Visualizing Movement in Theme Park
VAST 2015 Mini Challenge 1
Yuanchao Cai1
Karen Tay2
Budi Winarta3
Tin Seong Kam4
School of Information Systems
Singapore Management University
1 INTRODUCTION
Analysis of movement is currently a hot research topic
in visual analytics. Visual analytics leverages methods and
tools developed in other areas related to data analytics,
particularly, statistics, machine learning, and geographic
information science [1].
The 2015 VAST Challenge Mini-Challenge 1 dataset
comprises of movement tracking information for all of the
paying park visitors over 3 days at Dinofun World Theme
Park. The challenge involves characterizing the attendance
at the park (Question 1), identifying differences in patterns
of activity (Question 2), and spotting anomalies that are
most relevant to the crime committed over the weekend
(Question 3).
2 DATA PREPARATION
The data consists of 3 days of visitors’ movement and
rides they have taken in the theme park. It records 5 type
of variables: Timestamp, Visitor's ID, Movement Type, X
and Y coordinates on the map. We imported the park map
into Tableau and set it as a background image. By filtering
on check-in type, we were able to visualize the check-in
locations on the map, as shown in Figure 1. With the
tooltip, we can then map each individual ride to their
corresponding (X,Y) coordinates, and derived a list of
locations as shown in Table 1.
We used SAS JMP Pro to join the original dataset with
the list of locations. JMP Pro was used to concatenate the
3 days of data into a single set, and this is used for
Question 2 and 3. JMP Pro was also used to reshape the
data (via tables join, concatenate, and summarizing) to get
a list of unique visitors with aggregated data to show
details on number of rides taken by ride type, sequences,
and time spent in the theme park. The ride sequences
were concatenated via a custom Python script per visitor
per day of visit. The aggregated dataset is used for
Question 1.
Table 1 Sample list of derived locations
Coordinates
73,79
17,67
Location
Sauroma
Bumpers
Jurassic Road
17,43
Firefall
Ride Type
Kiddie
Rides
Rides for
Everyone
Thrill Rides
type
check-in
check-in
check-in
Figure 1 Park Map in Tableau: Tooltip for Creighton Pavilion
3 QUESTION 1
JMP Pro is used for Question 1, as it combines statistical
analysis and visualization tools. We applied hierarchical
clustering on the aggregated dataset, and the following
are used as the clustering criterion:
1. Total Number of Thrill Rides
2. Total Number of Rides for Everyone
3. Total Number of Kiddie Rides
4. Total Number of Shows & Entertainment
5. % of Thrill Rides (out of total check-in)
6. % of Rides for Everyone
7. % of Kiddie Rides
8. % of Shows & Entertainment
We used the interactive slider in JMP Pro, and
generated 12 clusters from the clustering results. Two way
clustering was selected so that a heatmap is displayed
together with the dendrogram. By observing the colours
of the heatmap, we are able to identify the various
characteristics of each cluster.
We can also observe the distribution of other variables
via JMP Pro. Via coordinated link views, we can select a
cluster or subset of a cluster in the clustering results
(Figure 2), to highlight the distribution of the variables in
another window (Figure 3). This allows us to study other
aspects of a cluster.
IEEE Conference on Visual Analytics Science and Technology 2015
October 25–30, Chicago, Il, USA
978-1-4673-9783-4/15/$31.00 ©2015 IEEE
147
visitor is inside the ride or is stationary until the next
movement type is recorded. In addition, the relevant
information is embedded in the tooltip. Via this
visualization, we were able to investigate any suspicious
behaviour and identify the anomalies.
Figure 2 Selected cluster in clustering results
Figure 3 Highlighted distribution via coordinated link view
JMP Pro also has the feature of saving the analysis steps
as a script so that the results can be re-generated easily.
This enables ease of collaboration among team members.
4 QUESTION 2
Tableau is used for Question 2 on the concatenated
dataset so that we can visualize the differences in patterns
across the 3 days. Tableau is a professional visualisation
tool which is user-friendly, and provides intuitive drag and
drop features allowing for fast analysis of data. It is also
easy to add annotations so that we can highlight the
insight on the image itself.
We used the Quick Filter in Tableau so that we can check
the patterns pertaining to each Ride Type; otherwise it will
be too much information for the human eye to perceive
patterns if all the ride types are shown together.
In addition, we can also use the Selection in Tableau to
highlight the pattern we are interested in, with the others
shown in a grey out manner. This allows the user to focus
on the selected data while still having an overall view of
the rest of the dataset, as shown in Figure 4.
5 QUESTION 3
From the findings of Question 2, we were able to
deduce that the crime happened on Sunday morning. To
visualize the visitors’ movement in Tableau, we plotted the
visitors’ activities in a sequence within a given time period
(via the filter function), as shown in Figure 5. The
‘movement’ type data is indicated by small blue circles,
while the ‘check-in’ types are using bigger shapes so as to
distinguish them easily. Only the Entrance, Creighton
Pavilion, and Grinosaurus Stage shapes are different, as
they are the point-of-interest in the investigation. The 3
Entrances are in ‘triangle’, the Creighton Pavilion is in
‘cross’, and Grinosaurus Stage is in ‘asterisk’. When
there’s a blank space between activities, it means the
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Figure 4 Selection feature in Tableau
Figure 5 Plotting visitors’ movement in Tableau
6 CONCLUSION
JMP Pro and Tableau had their own strengths and hence
were used in different manners during data preparation.
JMP Pro was used for Question 1 as it combines the power
of statistical analysis and visualization. Tableau was used
for Questions 2 and 3, as it is user-friendly, and provides
the tools for the user to explore various manners of
visualization.
REFERENCES
[1] Andrienko, N., and G. Andrienko. "Visual Analytics of
Movement: An Overview of Methods, Tools and
Procedures." Information Visualization, 2012, 3-24.