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Author's personal copy
Annals of Tourism Research, Vol. 38, No. 4, pp. 1594–1612, 2011
0160-7383/$ - see front matter 2011 Elsevier Ltd. All rights reserved.
Printed in Great Britain
www.elsevier.com/locate/atoures
doi:10.1016/j.annals.2011.02.007
HOTEL LOCATION AND TOURIST
ACTIVITY IN CITIES
Noam Shoval
The Hebrew University of Jerusalem, Israel
Bob McKercher
Erica Ng
The Hong Kong Polytechnic University, China
Amit Birenboim
The Hebrew University of Jerusalem, Israel
Abstract: A growing body of research is focusing on tourism in urban destinations. However,
there has been no research examining the impact of hotel location on subsequent tourist
behaviour. This article fills this gap both theoretically and empirically, through an analysis
of the time-space activity of tourists staying at four hotels in different areas of Hong Kong.
The movements of 557 tourists’ day-trips were tracked using GPS loggers. The study concluded
that hotel location has a profound impact on tourist movements, with a large share of the total
tourist time budget spent in the immediate vicinity of the hotel. Further, the study illustrated
the impact of geomorphic barriers on tourist movements. The findings have important
implications at both a destination and enterprise level. Keywords: hotel location, time-space
activity, Hong Kong, global positioning system. Ó 2011 Elsevier Ltd. All rights reserved
INTRODUCTION
Accommodation houses, including hotels, motels, hostels, guest
houses, bed and breakfasts, and other commercial enterprises (hereafter referred to by the generic descriptor—hotels) represent temporary
homes away from home for tourists, and as such, are the focal point
from which most tourism activity emanates in a destination
(Jansen-Verbecke, 1986). Tourists begin their day by leaving the hotel,
possibly return to the hotel intermittently during the day before engaging the destination once more, and finished their day by returning to
the hotel to sleep. Some research has examined the importance of
location in a hotel site selection, especially in urban destinations
(Begin, 2000; Dokmeci & Balta, 1999; Gutierrez, 1977; Hofmayer,
Noam Shoval is an Associate Professor and the Head of the Department of Geography of the
Hebrew University of Jerusalem (Jerusalem 91905, Israel. Email <noamshoval@huji.ac.il>). Bob
Mckercher is a Professor at the School of Hotel and Tourism Management of the Hong Kong
Polytechnic University. Erica Ng is a graduate student at the School of Hotel and Tourism
Management at the same university. Amit Birenboim is a graduate student at the Department of
Geography of the Hebrew University of Jerusalem.
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1986; Kot & Kowalczyk, 1997; Pearce, 1981; Urtasun & Gutierrez, 2006;
Wall, Dudycha, & Hutchinson, 1985), resulting in the creation of
several models of hotel location (Ashworth, 1989a; Egan and Nield,
2000; Ritter, 1986; Shoval, 2006; Yokeno, 1968).
Yet, virtually no research has examined the impact of hotel location
on subsequent tourist behavior. Intuitively, though, hotel location
should have a profound impact on tourist movements. Arbel and
Pizam (1977) argued nearly 40 years ago that most tourists wanted to
be within walking distance of major attractions and sought hotels that
were proximate to them. More recently, McKercher and Lau (2008)
discovered that 21% of all daytrips taken by independent, overnighting pleasure tourists in Hong Kong involved journeys of less than
500 m from the hotel. Yet, other than these studies, little or no other
empirical research has examined this issue explicitly.
This study tests the question of the importance of hotel location on
tourism movements, a topic that has previously not been explored in
the tourism literature. This is done through an inductive examination
of the movement patterns of independent pleasure tourists staying at
four different hotels in the Hong Kong Special Administrative Region.
Data were collected by using Global Positioning System (GPS) loggers
and analyzed using Geographical Information System (GIS) software.
The implementation of this methodology enabled the study team to collect high resolution data that led us to our main empirical finding, which
is, that hotel location has a profound impact on tourist movements.
