When and Where Tourists are Viewing Exhibitions:
Toward Sophistication of GPS-Assisted Tourist Activity
Surveys
Junya Kawase, Yohei Kurata and Naoto Yabe
Department Tourism Science
Tokyo Metropolitan University, Japanj.kawase0922@gmail.com[ykurata, yabebeya]@tmu.ac.jp

Abstract
In recent years, many tourist activity surveys using GPS devices have been conducted, but thesurvey methodology needs still to be improved. One critical problem is that we can know
where tourists visit and how long they stay there, but not what they actually do there. Thus, weinvestigated the relationship between tourists’ activities recoded by a video camera and their
GPS logs. The result shows that the location history of a tourist itself is not sufficient for
estimating how long he/she enjoys each attraction, and his/her walking speed seems critical forthis estimation.
Keywords: GPS, tourist activity survey, zoological park, logistic regression model

1 Introduction
Global Positioning System (GPS) provides accurate, continuous, worldwide, threedimensional position and velocity information to users with appropriate receiving
equipment (Kaplan, 1996). GPS expands the possibility of tourist activity surveys,
because with an effective use of GPS devices we can investigate where tourists visitand how long they stay there, without recording their activities manually. As a matterof course, there are also some drawbacks; first of all, GPS receivers work properly
only in outdoor environments. Measurement error and battery lifetime are anotherissues that need to be considered. In tourist activity surveys, however, there remains
one more critical issue—GPS logs tell where tourists have been to, but not what they
have been doing there.
Tourism is an activity that involves a large variety of activities. Thus, for discussing
marketing strategies, renovating tourist spaces, and providing appropriate information
for tourists, it is essential to clarify tourists’ behaviours in conjunction with their
attributes such as age and gender. For promoting tourist activity surveys to know
tourists’ behaviours, an intelligent technique for inferring tourist activities from their
GPS logs will be highly desirable.
As a first step toward this technique, we investigated the relationship between the
tourists’ activities and their GPS logs, focusing on the simplest activity of tourism—viewing something. Then, we developed a statistical model with which we can discernfrom a GPS log whether a tourist had been viewing exhibitions or not at each moment.
This model can be applied to GPS logs in previous surveys to estimate the time spentby tourists for viewing each exhibition and eventually, to estimate the attractivenessof each exhibition.
The remainder of this paper is organized as follows: Section 2 explains the
background of this research. Section 3 reports our initial GPS-assisted survey at azoological park and points out its problems. Section 4 describes our supplementary
experiment that aims at developing a new technique for estimating tourists’ viewing
time. Finally, Section 5 concludes with a discussion of future work.

2 Background
Recently, GPS loggers have become smaller, lighter, and less expensive. In addition,
mobile communication devices equipped with a GPS sensor, such as smartphones,
have become rapidly widespread all over the world. Along with this trend, activity
surveys using GPS devices have been conducted in various fields (Yabe et al., 2010).
Especially in tourism studies, location history data recorded by GPS devices arehighly useful to clarify what attractions tourists have visited in what order and how
long they have stayed there (Kurata et al., 2010). If tourists’ spatio-temporal
behaviours are well-understood, it would be possible to optimize transportation,
operations of tourist attractions, and marketing strategies, all in line with their actual
needs (Shoval and Issacson, 2007a). Of course, such information can be obtained
from a questionnaire or an observation survey, but usually such surveys are costly anddifficult to be conducted regularly for monitoring tourists’ behaviours. On the other
hand, GPS-assisted surveys impose almost no burden on tourists and are relatively
inexpensive if GPS devices are used repeatedly (Kurata et al., 2010).
GPS-assisted activity surveys have been conducted extensively in transportation
studies of motor vehicles. One of its reasons is that it is easy to install GPS devices on
vehicles (Shoval and Issacson, 2007a). For example, Nagao et al. (2004) analyzed
tourists’ movement patterns in a macro scale based on the data of GPS loggers
installed on rental vehicles. Some GPS-assisted tourist activity surveys targetedpedestrians to clarify their movement patterns and characteristics (Asakura and Hato,
2004; Asakura and Iryo, 2007; Shoval and Issacson, 2007b), as well as the spatial use
of a city by tourists (Shoval, 2008). Some surveys attempt to extract interesting
locations for tourists from their GPS logs (Zheng et al., 2011). However, it still
remains as a research challenge to develop a technique for inferring tourists’ activities
from their GPS logs, while it is important to know what tourists actually do at eachplace in order to optimize various operations in a tourist area.

