=Paper=
{{Paper
|id=Vol-1621/paper8
|storemode=property
|title= Visualizing Museum Visitors’ Behavior
|pdfUrl=https://ceur-ws.org/Vol-1621/paper8.pdf
|volume=Vol-1621
|authors=Joel Lanir,Tsvi Kuflik,Nisan Yavin,Kate Leiderman,Michael Segal
|dblpUrl=https://dblp.org/rec/conf/avi/LanirKYLS16
}}
== Visualizing Museum Visitors’ Behavior==
https://ceur-ws.org/Vol-1621/paper8.pdf
Visualizing Museum Visitors’ Behavior
Joel Lanir, Tsvi Kuflik, Nisan Yavin, Kate Leiderman, Michael Segal
University of Haifa, Mt. Carmel, Haifa, 31905, Israel
ylanir@haifa.ac.il, tsvikak@haifa.ac.il, nisan.yavin@gmail.com, kateleiderman@gmail.com,
msegal14@campus.haifa.ac.il
ABSTRACT such a system. Based on these interviews, we designed a system
Museum curators are interested in understanding what is that visualizes museum visitors’ behavior patterns. Initial
happening in their museum: what exhibitions and exhibits do feedback suggests that this can be a valuable tool that can provide
visitors attend to, what exhibits visitors spend most time at, what much insight and understanding for museum personnel.
hours of the day are most busy at certain areas in the museum and
more. We use automatic tracking of visitors’ position and
2. RELATED WORK
movements at the museum to log visitor information. Using this Many museum researchers analyse museum visitors’ behaviour to
information, we provide an interface that visualizes both help museum practitioners to improve their exhibits, provide
better interpretations, and better understand the way the audience
individual and small group movement patterns, as well as
aggregated information of overall visitor engagement. is experiencing the exhibits and content provided to them [2].
These works often use ethnographic observations to examine
CCS Concepts issues such as visitors’ circulation [1], use of signage and labels
• Human Centered Computing Visualization [McManus], interaction with exhibits [13] and social interaction
Visualization application domain Information Visualization [10]. Using manual tracking and timing of visitors’ behavior using
• Human Centered Computing Ubiquitous and mobile unobtrusive observations, museum researchers have measured
computing systems and tools variables such as: the total time in an area, total number of stops,
proportion of visitors who stop at a specific exhibit, visitors’ path,
Keywords time of non-exhibit-related behavior and level of engagement with
Museum behavior; Museum mobile guide; Visualization; the exhibit [5, 13]. Summarizing these variables while focusing on
visitors’ interaction with exhibits, two measures are often used in
1. INTRODUCTION museum studies [2, 12]. Together these variables effectively
It is very important for museum curators and museum personnel capture how thoroughly visitors were engaged with an exhibit:
to understand and be able to analyze the activity and behavior of
• Attraction power indicates the relative amount of people who
visitors in their museum. The behavior of visitors can provide
have stopped in front of an exhibit during their visit. It is
curators with feedback on what is happening at the museum –
calculated by dividing the number of people who stop, by the
which exhibits are successful, where do people go, and in general,
total number of people who have visited the museum. This
how people interact with the content and exhibits that they have
measure provides us with an initial idea of the power of
designed.
attraction of the exhibit.
In order to understand visitors’ behavior, museum researchers rely • Holding power measures the average time spent in front of
today either on self-reported questionnaires or on manual tracking an exhibit. It is calculated by summing up the time a visitor
of individual visitors using unobtrusive observation, measuring spent in front of a specific exhibit. This measure provides us
variables such as total time in an exhibit, number of stops, with an initial idea of the power of an exhibit to hold the
proportion of visitors who stop at a specific exhibit and more [5, interest of a visitor.
13]. However, with the advent of technology, systems exist that
track and record visitors’ movements and paths during their visit Lately, automatic tracking and positioning technologies make it
at the museum. This creates the opportunity to provide much more easier to gather large quantities of data on the way visitors behave
detailed and accurate information to the museum curators that and interact. Zancanaro et al. [14] used automatically generated
relies on data of hundreds and thousands of visitors. logs of visitor positioning to categorize visitors’ behavior. Lanir et
al. [8] found differences between the behavior of visitors who
In this work, we present a prototype system that visualizes
visitors’ behavior at the museum. We use information of visitors’
movements gained from an indoor positioning system situated in
the museum. We first conducted several interviews with museum
curators and personnel in order to understand the requirements of
Copyright © 2016 for this paper by its authors. Copying permitted for
private and academic purposes.
Figure 1. Positioning system equipment and usage scenario
used a mobile guide in their visit with those who did not. Kanda et visitors (194 females) using the mobile guide during their visit.
al., [14] used spatial clustering to show visiting patterns and Average age of visitors was 43.2 years (SD = 18.4). We cleaned
estimate visitor trajectories. While these studies examined specific and imported these logs into a database, for the use of the system.
aspects of the visit behavior, there is no research that we are The system was built as a web client able to access the database
aware of that used automatic tracking for an open-ended visual from anywhere. D3 was used to produce the visualizations.
analysis of museum visitor behaviour. In our previous work, we
designed static visualizations to enable analysis of visitor
behaviour based on a novel glyph design [9]. However, feedback 4. SYSTEM DESCRIPTION
from curators revealed that they require a simpler, more The system is divided into three main modules. Individual visitor
interactive system. In this work, we take a different approach, analysis, group analysis and general information.
