 4.
To give an example, if a participant completed a task, for example clustering, different
across all orientations, they would get assigned an OC-value of 1, as there is no consistency
in how they performed the task across orientations.
Likewise, a participant with OC-value 3 means they completed a task consistently across
3 out of 4 orientations, meaning they were more consistent but not 100%.
We applied this method to evaluate the consistency in user responses across tasks, participants
and physicalizations
So what did we find?
Our results show that user perception is directly affected by orientation, which might not be
a surprise.
But to give an idea of the extend of this influence, if you look at the first two columns
of this table.
In case the participants were consistent across all 4 orientations for each of the tasks that
you see here, the values would have been 100%, whereas clearly they are not.
But it goes further than this; the influence of orientation actually created a systematic
lack of consistency not only across tasks, but also across participants, and across physicalizations.
So why did this happen?
We found that occlusion is one of the main reasons for these inconsistencies, and I will
explain 3 types of occlusion that we observed by means of the clustering task.
The first type that we observed was continuity occlusion, which occurs when a set of objects
seems to be intersected, which prevents someone to see the full continuity.
For example, for phys3, in the North orientation a cluster of 3 cubes was identified, as one
of the cubes is occluded.
However, in the East orientation, the continuity of the cubes is not occluded, as you can see
by the 4 green objects. Therefore they led to a cluster of 4.
Then we also saw proximity occlusion, which occurs when the perceived distance between
objects appears either further or closer together, which prevents someone from seeing the true
proximity.
For example, for phys5, in the East orientation the objects were perceived as 3 clusters.
However, in the South orientation the front left cluster occludes the objects in the back,
so they seem closer together, and therefore they were perceived as one larger cluster.
And lastly, atomic orientation occlusion, which occurs when objects of different forms
are perceived similar or when objects of similar form are perceived different, due to their
atomic orientation.
So if you look at phys4, in the West orientation the objects were clustered by size.
However, in the North orientation, objects of different size were perceived similar due
to the perspective of the viewer, leading to one larger cluster.
And the 3 objects in the back that are very different in their orientation led to a smaller
second cluster.
If we zoom in a little bit more on the effects of orientation on anomalies, we observed 2
different themes.
First, we saw that participants more likely observed non-occluded objects first, for example
the tallest objects or objects that were very distant from others.
And the second theme that we saw is that there was a relation between the clustering exercise
and the filtering of anomalies.
For example in phys2, in case participants identified the diagonal, tallest and minimally
occluded objects as anomalous this also showed in their clustering of the physicalization
as they would cut it into 2 clusters by the diagonal line.
And then if we look at the effect of orientation on perceived extremum, we observed in general
that participants would pick the smallest and largest objects, which you can see in the
light and the dark blue cubes in the example on the left.
However, if you look holistically at the different extremum that the participants found, we saw
3 different behaviors, and I'll explain that by using phys2.
The first way that we saw is that participants chose one single object as extremum for the
entire physicalization.
The second behavior that we saw was that they would assign a cluster of objects as a collective
extremum.
Or lastly, they would assign an extremum within each cluster that they identified.
To conclude, our work provides first insights on the variability and complexity of the relation
between orientation and user perception.
Our empirical findings show that, at least for these conditions, physicalizations cannot
communicate information in all directions reliably, which breaks one of the fundamental
aims of physicalizations.
With our work we would like to encourage others to design physicalizations that take perspective
into account and to use Orientation Consistency as a measure to see how successful the physicalization
is in communicating information across perspectives.
And with this I would like to thank you for your attention!
In case of any questions, feel free to contact us.
Thank you.