Urban Tourism: Research Challenges and Emerging Technologies
It is somewhat ironic that urban tourism is one of the most popular
forms of tourism but among its least researched phenomena. Indeed,
the call for more and better research is a common theme much of
the literature written over the last 20 years. Ashworth’s (1989b) work
is seen by many as the beginning of research into urban tourism. His
central thesis was that urban centers were both the origins of most tourists and the destinations for many, but that most research tended to focus on non-urban areas and their resultant impacts. While the volume
of literature is growing, in his follow-up reflective piece (Ashworth,
2003) argued there was still insufficient research into various aspects
of the urban tourism phenomenon. Pearce (2001) also noted a general
increase in interest in this issue, with the phrase ‘‘urban tourism’’ entering the tourism lexicon. Yet, he too felt research was still in its early
stages, and that ‘‘there is still a considerable way to go in terms of developing a coherent corpus of work, pursuing common goals and carrying
out comparable studies (Pearce 2001, p. 928). He cites a number of
largely unexplored lines of inquiry including detailed examination of
tourists’ behavior as they arrive in and travel through cities, linkages
between tourist nodes and how such nodes interact. Most recently,
Ashworth and Page (2010, p. 7), discussing the paradoxes in urban tourism research, observe that ‘‘it is curious that very little attention has
been given to the questions about how tourists actually use cities.’’
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Tourism in urban areas is a spatially selective activity with tourist
nodes or precincts clustered unevenly throughout a city (Dredge,
1999; Pearce, 2001; Tunbridge & Ashworth, 1996). The number of
tourist nodes depends on both the size and geomorphology of the destination (Pearce, 1998). It is recognized that tourist nodes can be focused around icon attractions, shopping and business precincts or
anchored by hotels (Dredge, 1999). But even though tourism may be
perceived as a dominant force in such zones, in reality it may not be
the primary activity and tourists may not be the dominant user group
(Ashworth & Page, 2010). Beyond this generic knowledge, though, relatively little research has been conducted examining the spatial structure of tourism in cities at a neighborhood level (Pearce, 1999), with
Maitland’s (2008) work in London as the exception.
In a similar manner, while the desire to understand tourist movements has been of interest to researchers for many years (Dietvorst,
1995; Shaw, Agarwal, & Bull, 2000), little work has been done on this
subject in an urban setting. Instead most of the research has focused
on inter-destination travel (Oppermann, 1997; Van der Knaap,
1999). Lew and McKercher (2006) attempted to model urban tourist
movements by examining the interaction between territoriality (how
deeply an individual penetrates into a destination) and linearity (the
individual path taken). This interaction can produce a vast array of
movement patterns. This idea was followed up by an empirical study
by McKercher and Lau (2008) that used trip diaries to record movements. The study confirmed the importance of territoriality, but questioned the validity of linearity. This study, however, did not examine
the level of engagement nor did it seek to monitor the specific tracks
tourists followed within a destination.
Much of the lack of specific research can be traced to pragmatic
methodological challenges that have inhibited inquiry into these
issues. Traditionally, trip diaries and maps were used as the primary
data collection tool to gather information on tourist movements
(Thornton, Williams, & Shaw, 1997). This technique proved useful at
an inter-destination level (Oppermann, 1997), but as McKercher and
Lau (2010) discuss, it has a number of operational limitations at a
destination level. Scale issues and a tendency of respondents to identify
places visited or stops, but not routes taken results in a loss of fine
detail. Many trip diaries returned had incomplete information, resulting in many marginal data sets. Additionally, while the prospect of
completing a diary was met with initial enthusiasm, the actual
completion rate was low.
Lew and McKercher (2004) discuss the second key issue—data analysis. Collectively, tourists’ movements may follow recognizable patterns,
but individually, tourists’ movements may seem stochastic. Thus, what
on the surface seems to be a relatively simple task of mapping movements from point A to point B becomes the extremely complicated task
of documenting and then attempting to make sense of hundreds or
thousands of individual travel routes, some going directly from A to
B, some using different routes to make the trip and others stopping
at C, D or E. The issue is especially complicated in urban settings where
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the potential for nonlinear movements adds an extra layer of complexity (Pearce, 2001).
Emerging technologies have resolved both data collection and analysis problems, and in doing so have potentially revolutionized research
into tourist behavior in urban destinations. Global Positioning System
(GPS) loggers with an accuracy of at least +/ 10 m can track a tourist’s location as frequently as every second at every point on earth.
These instruments have resolved the data collection challenge by providing a level of fineness of detail that was previously unavailable.