3 GPS-Assisted Survey in Tama Zoological ParkAs an example of GPS-assisted tourist activity surveys, we report our survey at Tama
Zoological Park (Hino, Tokyo) on 3th (Fri) and 4th (Sat) of September, 2010. Thereare three advantages in conducting a GPS-assisted survey in a zoological park: (i)most exhibitions are located outside, (ii) the number of its entrances and exits, where
GPS loggers should be distributed and collected, are limited, and (iii) the time spentby each visitor does not exceed the battery lifetime of GPS loggers. In addition, in
Tama Zoological Park, animal exhibitions are located separately and thus, we can
expect clear difference between visitors’ viewing and walking states. Fig. 1 shows a
map of Tama Zoological Park. The plan is complicated due to its hilly location. This
is why its staff has wanted to know the visitors’ movement patterns in the park.

Fig. 1. A map of Tama Zoological Park
Our survey was conducted as follows: First, we stood at the main gate and distributed
GPS loggers to the visitors who agreed to participate in our survey. When the visitors
came back to the same place to leave the park, we collected the loggers from themand asked them to answer a questionnaire to obtain their attribute data, such as age,gender, and accompanying persons.
The GPS loggers we used were Qstarz Black Gold 1300 (Fig.2), whose weight was
only 22g. We asked the visitors to put the GPS loggers on their necks, bags, baby
carriages, or wherever they like. Each GPS logger was set to record the geocoordinates of its location once in every second.

Fig. 2. A GPS logger used for our survey (Qstarz Black Gold 1300)
We obtained 190 valid sets of GPS logs and visitors’ attribute data. The GPS logs
were refined by removing error points. In this study ‘error points’ were defined as
those that refer to a location more than three meters away from park aisles, because
the possible horizontal error of our GPS loggers was three meters (catalogue value).
From the refined data, we first calculated each visitor’s staying time at each animal
exhibition (i.e., how long each visitor had spent there) by counting the number ofpoints located within three meters from the boundary line between the exhibition andthe aisle (Fig. 3) in ArcGIS 9.1. This is because we assumed that the visitors viewed
animals just in front of each exhibition and the loggers’ possible measurement error
was three meters.

Walking
direction

Fig. 3. A sample GPS log, together with an area within three meters from theboundary between Flamingo exhibition and an aisle

Table 1 shows ten top animal exhibitions in terms of average staying time. In addition,
Table 2 shows the rankings of animal exhibitions by generations. The exhibition
where all generations of people spend the longest time was Insect Museum. Children,
especially boys, were caught by this exhibition (Table 2). Insect Museum has a long
fixed route in it and accordingly, visitors who went into this museum naturally has
spent long time there.
In general, most of Asian animals were not viewed for long time (Table 1). One ofzoo staff commented that many visitors wanted to see African and Australian animals,
while Asian animals were commonplace for them.
Our result shows that the animal exhibitions people visited, as well as the time theyspent there, were quite different by their age, gender and accompanying persons. For
instance, we found that people in their 40s spent more time in front of Japanesemonkeys and Chimpanzees than those in their 20s, 30s, and 50s (Table 2). This isprobably because they were typically interested in monkeys’ sociality. Data about
which ages of people are interested in each exhibition is suggestive for providing
appropriate information through guide boards at the exhibition.
Another unique finding was that families with small children actually stayed longer
time in cafeterias than in animal exhibitions.

