663346 research-article2016
JAC0010.1177/1478077116663346International Journal of Arch 10.1177/1478077116663346 itectural ComputingHyun et al.
Article
Investigating cultural uniqueness in theme parks through finding relationships between visual integration of visitor traffics and capacity of service facilities
International Journal of Architectural Computing 2016, Vol. 14(3) 247–254 © The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1478077116663346 jac.sagepub.com
Kyung Hoon Hyun, Aram Min, Sun-Joong Kim and Ji-Hyun Lee
Abstract The goal of the article is to find the relationships between theme park visitor traffics and service facility location along with their capacities. To do that, we analyzed four Disneylands situated in Paris, Tokyo, and the United States (Florida and California). By analyzing the visual integration of visitor traffics at each Disneyland and calculating the capacities of service facilities such as the attractions, shops, and restaurants, we ran through a linear and a geographically weighted regression analysis. Our results indicate that there is a unique relationship between the service facility placements and the amount of predicted traffic flows for each Disneyland.
Keywords Theme park localization, cultural uniqueness, service facility placement, visitor traffic, visibility analysis
Introduction Despite the globalization tendency of consumer habits, theme park industries that are transnational with a strong design identity and similarities are increasingly taking the specificity of the locals into consideration as a key element for their competitiveness. As claimed by Anton Clavé,1 cultural and language differences that exist between places cannot be overlooked. Moreover, Norcliffe2 suggests that theme parks may be a progressive interpenetration between the global and the local. This relationship between the global and the local is especially true when designing a theme park with a cultural theme or taking root in the local identity. Adapting the local identity by transnational corporations such as Disneyland is emphasized and investigated in several cases, in particular by the Disneylands situated in Asia. In the case of Disneyland Tokyo, Tobin3 GSCT, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea Corresponding author: Ji-Hyun Lee, GSCT, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-Ro, Yuseong-Gu, Daejeon 34141, Republic of Korea. Email:
[email protected]
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argued that Disneyland Tokyo does its best to emphasize contextualized cultural description over decontextualized grand theory thereby adopting the local culture. For example, the reason for Disneyland was exported to Japanese, Administrators of Disneyland Japan wanted to have a duplicate version of Disneyland in the United States. However, due to the different cultural norm of not eating while walking, restaurant size and seating area were expanded in Disneyland Tokyo.4 On the other hand, in the case of Hong Kong Disneyland, Fung and Lee5 point out that Hong Kong Disneyland deliberately keeps the global form in response to the characteristics of the Chinese market. Although it may appear not to consider the locals, the strategy that Hong Kong Disneyland practiced may be regarded as one of the ways to adopt the local culture. In summation, theme parks situated in the particular area will have evolved to fit the local needs or the frequent visitors’ needs for the optimization of service and consumption. Thus, each theme park will show a cultural difference in their design depending on the cultural context they are in. This difference is expected to be particularly true regarding service facility operation systems because they are the primary actors that adjust to meet the needs of the consuming pattern of the visitors. In this article, we focus on the visitor traffics in relation to the distribution of service facilities (attractions, restaurants, and shops) for four theme parks in three different countries to find culturally unique characteristics. Therefore, the relationships between the amount of visitor traffic and the locational distribution including the capacities of attractions, restaurants, and shops in a theme park were investigated. For the experiment, we conducted the following tasks: (1) gathered theme park blueprint data, (2) visual integration of visitor traffic, (3) identified the placements of the service facilities (attractions, restaurants, and shops), (4) calculated service facility capacities, and (5) conducted linear and geographically weighted regression to identify the relationships between visitor traffics and service facility capacities.
Related works Culture and theme parks Academic research works on theme parks are on an increase realizing that they help to answer questions regarding the nature of our society and our culture.1,6 As a large leisure and consumptive space, theme parks are visited by millions of people each year. Interacting with a large number of visitors for its optimized service provision and design, the fact that theme parks say a lot about our society, our cultural needs, and our communal acknowledgments is inevitable.1 According to Anton Clavé,1 an author of a well-written book, The Global Theme Park Industry, regardless of the size and scope of theme parks, they represent and give shape and sense to the societies in which they exist. As stated, theme parks “represent and give shape and sense,” meaning, not only do the theme parks affect the society where they are located, but they are also affected by the surrounding society. In this regard, theme parks today are a reflection of the culture where it is located and the people who visit. Taking the surrounding society, or the locals, into account can be observed even during the development process of theme parks. In fact, for a theme park development process to be sustainable and successful, stakeholders should consider the local economic processes because the implementation of theme parks in a particular region acts as a catalyst affecting the region’s economy, social relations, and cultural identities. In other words, theme parks are a powerful organization that can generate new social dynamics and influence the cultural characteristics of a place.1 Being an influential factor to the local community, the design of theme parks should consider and reflect the surrounding culture during the development process.