designing a full interactive system built on top of a visitor
database to enable a more generalizable system with easy access The individual visitor view allows seeing an individual user’s path
and understanding of visitor behaviour. during his or her visit at the museum. The path is shown on top of
the museum map. The view enables “playing” the path of the
3. INFRASTRUCTURE visitor, which uses a footstep icon that moves on the map and
The PIL research project focuses on exploring the possibility to emulates the visitor’s path. The user can fast forward the view to
use novel technologies to enhance the museum visit experience the end where the entire path is seen (Figure 2). In addition, a list
[7]. In the framework of the project, the Hecht1 museum, a small of exhibits, times spent at each exhibit, time between exhibits, and
to medium sized museum containing both archeological and art a list of presentations seen by that visitor on the mobile museum
exhibits located on the campus of the University of Haifa, was guide is available.
equipped with a radio frequency (RF)-based positioning system The group analysis view enables seeing the behaviour of a small
based on a wireless sensor network (WSN) (see [6] for details]. group of visitors arriving together. Many visitors arrive in small
Figure 1 shows the details of the positioning system. Beacons groups of family and friends, and it is important for curators to be
were statically located at entrances and exits, as well as near able to also understand group behaviour. The first view plots the
path of each member of the group on the map, similar to Figure 2,
with each member of the group having a different color. However,
this does not show the temporal aspect of the visit and even
though two lines are overlapping, this does not mean that visitors
were at the same exhibit at the same time. To understand the
temporal aspect of the group visit, we provide a timeline view of a
small group visit (Figure 3). This view is also available for a
single visitor’s visit. The view charts the time (starting from the
start time of the visit) on the x-axis, and each exhibition room on
the y-axis. Each visitor is depicted by a color, and time spent at
each exhibit within each exhibition room is shown. For example,
it is easy to see that visitors started visiting the museum together
and spent time at the same exhibition. At about minute 12:52, they
parted with the “red” visitor following the “blue” visitor. At the
Figure 2. Individual visitor’s path through the museum end of the visit, the visitors joined back in the first exhibition
room.
relevant locations of interest in the museum, while visitors carry The aggregated view shows overall visitor information per exhibit
small matchbox-size sensors called Blinds. When a Blind is in (Figure 4). It shows the information in a spatial view on top of the
proximity of a Beacon (determining location) that Blind reports
this information to the server, determining that the visitor was in
proximity to that known location. While providing a reasonable
indoor positioning solution, the system’s major weakness is that it
only knows when a person is in proximity to a Beacon, not being
able to detect positioning in transition from one Beacon to
another. Thus, the system provides sporadic rather than
continuous movement data. A research prototype of a location-
aware mobile museum guide was developed and then converted
into a working museum visitors’ guide. The guide was handed off
to regular museum visitors visiting the museum over a period of
10 months. Log data was gathered for analysis. A total of 423
Figure 5. Aggregate view showing heatmap of attraction
power at the museum.
1
http://mushecht.haifa.ac.il/Default_eng.aspx
Figure 3. Temporal view of small group visiting times in
each exhibition.
Figure 4. Aggregated view. Blue circles show number of visits at a location (attraction power), gray opaque circles show
amount of time in each location (holding power). In addition, several filters are available.
museum map. The blue circles show the percentage of visitors moderate holding power. Exhibits 5 and 6, show two points in the
visiting that location from all visitors at the museum (attracting main attraction of the museum – a 2400-year old ship extracted
power). The grey opaque circle, shows the average time spent at from the sea. Thus the high holding power of point 6, is not
that location (holding power). In addition, it is possible to filter surprising. Finally, location 7 shows the second floor. It can be
the data according to age range, sex or language used in the seen that very few visitors visit the second floor – a point for
mobile guide (the mobile guide supports 3 different languages – concern for the museum staff. Figure 5 shows the same view,
Hebrew, Arabic and English). The image can show various using a heatmap on the attraction power. In addition to the map
patterns of different behaviors at different exhibits. For example, view, the system shows the exact numbers for the average time
the location annotated with “1” is the entrance to the museum. spent and the percentage of visitors attending (holding and
Because explanations and initial use of the mobile guide was attracting power) of each exhibit using a simple bar chart (graph
performed there, the both attraction power (every visitor starts not shown here).
there) and holding power there are high. Locations 2 and 3 are at Finally, for providing overall information, the system shows the
eh corridor in which visitors go through to enter the museum. This distribution of visitors at the museum according to visitor hours
explains both the high attraction and holding power. Location 4 is using a stream graph [3]. Figure 6 shows for the distribution of
the main decision point of the museum where visitors decide visitors per hour of day at the museum. Each line color shows the
whether to go to the left exhibit, straight ahead, or up the stairs (to average number of visitors at a different exhibition room.
the right). That explains the high attraction power and relative Hovering over the line provides the name of the room and the
number of visitors at that hour. The overall width of the graph
shows the overall number of visitors at that time at the museum.
At the Hecht museum, opening hours are 10:00 to 16:00 (with
Tuesdays open till 18:00). Looking at the graph, we can see that
by far, 14:00 is the busiest time at the museum, with most visitors
vising between 13:00 and 15:00.
5. CONCLUSIONS
We presented a system that visualizes various visitors’ behaviors
at the museum. A curator can use the system to investigate what
happens at the museum by looking at the paths of individual
visitors, small groups of visitors, or general aggregated
information. We intend to evaluate the system by presenting it to
museum curators and museum personnel and conducting semi-
Figure 6. Distribution of visitors per hour at the different structured interviews. By receiving qualitative feedback, we hope
exhibition rooms to gain insight regarding the useful features and the general
usability and usefulness of the system. After receiving feedback, visit phases. Information Technology & Tourism, 15(1),
we plan to deploy the system at the Hecht museum for the actual pp.17-47.
use of the staff. [8] Lanir, J., Kuflik, T., Dim, E., Wecker, A.J. and Stock, O.,
2013. The influence of a location-aware mobile guide on
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