Geographic Information System (GIS) software can analyze these high
resolution data quickly and efficiently, and importantly provide a range
of analytical options including tracking tourist movements, time analysis, space analysis and length of stay (Shoval & Isaacson, 2007). In recent years a growing body of work has demonstrated the efficacy and
the limitations of using tracking technologies to explore leisure and
tourist activities (for a review see: Shoval & Isaacson, 2010).
However, as with any emerging technology, tourism researchers are
still experimenting to determine the limits of its application. Much
of the research that implemented tracking technologies, up to this
date, tends to be rather descriptive and small scale. Some more sophisticated studies have been conducted, but they have bee tightly spatially
bound, for example, focusing on small historic cities (Modsching,
Kramer, Ten Hagen, & Gretzel, 2008; Shoval, 2008; van der Spek,
2008; Tchetchik, Fleischer, & Shoval, 2009), confined attractions like
theme parks and zoos (Russo, Clave, & Shoval, 2010; Zillinger,
2010), natural parks (Arrowsmith and Chhetri, 2003; Harder, Bro,
Tradisauskas, & Nielsen, 2008; Hovgesen, Bro, Tradisauskas, & Nielsen,
2008) and small Islands (Nielsen, Harder, Tradisauskas, and Blichfeldt
(2010); Xia, Zeephongsekul, & Arrowsmith, 2009). Each of these
locales has a clearly defined entry and exit point both making the selection of potential participants and the modeling of their movements an
easier task. Large, complex and multifunctional urban settings like
Hong Kong have not yet been investigated using GPS.
Hong Kong as a Pleasure Tourism Destination
This study took place in the Hong Kong Special Administrative
Region of China. During the recession year of 2009, some 29,590,000
arrivals were recorded, which represents and increase of 0.3% from
the preceding year. Some 16.9 million of these arrivals stayed overnight, with the balance representing same day tourists (HKTB,
2010). Expenditure associated with inbound tourism was about $20.5
billion in 2008 (HKTB, 2009), the most recent year when full data were
available. About 55% of tourists came for pleasure reasons, with 21%
for business, 19% to visit friends and relatives and the rest for other
reasons (HKTB, 2009). The average length is less than four nights
(HKTB, 2009). The Asian market tends to view Hong Kong as a short
break weekend destination, while the long haul market tends to regard
it as an intermediate stopover on the way to or from other main
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destinations. Australian tourists see it as a dual purpose destination: a
short break main destination and as a stopover for longer trips to
Europe.
Pearce (1998) described Paris as a polycentric destination. The same
descriptor should be applied to Hong Kong, for its tourism plant is
highly decentralized. The HKSAR covers an area of about 1,100 square
kilometers and consists of Hong Kong Island (81 sq km), Kowloon (47
sq km) and the New Territories and its 260 outlying islands occupying
975 sq km. The terrain is dominated by mountains with steep slopes,
with the small amount of flat land found mostly along the coastline.
Much of it is reclaimed land. As a result, the HKSAR has two distinct
central business districts, one located on Hong Kong Island and the
other in the Tsim Sha Tsui (TST) section of Kowloon, where a variety
of shopping, dining and accommodation opportunities exist.
On Hong Kong Island, the Peak Lookout node is accessible by funicular railway from the downtown core. Three major tourism nodes can
be found on the south side of the Island, including the Stanley Market
area, the Ocean Park theme park precinct and Aberdeen shopping and
dining node. Tsim Sha Tsui is the home to most museums, and a waterfront promenade where a nightly fireworks/laser light show is held.
Three additional nodes extend north from TST and include Jordan,
Yau Ma Tei and Mong Kok. Each offers a variety of shopping and dining opportunities, plus open air markets. Lantau Island to the west of
Hong Kong Island is the home to the region’s international airport, a
Disneyland theme park, a cable car to the world’s largest seated bronze
Buddha and other lesser cultural and heritage attractions. The New
Territories and outlying islands offer a variety of historic, cultural, nature-based and dining opportunities. Kowloon and Hong Kong Central
are connected by the famous Star Ferry, as well as subway lines and a
cross harbor tunnel. The most popular places visited in 2008 according
to the Hong Kong Tourist Board include: The peak lookout (47% of
Vacation Overnight Tourists); The Avenue of Stars promenade along
the Kowloon waterfront (37%); the open air Ladies Markets in Mong
Kok (34%); the two theme parks (27% and 25% of tourists each);
and the open-air Temple Street Market (20%) (HKTB, 2009).