Service facilities and traffic analysis As emphasized in the introduction, the aspect that will show the most distinctive cultural difference is not the spatial aspect, but it is the consumption service aspect. In this part of the literature review, the characteristics and the tendencies of service facilities in theme parks are reviewed. According to Anton Clavé,1 the
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design of processes has a determining influence on the general layout of the park and that of each facility. The layout serves to determine the best physical arrangement of the different components that make up the park. For instance, the movement of theme park visitors can be influenced by the layout of the service facilities.7 The processes, therefore, condition issues such as the location of each facility, its capacity, the size of the waiting areas and its internal structure. At large, theme parks are composed of three kinds of service facilities: attraction, restaurant, and shop. These three service facilities play different roles obviously with different purposes, and with different production and service characteristics. First of all, in the case of payment, while the visitors have already paid for the attractions when entering the park, for restaurants and shops, the visitors will have to pay additionally during their time in the park. Second, in the case of service characteristics, attractions have a continuous nature1 with a purpose to minimize waiting time. While it is important to reduce the waiting time, it is also important to consider waiting areas that can undertake the peak amount of visitor flow. Similarly, for catering services of restaurants, the facility must include waiting areas and appropriately sized dining rooms. Even for a self-service, efficient waiting facilities for the route and the provision of cash registers should be considered.1 As for the service characteristics of shops, since the visitor chooses an item on the shelves and pays for it on their pace, it does not have specific service model to describe. From this literature review, we can see that the three service facilities are very different in their service characteristics. In the theme park industry and the academia, the necessity of an efficient park operation supporting system is widely shared.8,9 It is because an efficient park operation supporting system can help to increase the half-life of the number of visitors.10 In order to increase the half-life of the number of visitors, researchers have studied the visitors’ behavior, motivational factors, and decision-making process in the theme park.8,9,11–13 Despite the importance of understanding visitor traffics, methods of evaluating visitor traffic in theme parks are limited. However, Turner and Penn14 suggested visibility graph analysis (VGA) which combines isovist fields with space syntax to measure integration of isovists whiting the environments. Through VGA, it is possible to calculate the visual integration which shows how the point of visual steps is connected to other points. Thus, visitor traffics of the theme parks can be analyzed through VGA.
Methods In this study, we have conducted five tasks to find significant relationships between visitor traffics and service facilities. First, Disneyland blueprints were collected, and the paths were divided. Second, visitor traffic analyses were conducted based on the blueprints of the Disneyland maps. Third, visitor throughputs were calculated based on each service facility (attractions, shops, and restaurants). Fourth, service facility capacities have been computed based on the visitor throughputs and number of each service facility. Finally, linear and geographically weighted regression analyses were conducted.
Data structure We evaluated four Disneylands: Tokyo Disneyland; Disneyland Paris; the Magic Kingdom at Walt Disney World in Florida; and Disneyland in Anaheim, California. The paths of four different Disneylands were traced from the satellite images and adjusted based on the official Disneyland maps found online. The paths of the theme parks are composed of nodes (mediating paths) and edges (linking paths). Based on the blueprints of the four Disneylands, we segmented and assigned the nodes and edges with an identification number for regression analysis (Figure 1). As a result, we ended up identifying 35 segments for Tokyo, 45 for Paris, 37 for Florida, and 35 for California. The location and the number of service facilities were mapped onto the path blueprint based on the Disneyland online official maps. Furthermore, the waiting times of the attractions (or the capacity values) were collected from Disneyland online application.
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Figure 1. Path division samples: (a) original pathway blueprints and (b) segmented path.
