Tourists’ spatial-temporal behavior patterns in theme parks: A case study of Ocean Park Hong Kong

Journal of Destination Marketing & Management 15 (2020) 100411

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Research paper

Tourists’ spatial-temporal behavior patterns in theme parks: A case study of Ocean Park Hong Kong

T

Xiaoting Huanga, Minxuan Lia, Jingru Zhangb,∗, Linlin Zhanga, Haiping Zhangc, Shen Yand a

Department of Tourism Management, College of Management Shandong University, China Department of Tourism, University of Otago, Dunedin, New Zealand c School of Geography, University of Nanjing Normal, Nanjing, China d Department of Statistics, University of Illinois Urbana-Champaign, Illinois, USA b

A R T I C LE I N FO

A B S T R A C T :

Keywords: Tourist behavior Spatial–temporal patterns Time geography Time–space path Ocean park Hong Kong Theme parks GIS research

Time, space, and activities are considered three important domains of the tourist experience. The core time geography concept of the ‘space–time path’, which highlights activity-based constraints, serves as a powerful visualization and quantification tool revealing tourists' spatial-temporal behavior. Moreover, the proliferation of tourist-tracking technologies has enabled more precise tourist behavioral data than ever before. This paper aims to integrate multiple data sources to analyze tourists' spatial–temporal behavior patterns on a micro scale. A fiveday survey was conducted at Ocean Park Hong Kong from July 6 to 10, 2014. Information about tourists' temporal–spatial behavior was gathered using handheld GPS tracking devices, and questionnaires were distributed to assess tourists' socio-psychological characteristics. Given marked differences in demographic and emotional characteristics, three spatial–temporal behavior clusters were identified via density center clustering, consisting of four factors: path length, tour time, coverage area, and oval circumference. This study presents a novel way to develop a better understanding of tourists' spatial-temporal behavior patterns based on GIS visualization and clustering methods from a microscopic perspective, thus contributing to theme park attraction management and tourist experience enhancements.

1. Introduction Although human trajectories involve numerous different possibilities, they exhibit a high degree of temporal and spatial regularity. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, individual travel patterns can be collapsed into a single spatial probability distribution (Gonzalez, Hidalgo, & Barabasi, 2008). This distribution indicates that, despite individuals’ diverse travel histories, humans follow simple, reproducible patterns. Inherent similarities in travel patterns could affect various phenomena influenced by human mobility, from epidemic prevention to emergency response, urban planning, and tourism (Coles & Hall, 2006; Han, Kim, & Otoo, 2018; Kwan & Schwanen, 2016). In a theme-park context, mobility patterns of park visitors have also drawn the attention of many tourism researchers. Knowing which rides have been taken, which shows have been attended, and which shops and squares have attracted tourists could lead to improvements in attraction management, such as the layout and capacity of parks (Zhang, Li, & Su, 2017), retail profits (Rajaram & Ahmadi, 2003), and tourist

∗

satisfaction (Tsai & Chung, 2012). Nowadays, various mobility models and simulation tools of park visitors have been employed in industrial practice to evaluate the management performance of theme parks, while all of these models and tools are based on the understanding of real-world mobility patterns of park visitors (Solmaz, Akbas & Turgut, 2012; Vukadinovic, Dreier, & Mangold, 2011). Thus, the understanding of visitor mobility practice is a key factor for the success of theme parks. Since researchers began exploring changes in the meaning of space among tourists following a sightseeing route within a destination in the 1990s (Fennell, 1996), studies on tourists' behavior patterns have mainly focused on macro-scale destinations, such as cities (Grinberger, Shoval, & Mckercher, 2014; McKercher, Shoval, Ng, & Birenboim, 2012; Zoltan & McKercher, 2015) or nature parks (Hallo et al., 2012). Influenced by humanism and postmodernism, the study of human geography has shifted its research objectives toward social goals, such as upgrading quality of life, and now focuses on explaining human geographic phenomena through the lens of micro-individual behavior (Chai, Zhou, Wu, & Zhang, 2007). This change has also inspired academic attention around tourists' behavior in micro-scale destinations,

Corresponding author. E-mail address: [email protected] (J. Zhang).

https://doi.org/10.1016/j.jdmm.2020.100411 Received 19 September 2019; Received in revised form 2 January 2020; Accepted 16 January 2020 2212-571X/ © 2020 Elsevier Ltd. All rights reserved.