METHODS
Data Collection and Analysis
This study tracked the day trip movements of tourists staying at one
of four hotels located in different areas of Hong Kong Island and
Kowloon. General Managers from each of the hotels were approached
initially and agreed to participate in the study. Data were collected at
one hotel from November 2008 to September 2009 that the other three
hotels from May through August 2009. Analysis of the movement patterns shows no seasonal variation, enabling the samples to be aggregated. Potential participants were approached in the hotel lobby
after breakfast and were asked if they wished to participate in the study.
To qualify, participants had to confirm that they were independent
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pleasure tourists (not on a package guided tour), were not departing
Hong Kong that day and were not planning on purchasing a full or half
day sightseeing tour. On acceptance, they were administered a simple
survey that gathered basic demographic and trip profile information.
Participants were then given a GPS logger (Figure 1) and instructed
to return the device to the front desk when they retired at the end
of the say. The device was programmed to record their location every
10 seconds.
A total of 791 tourists agreed to participate. Valid GPS sequences were
gathered from 557 (or 70.4% of the sample). A total of 234 sequences
were not used in the study due to technical problems with the devices
(i.e., the device was turned off or did not record the locations), or to
severe deficiency of GPS samples mainly as a result of the ‘‘urban canyon’’ effect caused by tall buildings obstructing triangulation with satellites. The approximately 30% failure/rejection rate falls within the
norms of similar studies conducted in highly congested urban settings,
but is higher than found in studies in open air theme parks. For example, Shoval (2008) had a 45% problematic track ratio in the densely
built historic city of Akko, while only 4% of tracks were discarded in a
tracking study in a theme park in Spain (Russo et al., 2010).
It is interesting to note that this study is the second such study
attempting to track tourists in Hong Kong through the use of electronic
devises. The earlier study was conducted in 2005 and tested the applicability of tracking tourists using a telephone tracking system offered commercially by a local provider. Trip diaries were used as the back-up data
collection method. This approach was met with substantive resistance
by potential participants, as reported in McKercher and Lau (2010). Indeed, potential respondents reacted so badly to the prospect of ‘Big
Brother’ being able to track their movements through their personal
phones that few agreed to participate. In the end, this method was
Figure 1. Three GPS Loggers
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abandoned and the trip diary method used exclusively. Such resistance
was not encountered with GPS devises and in fact, the opposite was true;
people were keen to participate once the system was explained to them.
GPS is still a fairly novel technology to many people that is only now
becoming more common on cars. Moreover, because people were physically handed a device, which was then collected at the end of the day,
they felt no personal invasion of privacy.
Data were analyzed using an ESRI’s ArcInfo 9.3 GIS (Geographical
Information System) commercial software. The utilization of such data
in a GIS platform permits both spatial and temporal analysis of movements. The central areas of Hong Kong and Kowloon were divided into
200 by 200 meter cells in order to facilitate ease of analysis and presentation of data. This approach enables intensity of activity in each cell to
be calculated in terms of both percent of total tourists and average
time spent. A similar grid analysis has been employed elsewhere (Kwan,
2000; Shoval, 2008) and has proven to be both an efficient and effective method to analyze the data.
The method, therefore, tracks the movements of an individual tourist during one day of his or her visit in Hong Kong. No attempt was
made to track movements over an individual’s entire stay, primarily
due to pragmatic considerations about the battery life of GPS loggers.
The data can be aggregated to develop a deeper understanding of
tourist movements during the middle days of the trip when they are
most likely to engage the destination most deeply. The study by
McKercher and Lau (2008) that examined movement patterns during
an entire visit found that day trips taken during the arrival and
departure days were confined disproportionately to the immediate
environs of the hotel due to either late arrival or anticipated early
departure. Trips taken during intermediate days were not so artificially
constrained. Moreover, the authors found no significant differences in
the likelihood to visit certain attractions or to travel to certain districts
by intermediate day of trip. Thus, aggregation of data is acceptable for
the type of analysis to be undertaken in this study.
Hotel Description
The location of the four participating hotels is shown in Figure 2.
