Hello everyone! Welcome to the presentation of our paper
'Multivariate Visualization of Game Metrics: An Evaluation of Hexbin Maps'
in which we explore the use of those kinds of maps for the analysis of
gameplay data.
By now, visualizations have become an important part of games user research
and analytics as well as for players to facilitate the analysis of behavioral
data, also including the evaluation of spatial
game metrics A frequent task in this respect is to
assess how certain metrics vary across a game map which is also
reflected by the popularity of heatmaps in games research.
However, one limiting factor of many existing visualizations
- and this also includes heatmaps - is their focus on a single variable.
This makes it difficult to compare multiple variables against each other
and also across a map. Here, we address these
issues by using maps based on hexagonal binning
combined with visual glyphs to encode multivariate data.
as glyph we use a variation of the so-called Wurman Dot. It is basically
composed of two circles where the outer circle indicates the
spatial area to which the data belongs to
and an inner circle which shows the actual data within the area.
By altering visual attributes such as the size and color of the inner circle
multiple data values can be encoded. In addition, we also added direction
indicators which show directional information
which can also be very interesting in games, for example, from where attacks
came from. These glyphs have the advantage that
they are visually simple and hence easy to read when they become
smaller. The map itself is subdivided into
regular hexagons used for binning the data. A glyph
representing the data is placed within each hexagon while the
hexagons itself are not shown in the final visualization.
Regular hexagonal binning also has several advantages.
Amongst others, it creates areas of equal size
which eases comparisons between areas and it also makes data captured at
different time points comparable as the subdivision itself is not
affected by the actual distribution of the data.
To evaluate the approach we used real data from the real-time strategy game
Starcraft showing different information such as
the number of destroyed units, team information, or the direction of
attacks. Based on the data we created five
different variants of hexbin maps with increasing complexity.
The simplest one only showed one variable by varying the size and color
of the inner circle. A simple unipolar color gradient was
used in this case. The next one also only showed one
variable but used a bipolar color scheme to
also include information about the team. This was then further extended by adding
a second variable encoded through the color of the outer circle.
Next, we added direction indicators to the information already present in the
previous map for a total of three variables. And the
last one, the most complex one, showed four
variables by encoding two variables through the size
and color of the inner circle (instead of only one like in the previous
maps). For the evaluation we also defined a
number of tasks of varying complexity that the participants had to solve and
which captured different aspects of the visualization.
These tasks were administered through an online survey which required
participants to mark the correct area on the map.
The survey controlled for learning effects by randomizing the order of the
maps and also by using different data for
them. In total, we received about 200 responses.
For each task we analyzed the correctness,
the time taken to solve it, and the efficiency of the visualization for the
task. You can find detailed statistics on all
three aspects in the paper but today I will rather focus on more
high-level implications and results.
First, we can say that participants appreciated the display of multiple
variables in a single visualization, confirming its value for gameplay
analysis. Even with three and four variables the
visualization remained clean and structured. Correctness achieved with
the maps, in terms of estimating and comparing values,
was generally good but sometimes also reduced by the visual encoding which was
used. As such, the visual encoding needs to be
chosen with care as it can severely impact performance.
One also needs to keep in mind that only a certain number of variables
can be reasonably shown within an area of certain size,
also because the evaluation has shown that the color-coded areas
need to be of sufficient size themselves to ensure that the colors can be
perceived accurately.
To conclude, our results indicate that the display of multiple variables at
once, as made possible by the proposed hexbin
maps, is useful for gameplay analysis and that
the representation remains clear and structured
even for up to four variables. If you are interested in a detailed breakdown of
performance across different tasks and maps please refer to
our paper. That's it from me today. Thanks for
watching!