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Tourism scholars have paid increasing attention to the effects of time factors on tourist behavior, and time is considered one of the three primary constraints on tourism demand (the other two being money and political control) (Bull, 1991). However, the value of time geography to tourism flows or itineraries remained relatively neglected until this century (McKercher & Lew, 2004). Related conceptual work began with a discussion of how to coordinate spatial and temporal elements (Shoval & Isaacson, 2007), followed by adopting time geography constraints for tourists' activities (Shoval, Mckercher, Birenboim, & Ng, 2015). Empirical work has also focused on exploring spatial–temporal patterns at aggregated and disaggregated levels (Birenboim et al., 2013; Russo, Clave, & Shoval, 2010). An interesting topic involving new time geographic objects concerns integrating the quality of experience into spatial–temporal patterns, which has promoted discussion around subjective perspectives (Birenboim, Reinau, Shoval, & Harder, 2015; Kwan, 2007). These efforts have helped introduce time geography into tourism research applications. Time geography also enhances tourism scholars' ability to analyze and visualize GPS data; however, few researchers have considered relationships between tourists' behavior patterns and tourists’ characteristics using multi-source data. In a big-data context, tourists are less likely to be considered individuals possessing initiative.

such as old towns (Shoval & Isaacson, 2007) and theme parks (Birenboim, Anton Clavé, Russo, & Shoval, 2013; Birenboim, Reinau, Shoval & Harder, 2015). However, due to contextual limitations, the methods adopted in these studies (e.g. observations, interviews, or questionnaires) appear insufficient in providing a clear picture of tourists’ behavior patterns. Constraints related to participant and operator recall may also cloud the accuracy and objectivity of results. The excessive time and energy associated with traditional research approaches further limit the quantity and diversity of data, thereby impeding in-depth analysis. The development and application of tracking technologies now offers a more scalable and objective means of capturing detailed spatialtemporal behavior data (Shoval & Isaacson, 2010). GPS devices and mobile phones are two types of tracking technology often applied in tourist behavior studies. The most prevalent research approach in tourism involves distributing GPS-logging devices to respondents to leverage global-navigation satellite systems (Shoval, McKercher & Birenboim, 2011; Tchetchik, Fleischer, & Shoval, 2009). An alternative method involves tracking the movement of mobile phones through a cell tower network without direct participation of a phone's owner (Ahas, Aasa, Mark, Pae, & Kull, 2007; Ahas, Aasa, Roose, Mark & Silm, 2008; Ratti, Pulselli, Williams & Frenchman, 2006; Zhao, Lu, Liu, Lin, & An, 2018). However, acquisition of accurate GPS data (to the second on a spatial scale and to the meter on a time scale) hinders analytical innovation and integrity. Research approaches have thus remained limited to descriptive analysis and simple pattern clustering; few studies have considered multi-source data. In an attempt to rectify these issues, the current paper aims to explore tourist spatial-temporal behavior patterns in a micro-scale destination, Ocean Park Hong Kong (Ocean Park), using a new method that integrates multi-source data and combines traditional cluster methods with GIS measurement. The study also seeks to condense and use data effectively to bridge tourists’ behavior patterns and demographic characteristics after a visit. Given this aim, this paper opens with a review of extant literature related to spatial–temporal behavior patterns. Then, it outlines the research methods, illustrates the GPStracked survey process, and presents the results of cluster analysis. Finally, the paper provides a discussion and conclusions related to practical planning and policymaking.

2.2. Multi-source data using new technologies The accuracy and validity of data collection is essential to spatial and temporal behavior research. Previously, data were collected using interviews or questionnaires, but researchers soon realized these methods were ineffective given behavioral complexity. Space–time diaries, following from time budget theory, were thought to reveal much more in-depth behavioral information (Fennell, 1996; Thornton, Williams, & Shaw, 1997; Xu, Cong, & Wall, 2019). Non-participant observation, wherein a researcher accompanies individual tourists, can also provide precise information but is incredibly time-consuming (Hartmann, 1988). A lack of data has tempered behavioral research although its value has been widely recognized. Fortunately, recent developments in location-based services have produced numerous applicable technologies. Passive data collection can now be accomplished using cell phone or Bluetooth (Ahas, Aasa, Roose, Mark, & Silm, 2008; Versichele et al., 2014). Active collection can use GPS technology to track tourists' movements (Shoval & Isaacson, 2007; Zheng, Huang, & Li, 2017). GPS data, which are at times combined with semi-structured interviews or minute-by-minute photographs, can serve as a more reliable data source for exploring tourists’ behavioral constraints, preferences, and discrepancies between segments (Bauder & Freytag, 2015; Ferrante; Cantis; Shoval, 2018; Li, Yang, Shen, & Wu, 2019; Pettersson; Zillinger, 2011). Studies of tourist behaviour have entered a period of data explosion. With the development of individual tracking technologies and GIS, the operation and implementation of time-geographic constructs has become more feasible (Kwan, 2004). For example, in investigating urban residents’ activities, Chen et al. (2011) developed an ArcGIS extension called Activity Pattern Analyst to represent and analyze spatial–temporal activity data at the individual level. Principal component analysis and network planning, used together with GIS, are often employed to evaluate individual spatial–temporal behavior data (Dietvorst, 1994; Huang & Wu, 2012; van der Knaap, 1999; Xia, Evans, Spilsbury; Ciesielski; Arrowsmith & Wright, 2010). Tourist activities represent a form of highly complex social behavior. Multi-source data analysis that provides us with opportunities to combine both ‘objective’ and ‘subjective’ data from a comprehensive perspective is therefore a useful approach to understand tourist activities. Consolidating data from different sources has undoubtedly become a key issue in data analysis. However, many types of clustering analysis result in subjective, descriptive explanations that are unsupported by data. Explanatory variables also tend to lack sufficient