Hotel A is an 819 room four-star hotel located on the edge of the
Tsim Sha Tsui tourist district. While the hotel is within walking
distance of the waterfront promenade, it is located more than one
kilometer from the main Nathan Road shopping area and about
2 km from the Star Ferry. As a result, the hotel offers a regular courtesy shuttle bus service to the Star Ferry terminal. Most guests take
advantage of this service. It is also the only hotel included in the study
to offer such a service. Hotel B is a 465 room four-star hotel located in
the heart of the Nathan Road shopping area and near the street
markets in the Jordan area of Kowloon.
Hotel C is a five-star 592 room luxury hotel located in the central
part of Hong Kong Island. It is the most up-market hotel included in
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Figure 2. Map of Hong Kong
the study. It is connected to an up-market shopping mall and is within
easy walking distance of most of Hong Kong Island’s major attractions,
including the Peak Tram funicular railway. Hotel D is a four-to-five star
810 hotel (depending on the rating body) located on the periphery of
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the central business district of Hong Kong Island in the Causeway Bay
district. It is located within walking distance of a number of shopping
centers and is about a five minute walk from the nearest subway station.
The rates for Hotels A, B and D are comparable. An Internet search in
April, 2010, listed the best available rates for each of these hotels at
about US $190 per night. The rates for Hotel C are much higher, with
the quoted Internet rate at about $310 per night.
RESULTS
The profile of participants is shown in Table 1. Most respondents live
in Western countries, with Australia and the UK being the two most
Table 1. Sample Description
Hotel B
Hotel C
Hotel D
Hotel A
Mean Std Dev Mean Std Dev Mean Std Dev Mean Std Dev
Nights in HK
5.20
3.21
4.33
2.62
5.92
12.41
5.12
4.12
n
%
n
%
n
%
n
%
Age group
18–25
26–35
36–45
46–55
56–65
66+
7
20
21
25
20
6
7.07
20.20
21.21
25.25
20.20
6.06
5
13
9
12
13
5
8.77
22.81
15.79
21.05
22.81
8.77
7
41
36
41
17
3
4.83
28.28
24.83
28.28
11.72
2.07
45
110
105
103
89
29
9.36
22.87
21.83
21.41
18.50
6.03
Education level
High-school or less
College
Postgraduate
24
57
18
24.24
57.58
18.18
10
23
24
17.54
40.35
42.11
28
74
40
19.31
51.03
27.59
121
255
95
25.37
53.46
19.92
Origin
Australia, New Zealand 49
Asia
0
UK
21
Europe
24
US, Canada
4
Other
2
49.00
0.00
21.00
24.00
4.00
2.00
11
7
7
2
29
2
22.92
14.58
14.58
4.17
60.42
4.17
12
28
64
13
20
8
8.28%
19.31
44.14
8.97
13.79
5.52
145
51
118
109
49
13
29.90
10.52
24.33
22.47
10.10
2.68
1
3
14
22
29
18
1.15
3.45
16.09
25.29
33.33
20.69
3
1
2
5
8
29
6.25
2.08
4.17
10.42
16.67
60.42
8
19
26
28
58
0.00
5.76
13.67
18.71
20.14
41.73
7
29
83
75
93
121
1.72
7.11
20.34
18.38
22.79
29.66
57
41
58.16
41.84
23
34
40.35
59.65
74
71
51.03
48.97
302
179
62.79
37.21
Income ($US)
Less Than 30,000
30,000–49,999
50,000–69,999
70,000–99,999
100,000 or more
Number of visit
First time visitors
Repeat Visitors
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common source markets. The bias towards Westerners is a function of
both the markets attracted to each hotel and the reluctance of
mainland Chinese tourists to participate in consumer surveys (Hui &
McKercher, 2001). Little variation was noted in the overall profile of
the tourists, with the exception of those staying in Hotel C. The typical
participant was between 36 and 55 years of age and traveling with his or
her spouse but not with children. A strong majority was university educated, and the sample had a median household income of over $70,000
per annum. Tourists at most hotels were somewhat more likely to be
first time tourists to Hong Kong, with a large minority repeat tourists.
The typical length of stay was between four and five nights. Participants
from Hotel C were generally more affluent than those from other hotels and were also more likely to be repeat tourists. The travel party was
also more likely to include two couples.