Table 1. Ten top animal exhibitions in terms of average staying time
Rank

Animal Exhibition

Staying time1

Insect Museum

11’23”

2

Lions

8’06”

3

Giraffes & Zebras

6’15”

4

Japanese Monkeys

4’30”

5

African Elephants

4’08”

6

Chimpanzees

3’49”

7

Walk-in Bird Cage

3’45”

8

Siberian Tigers & European Grey Wolves

3’03”

9

Orang-utans

2’30”

10

Asian Elephants

2’23”Table 2. Ten top animal exhibitions in terms of average staying times by generations
Rank

10s

20s

30s

40s

50s

1

InsectMuseum25’49”

InsectMuseum8’45"

InsectMuseum10’16”

InsectMuseum12’21”

InsectMuseum7’23”

2Japanese
Monkeys
20’36”

Giraffes &Zebras
5’35”

Lions
8’16”

Lions
7’56”

Wolves &Tigers7’13”

3

Giraffes &Zebras
6’20”

Lion
4’53”

Giraffes &Zebras
7’10”

Japanese
Monkeys
6’03”

Giraffes &Zebras
6’54”

4Brown Bears
5’18”

Chimpanzees
3’06”

Walk-in Bird
Cage
4’56”

Giraffes &Zebras
5’49”

Lions
4’03”

5

Lions
5”11’

Walk-in Bird
Cage
2”47’

Chimpanzees
4’23”

Wolves &Tigers
4’47”

Japanese
Monkeys
3’53”

We also calculated the percentage of visitors who visited each animal exhibition. Here
we assumed that a visitor had ‘visited’ an exhibition if he/she spent more than fiveseconds there. Table 3 shows the ten top highly-visited exhibitions. The result
indicates that animal exhibitions along major corridors and those with large cages
were visited by a larger number of people. However, we also found that higher
percentage of visits did not necessarily mean longer average staying time (Table 1).
Table 3. Ten top highly-visited animal exhibitions
Rank

Animal exhibition

Percentage

1

Giraffes & Zebras

88%

2

Lions

87%

3

Oriental Storks

73%

4

African Elephants

69%

5Great Indian Rhinoceroses

68%

6

Siberian Tigers & European Grey Wolves

66%

7

Raptors

66%

8

Orang-utans

63%

9Parma Wallabies & Wombats

62%

10

Reindeers & Emus

62%

In this way, we successfully clarified the spatio-temporal behaviour of park visitors
based on a GPS-assisted activity survey. We, however, considered only ‘staying time’,but not ‘viewing time’. Viewing time is a direct measure for evaluating theattractiveness of each exhibition. On the other hand, staying time is easily affected byother factors. For instance, one of zoo staff pointed out the possibility that visitors had
spent time resting on a bench without viewing an exhibition nearby (e.g.,chimpanzees), and also the possibility that the staying time becomes inevitably longer
if the exhibition has a long aisle along it (e.g., giraffes).
Our assumption that people viewed animals just in front of exhibitions was alsoquestionable, because large animals can be viewed from more than three meters away,
while small animals cannot be seen even in front of the exhibition when they arehiding. Thus, in order to judge visitors’ viewing states from their GPS logs, we
decided to look into additional available data in GPS logs; that is, walking speed.

4 Relationship between Tourist’s State and Walking Speed
We conducted a supplementary experiment to investigate the relationship between
people’s activity (whether viewing or not) and his/her walking speed. This experiment
was conducted from 15th of May (Sun) to 28th of June (Tue), 2011 in the same Tama
Zoological Park. At this moment we employed five undergraduate and graduate
students (three males and two females) as subjects. All of them are in the early 20s.
In this experiment, the subjects were asked to go around a zoological park, carrying a
GPS logger. In addition, our staff followed each subject and filmed the subject’s
activity by a video camera. From this video we made the subject’s activity log, which
recorded precisely when the subject had been viewing an animal exhibition. The GPSlog and activity log of each tourist were then matched. Note that the GPS logs wererefined by removing the points recorded when the subject had been in a bus or anindoor exhibition.
Fig. 4 shows a sample GPS log with the subject’s activity record. It seems that the
subject has walked into the cage of Flamingo, but this is actually not (recall that ourGPS logger has a measurement error). The figure also shows that the subject started
viewing an animal exhibition even from more than three meters away when he isapproaching the exhibition. Considering that the possible horizontal error of our GPS
loggers is three meters, this indicates that our previous assumption that people view
an animal exhibition just in front of it seems not appropriate.