Figure 2. Visibility graph taken from Turner et al.16
Visual integration Visitor traffic analysis in this article indicates the value derived from a VGA. VGA is fundamentally based on the idea of Hillier and Hanson15 who believed that there is a social logic of space. The social logic of space is brought about from the idea that the environment or the space people occupy has an effect on the way people walk, interact, and view the surroundings. Based on this assumption that the spatial shape influences the people, the idea of isovist, or also known as a viewshed, began. A viewshed includes all the sightlines in all different directions standing at a point in space.16 When the viewsheds are graphed at each of the points in an arbitrary grid, a visibility graph can be constructed (shown in Figure 2). From the visibility graph as a base, a variety of analyses can be implemented, and according to Turner et al.,16 VGA is a promising avenue of research since VGA is the core of the visitor movement evaluation such as agent analysis. For this research, the segmented paths of four Disneyland theme parks were used to analyze visitor traffics. DepthmapX version 3.0, developed by Tasos Varoudis, was used for VGA. Using the visibility graph map, the visual integration values are derived. Visual integration of a point on visibility map shows relationships of visual steps to a whole system to measure the accessibilities from points to points.17 Then, to get the average value of the visual integration at a particular path segment, the cells in the path were highlighted. The average values derived here are used as a visual integration of visitor traffic value where the higher the number, the more the people are expected to go through or occupy the path segment.
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Visitor throughputs We valuated visitor throughputs of attractions, shops, and restaurants. Visitor throughput is the capacity of the service facilities that can process number of visitors per hour. For the evaluation of visitor throughputs, we used service facilities that have influence on the visitor traffics, such as attractions, restaurants, and shops; other basic facilities, such as a bathroom and information desks, are excluded. For specification, the attractions include rides and entertainments. For the calculation, we adapted a model developed by Ahmadi8 shown below Si ,t = Min(Qi ,t , Ci , g ) (1) Si ,t represents the visitor throughput of attraction i in time t and it is calculated using Ci , g, number of passengers per attraction, and Qi ,t, number of visitors waiting in time t per attraction i. The visitor throughputs of attractions are expressed based on the number of visitors waiting and a minimum number of passengers at a particular time. Quantification on restaurant facilities could use the same visitor throughput model used above. However, information on waiting visitors in restaurant was not available; therefore, we have modified the equation as follows Si ,t = Min(Ci , g )
(2)
Si ,t is the visitor throughput of restaurant i in time t. In other words, the restaurant is quantified by the number of visitors that the restaurant can host based on the particular operating condition. However, it was difficult to collect real-time restaurant usage data; we have used the maximum values instead of the minimum. The maximum number of restaurant visitors was calculated based on the number of tables that the restaurants have. In cases where the restaurants do not have any tables such as a wagon, the average waiting visitor is used as seven as a fixed value. However, incorporating the maximum number of visitors could result in heavier influences as a downside. To eliminate such downsides, the minimum number of passengers was used for attraction quantification during the most crowded time. The quantification for shop visitor throughout is identical to the above throughput model. Shops do not require waiting visitors. Therefore, the throughput of the shop will be calculated as well.
Service facility capacities The visitor throughput of the attractions, restaurants, and shops was then used as weighting values to calculate service facility capacity. For instance, attraction capacity on path is calculated as follows
Ci =
Si − Min( S ) * N i (3) Max( S ) − Min( S )
C is the attraction capacity in path i where S is the visitor throughput of attraction facility in path i. From the visitor throughput of attraction, the minimum value is subtracted which then is divided by Max( S ) − Min( S ). Next, it is multiplied by N, number of attractions in path i. The same formula was used to calculate the capacities of shops and restaurants.
Results and discussions To find the patterns of service facility placements in theme parks, we test the relationships between visitor traffics and capacities of the service facilities per path. The analyzed visitor traffic or the averaged visual
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integration values were used as a dependent variable ( y ); the attraction capacity ( bA ), the shop capacity ( bS ), and the restaurant capacity ( bR ) values for each of the path segments were used as independent variables as shown in the following formula y = bA + bS + bR (4) The results of regression analysis on each of the four Disneylands showed individually unique relational tendencies between the analyzed visitor traffics and the capacity weighted number of service facilities per path. In the case of Disneyland Tokyo, restaurant capacities per path were negatively predicted by the analyzed visitor traffics (with a coefficient of −0.74, p