Spatial Patterns
The extent to which hotel location influences tourist movements is
explored in three different ways. Analysis begins by examining movement patterns in aggregate, followed by a study of the time of day when
tourists are most likely to visit different regions. Finally, an analysis of
the impact of distance on intensity of activity is conducted.
Figure 3 represents the aggregate time spent in each of the 20 by
200 m cells by tourists staying at each hotel, while Figure 4 illustrates
the average proportion of each cohort’s total non-hotel time budget
spent in individual districts. Figure 3 can be interpreted as follows.
The color reflects the percent of respondents in each cohort found
in each cell. Dark green cells indicate less than 7% of tourists were
found there. Light green cells represent visitation rates of 7% to
15%; yellow cells represent 16% to 30% of tourists; orange cells represent 31% to 60% of tourists; red cells indicate more than 60% of tourists. The height of the column represents the average length of stay in
each cell. As the reader will note, a strong correlation exists between
share and duration of visitation, indicating that tourists tended to both
concentrate their movements and time budget expenditures.
Three patterns common to all hotels can be observed. First, intensity
of use is clustered tightly around the immediate hinterland of the hotel. Second, the presence of the Hong Kong Harbor acts as a barrier
hindering movement from Hong Kong Island-based hotels to
Kowloon, in particular, and in a lesser degree from Kowloon-based
hotels to Hong Kong Island. Third, icon attraction nodes, such as
the Peak and Stanley Market attract a large share of tourists from each
hotel signifying their broad appeal, regardless of hotel proximity.
However, it is also evident that hotel location exerts a significant impact on how and where tourists engage the destination. Guests of Hotel
A clearly took advantage of the shuttle bus to the Star Ferry and concentrated their activities around this node. While a substantial proportion took the cross-harbor ferry, most did not spend much time in this
area. Instead, they transited through it on their way to and from icon
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Figure 3. Aggregate Time Spent in Cells Measuring 200 by 200 Meters
attractions on Hong Kong Island. They also demonstrated selectively
concentrated activities in Kowloon, spending much time in TST and
Mong Kong, but choosing to bypass the Jordan district, and a strong
preference for public transport (taxi, bus or subway) over walking. This
pattern contrasts sharply with the movements of guests from Hotel B
who demonstrated a much higher tendency to travel by foot up and
down the length of Nathan Road and its adjacent street markets. In this
instance, the hotel’s convenient location facilitates much heavier use of
this area than guests from the more peripherally located Hotel A.
Movement patterns for guests at Hotel D were constrained by its location on the periphery of the built up urban core of Hong Kong Island.
Again, a disproportionately large share of the total time budget was
spent in the immediate environs of this hotel. When guests ventured
from the hotel, they were likely to visit the Ocean Park tourist node
on the South side of Hong Kong Island, Central Hong Kong or take
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30%
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Hotel A
Hotel B
25%
Hotel C
Hotel D
20%
15%
10%
5%
0%
Figure 4. Average Proportion of Time Budget Spent in Individual Districts
the ferry to Kowloon. However, they did not explore Kowloon deeply.
Guests at Hotel C demonstrated some rather unusual movement patterns. Like others, much of their daily activity was concentrated in
the vicinity of the hotel and on Hong Kong Island. However, unlike
guests of Hotel D they tended to venture more widely in both Hong
Kong Island and Kowloon. In particular, they were far more likely than
guests of Hotel D to visit Mong Kok and also were far more likely to go
to the TST waterfront (Avenue of Stars).
The extent of the impact of hotel location is shown more graphically
in Figure 4. Guests staying in hotels located on the Kowloon side of
Hong Kong spent a majority of their time here and tended to divide
the rest of their time budget almost equally between the Central Hong
Kong and other locations. Those guests staying on Hong Kong Island
showed a strong preference for Central and other locales and an
equally strong aversion to Kowloon. In particular, guests at the relatively peripherally located Hotel D spent a majority of their time in
Central.
Tourists staying at the four hotels spend about the same amount of
their daily time budget at Stanley, the Peak and Central Hong Kong.