Walking
direction

Fig. 4. A sample GPS log together with the subject’s activity record
We then calculated the probability of viewing (i.e., the probability that people view ananimal exhibition) in relation to walking speed, as the relative frequency of viewing
states at each speed (every 0.1km/h). The resulting graph (Fig. 5) clearly shows alinear inverse relation between walking speed and the probability of viewing; that is,
the probability of viewing increases as people walk more slowly, but even while notmoving he is not always viewing an exhibition.

Fig. 5. A relation between probability of viewing and walking speed

From the GPS logs with the record of the subjects’ activities, we built a logistic
regression model in Eq. 1, with which we can estimate from walking speed whether
people are viewing an exhibition or not. In Eq. 1, Pi means the probability of viewing
at point i, and vi means walking speed at point i. When applied to the GPS logs
obtained in our experiment, this model correctly discerned the subjects’ viewing
states over 68.4 percent of the entire logs. On the other hand, if the previous rule (i.e.,
we regard people as viewing exhibitions if they are within three meters from theexhibition) is applied to the same GPS logs, the success rate drops down to 56.7%.
From this fact, we can conclude that the time people have spent in front of an
exhibition is not sufficient to estimate the time they have viewed it.

logitPi   0.6194  0.3237vi
Estimate

Standard deviation

Chi-squared

p-value

0.61940.0226752.1810

0.0000-0.32370.0105948.5306

0.0000(1)Exp(estimate)0.7235The logistic regression model in Eq. 1 can be applied to the GPS logs recorded in our
initial survey. For example, Fig. 6 shows the GPS log of a visitor in our initial survey,
together with the presumption of his viewing state. In this case, he probably viewed
the exhibition only first half of his stay in front of this exhibition. By counting thenumber of these points where he is presumed viewing an exhibition, we can estimate
his viewing time on this exhibition, as each point corresponds to one second. Indeed,by repeating this process for everybody, we calculated the average of estimatedviewing time for each animal.
For the calculation of average estimated viewing time, we used the GPS logs of 26
visitors who were in their 20s and accompanied by a single person, because the modelin Eq. 1 was developed based on the data of subjects in their 20s followed by a staff.
In addition, we assumed that each subject views the nearest animal exhibition when
the subject was presumed viewing something.

Walking
direction

Fig. 6. A sample GPS log with the visitor’s presumed viewing states at each location
Table 4 compares two rankings of animal exhibitions: the left one features averagestaying time, which was calculated by our previous method (i.e., counting the numberof log points within three meters from the boundary between an exhibition and an
aisle), while the right one features average viewing time estimated by our new method.
The result shows several differences between staying time and viewing time. First, theviewing time of lions (5’08”) is much shorter than its staying time (7’20”). This isbecause the lions’ exhibition has a long aisle along it and accordingly, people havespent long time for walking the aisle often without viewing the lions. The same reason
applies to Insect museum which has long fixed route in it. On the other hand, theviewing time of wolves and tigers (4’41”) is estimated much longer than its staying
time (2’56”). This is probably because many visitors have viewed these animals froma distance, particularly the space under trees, rather than the area near their cages.
In sum, in order to evaluate how much people are involved in each exhibition, it isbetter to consider not only their location, but also their moving speed. In addition,other elements such as distance and angle to each exhibition may work as additional
clues for discerning whether people view it or not. Thus, we are currently working on
the refinement of our logistic regression model by considering additional parameters.
In addition, we are currently conducting an additional experiment with non-student
subjects (e.g., families with children) to expand the target of our model.