Guests at Hotel B spend more than one quarter of their total time
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budget in the immediate vicinity of their hotel, while Causeway Bay and
Admiralty consume a disproportionately large share of the total daily
time spent outside of the hotel by guests at Hotels C and D, respectively. Interestingly, the impact of the shuttle bus service on the time
budget of guests from Hotel A become self-evident, as the TST south
node consumes more than 15% of their total daily time budget. Causeway Bay is an appealing to guests from hotels other than Hotel D,
Admiralty holds little interest to guests from other hotels than Hotel
C, while Yau Ma Tei is of little appeal to guests not staying at Hotel
B. Mong Kok has some appeal for guests from Hotels A, B and D
who can access it easily by foot or direct subway line, but seems to be
of little interest to guests from the Hotel D who must make a subway
transfer to reach there.
Temporal Patterns
GPS data also enable an analysis of time of day when tourists are
most likely to visit specific attractions. Figure 5 shows the time of day
when tourists from each of the hotels are most likely to visit popular
locales. Figure 5a shows the intensity of visitation to the Peak. Tourists
staying at hotels on Hong Kong Island are both more likely to visit in
general and more likely to visit it earlier in the day than those staying in
Kowloon. Guests at Hotel C, which is located less than 500 m from the
start of the Peak Tram Railway visit earliest in the day, with many
appearing to make it their first stop. Guests at Hotel C are more likely
to visit over the lunch time period, while those guests staying in
Kowloon-based hotels are more likely to visit during mid to late afternoon. Interestingly, guests from the Hotels B and C rarely visit in the
evening (for reasons that will become clear), while those from Hotels
A and D demonstrated a secondary peak after 6 PM.
By contrast, Figure 5b shows both the intensity and time of day when
people are most likely to visit the Street market area in Kowloon. Again,
Figure 5a. Average Time Spent in The Peak by the Hour of the Day
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Figure 5b. Average Time Spent in Mongkok by the Hour of the Day
tourists staying at the more proximate hotels are more likely to visit earlier in the day, with visitation by guests from the nearby Hotel B peaking at noon. Two peaks in mid afternoon are evident from guests
staying at Hotel A. Interestingly, guests from Hotel C are most likely
to visit late in the afternoon, after five o’clock, with secondary peaks
at about 7 PM and 10 PM. The visitation patterns evident in Figures
5a and 5b suggest that these tourists spend the day on Hong Kong
Island and then venture into Kowloon during the evening. Guests
staying at Hotel D rarely visit this district, primarily because access is
inconvenient.
Finally, it is possible to calculate the relationship between time spent
in any one place and distance from the hotel. Distance decay has been
demonstrated to affect tourism demand on a macro scale. McKercher,
Chan, and Lam (2008) illustrated that aggregate global tourism demand declines by about 50% with every thousand kilometers traveled
from an international border, while mean demand for any destination
declines at an even faster rate. Three broad distance decay patterns can
be identified (McKercher & Lew, 2003): a standard decay curve where
demand falls geometrically at or near the origin point; a plateauing demand curve, where demand stays stable over an extended period before declining rapidly; and a two-tailed curve where demand peaks
near the source, falls rapidly before a secondary peak is noted. The impact of an Effective Tourism Exclusion Zone where little or no tourism
activity occurs can have the impact of accentuating or delaying the onset of decay.
If distance decay is a universal law, then a similar decay curve should
be observed at a local destination scale. Figure 6 tests this proposition
by illustrating the proportion of the total daily time budget allocated by
distance from the hotel. Distance is calculated in increments of one
kilometer from the hotel location. A clearly defined traditional
distance decay curve is noted for guests from Hotel A and B. The
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35.0
30.0
sp
Percent of time
ent
40.0
A
25.0
20.0
15.0
10.0
5.0
0.0
B
C
D
1
2
3
4
Distance
5
6
7
(km) fro
m hotel
8
9
10
Figure 6. The Proportion of Total Daily Time Budget Allocated by Distance
from Hotel
difference is that demand peaks in the immediate environs of Hotel B,
as one would expect from its central location, while the impact of the
shuttle bus taking tourists to the Star Ferry explains the shift of demand at Hotel A. The demand curve for guests from Hotel C represents a typical demand curve with a large tail. About 40% of total
time spent away from the hotel is restricted to a two km radius, but
there is a large tail represented by trips to Mong Kok. Guests at Hotel
D demonstrate a plateauing demand curve, with much time spent in
the immediate environs of the hotel, followed by a low demand zone
that corresponds with the tourism attraction for aid between the hotel
and the central business district.
CONCLUSION
This study examined the impact of hotel location on tourist movements, using data collected from GPS receivers and analyzing them
using GIS software. Hotel location and its impact on subsequent tourism movements is a neglected area of research, yet as this study illustrates, has a profound impact. Icon attractions and iconic tourism
nodes seem to have the ability to draw tourists regardless of the hotel
location. However, visitation to other tourist nodes within the city is
influenced strongly by the location of the hotel, with the volume of
tourists further moderated by the presence of geomorphological barriers. Moreover, the study suggests that distance decay is applicable at a
micro as well as a macro level.
The study findings make a number of practical, theoretical and
methodological contributions to tourism research. At a practical level,
they highlight the importance of hotel location as a critical factor influencing consumption patterns within a destination, with broad implications for both product development and destination marketing. While
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it is recognized that tourism is a spatially selective activity, this study
illustrates the spatial selectivity is driven largely by hotel location. Thus,
apart from icon attractions, recognized tourism precincts may be of
greater importance to guests from certain hotels and largely irrelevant
to guests from other hotels, depending on ease of access and distance.
Destinations targeting activities and especially in-hotel promotional
activities, therefore, need to be cognizant of these factors. Small and
medium-size enterprises, in particular, as well as local tourism marketing and business promotional organizations can generate greater benefits by targeting hotels in their immediate environs, even if they are
smaller, in larger hotels located elsewhere.
At a conceptual level, this study highlights the impact of distance on
tourist movements at a micro level within a destination. It also contributes significantly to addressing Pearce’s (1999) desire to understand
tourism as a localized, or neighborhoods scale. The study also has
broad implications to model tourism movements, urban destination
planning and development. Lew and McKercher (2004) have suggested that urban tourist flows clearly have a tendency to spread themselves unevenly, both spatially and temporally. This study also explain
while more popular sites and access routes often suffer from overcrowding and while others are under-utilized. This state of affairs highlights the inefficient use of economic and social resources and one that
may be ultimately unsustainable.
Additionally, it demonstrates clearly the impact that hotel location
has on tourist movements and the need, therefore, to consider it in
any analysis of urban tourist behaviour. Data on within-destination visitation tends to disregard hotel location as a mitigating factor, primarily because these type of data have not been collected before. Yet this
study illustrates clearly that hotel location plays a critical role on at least
four aspects of tourist behaviour: spatially concentrated activity around
the hotel; places tourists are likely or unlikely to visit; volume of visitors
at all but icon attractions and; diurnal visitation patterns. Understanding the implications of hotel location on both tourists’ physical and
temporal movement patterns can assist the development of tourist
management schemes and new tourist nodes to maneuver tourists in
a more rational way.
Finally, the study demonstrated the importance and validity of adopting innovative research methods to gain deeper insights into tourist
behavior. Methodologically, the study made use of advanced tracking
technologies to explore tourist activity. In recent years the use of such
technologies is becoming more popular. The study illustrated how the
use of GPS devices can provide valuable temporal and spatial information that can better inform tourism studies. Moreover, the level of detail combined with the relatively low data collection cost and ease of
analysis make it an appropriate, complementary tool for tourism
research.
This method can assist a range of tourism stakeholders, including
Destination Management Organizations (DMOs), to inform them
about the activity of tourists in their destination. The method is easy
to replicate and cost effective, GPS cost about $40 and GIS software
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is not expensive. The authors’ experience was that hotel GMs were generally enthusiastic about this method, as it was both innovative and
non-intrusive. Moreover, they could gain site specific movement data
which could make the delivery of services more effective.
Organizations ranging from hotels to DMOs can use these data to
gather a more comprehensive understanding of tourist movements
through an entire destination region, and not just visitation levels to
specific attractions listed on a tourist survey. In doing so, they can gain
insights into which, if any, attractions tourists visit that may not be the
focus of marketing activities, and which places they do not visit that are
promoted. For example, this study could be of high relevance to local
planning agencies when planning future hotel development in the city.
It is clear that the location of the hotel have a direct and substantial
impact on the consumption patterns of the tourists.
At the outset, the study tests the question of the importance of hotel
location on tourism movements, a topic that has previously not been
explored in the tourism literature. The study concluded that a hotel
location has a profound impact and must be considered in future studies examining consumption patterns and visitation levels to a wide
range of tourism products, attractions and services.
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