Table 4. Two rankings of animal exhibitions in terms of average staying and viewing
times, estimated from the GPS logs of 26 visitors in 20s coming with another person
Animal Exhibition

StayingTime

Rank

Animal Exhibition

Viewing
Time

1

Lions

7’20”

1

Lions

5’08”

2

Insect Museum

5’43”

2

Giraffes & Zebras

4’43”

3

Giraffes & Zebras

5’14”

3

Wolves & Tigers

4’41”

4

Japanese Monkeys

4’06”

4

Insect Museum

4’34”

5

Chimpanzees

3’09”

5

Japanese Monkeys

3’40”

Rank

5 Conclusion
The result of our experiment shows that not only the location history of visitors, butalso their walking speed is essential for judging their viewing states and eventually forevaluating the exhibition’s attractiveness. The information about exhibitions’attractiveness will be important for considering how to improve exhibitions, spatial
designs, and tourist information of the zoological park. For instance, animal
exhibitions which small children like can be be relocated near the entrance, because
children cannot walk long distance. On the other hand, animal exhibitions which
attract adults rather than children may better be equipped with an adult-oriented guideboard that explains the social aspect of the animal, etc.
In this work, the subjects are limited to students in their 20s. To increase generality,
we are currently conducting a similar experiment where the subjects are generalvisitors accompanying small children, as they are the main target of zoological parks.
In addition, we are refining our model by adding other parameters than location andwalking speed, such as acceleration and walking angle to the exhibition.
In future work, we will refine the model, such that we can discern a larger variety of
tourists’ activities. In addition, we will conduct similar surveys in other sorts of tourist
facilitates, such as amusement parks and open-air museums, and examine whether our
technique can be applied generally to these facilities.

References
Asakura, Y. and Hato, E. (2004). Tracking survey for individual travel behaviour using mobile
communication instruments. Transportation Research Part C: Emerging Technologies
12(3-4): 273-291.

Asakura, Y. and Iryo, T. (2007). Analysis of tourist behaviour based on the tracking datacollected using a mobile communication instrument. Transportation Research Part A:
Policy and Practice 41(7): 684-690.
Kaplan, E D. (1996). Understanding GPS, Principles and Applications. Artech House.
Kurata, Y., Yabe, N., Komaki, N., Arima, T., and Sugimoto, K. (2010). What, When, and How
Much Do They See, and What Are They Interested In?: Toward More Detailed Survey
on Inbound Tourists’ Behaviors. Research Conference of Society for Tourism
Informatics, 43-48 (in Japanese).
Nagao, M., Kawamura, H., Yamamoto,M., and Ohuchi, A.(2004). Acquisition of Personal
Tourism Activity Information Based on GPS Log Mining Method. Fifth Asia-PacificIndustrial Engineering and Management Systems Conference, CD-ROM.
Shoval, N. (2008). Tracking Technologies and Urban Analysis. Cities 25(1): 21-28.
Shoval, N. and Isaacson, M. (2007a). Tracking Tourist in the Digital Age. Annals of Tourism
Research 34(2): 141-159.
Shoval, N. and Isaacson, M. (2007b). Sequence Alignment as a Method for Human Activity
Analysis. Annals of the Association of American Geographers 97(2): 282-297.
Yabe, N., Arima, T., Okamura, Y., and Kadono, A. (2010). An Agenda on Tourist Activity
Survey Using GPS and Investigation of Analysis Methods. International Journal of
Tourism Science 3: 17-30 (in Japanese).
Zheng, Y., Li, Y., Zha, Z., and Chua, T. (2011). Mining Travel Patterns from GPS-Tagged
Photos. Lee, K., Tsai, W., Liao, H., Chen, T., Hsieh, J., and Tseng, C. (Eds.) Advancesin Multimedia Modelling, Lecture Notes in Computer Science 6523: 262-272, Springer.

Acknowledgements
We appreciate the visitors to Tama Zoological Park who participated in our surveys,
as well as its staff who kindly helped our surveys and gave valuable comments to us.
This work was supported by JST RISTEX’s research program on Needs-oriented
Service Science, Management and Engineering (project title: “Architecting service
with customer participation based on the analysis of customer experience and design
processes: sophisticating tour design processes as a case study”) and Tokyo
Metropolitan University’s internal research fund.