2. Literature review 2.1. Time geography As a methodology, time geography was proposed and developed by the famous Swedish geographer Hägerstrand, who led a research team at the Lund School in the late 1960s. One of Hägerstrand's most profound achievements was the representation of space and time in a single diagram: not as an ordinary map, but rather a snapshot reproducing a moment in time (Chai, 2002). Time geography offers a useful conceptual framework to study individual activity patterns under various constraints in a space–time context (Hägerstrand, 1970, 1989). The core concept of time geography is the space–time path, which highlights constraints imposed by activities that are finite in space and time as well as the need to trade time for space when transitioning among activities (Raubal, Miller, & Bridwell, 2004). Time geography emphasizes individuals; space–time paths represent individual spatial movements over time and offer an effective way to model activities' spatial–temporal characteristics (Chen et al., 2011). Emotional factors comprise a distinct component of space–time paths. Individuals' feelings and pre-feelings have been found to influence human behavior; as people proceed through a space–time path, space, time, and emotion become mutually influential (McQuoid & Dijst, 2012). Time geography is a powerful theory for understanding tourists' behavior across time and space. Time, space, and context are three important domains of the tourist experience (van der Knaap, 1997). 2

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3. Study methods

empirical support. Comprehensive analysis of big data warrants additional exploration. Thus, there is a call for addressing challenges in integrating multi-source data and combining several methods in empirical studies.

3.1. Sample and procedure Ocean Park is a world-famous marine-life theme park. It has been named one of the world's most popular amusement parks, ranking top twenty in the world and top 10 in Asia in 2018 (Themed Entertainment Association, 2019). The park is divided into eight regions including Amazing Asian Animals, Aqua City, Whiskers Harbor, Rainforest, Marine World, Headland Rides, Adventure Land and Thrill Mountain. The park covers roughly 17 ha. The Brick Hill (Headland) can only be reached by cable car or the Ocean Express, which was launched in 2009 from the Waterfront (Lowland). The Tai Shue Wan is connected to Brick Hill by the world's second-longest outdoor escalator. Ocean Park is an ideal theme park example in terms of node level, tourist scale, scenic area, internal structure, and traffic. A survey of tourists' temporal-spatial behavior at Ocean Park was conducted July 6 to 10, 2014. The survey captured data about tourists' temporal–spatial behavior, sociodemographic characteristics, and emotions using GPS tracking via handheld devices and questionnaires. To ensure a representative sample, researchers must not choose respondents subjectively; therefore, researchers in this study were instructed to go to the Ocean Park entrance with prepared GPS devices and questionnaires and ask the first visitor they encountered if he or she would agree to take the survey. If the visitor refused, researchers continued approaching patrons at the entrance until identifying a willing tourist. Tourists who agreed to participate in the survey were asked to carry GPS devices at the Ocean Park entrance and Tourist Information Center. Participants later completed their questionnaires while returning the GPS device upon conclusion of their visit. All participants’ space–time paths were recorded by the supported server. Respondents were monitored in real time throughout the study process via the GPS Real-time Monitoring Platform (Fig. 1). During the study, 520 GPS devices were distributed but 752 GPS trajectories were captured (each participant group had one device). When participants returned the GPS device, they were asked to complete a questionnaire asking about their demographics and travel experience information. When the GPS trajectory and questionnaire were each complete and matched, the GPS trajectory was deemed valid (validity rate: 63.0%). Ultimately, 474 respondents’ GPS trajectory data, demographics, and travel data were included for analysis.

2.3. Intra-destination space–time patterns Many scholars have explored inter-destination movement patterns (Lew & McKercher, 2002; Oppermann, 1995), whereas studies involving intra-destination tourist behavior remain relatively scarce (Caldeira & Kastenholz, 2018, 2019). Intra-destination space refers to an enclosed space with defined boundaries, in which tourist behavior is much more controllable. To analyze tourist behavior within a destination, scholars have applied advanced techniques to measure spatial–temporal behavior on a macro scale using traditional methods such as travel diaries and observations (Asakura & Hato, 2004; Asakura and Iryo, 2007; Lew & McKercher, 2006; O'Connor, Zerger; Itami, 2005). Recently, various devices have been used to collect high-resolution data, laying a foundation for micro-scale research. For example, Huang and Wu (2012) developed a combined approach to clarify tourists' behavior patterns quantitatively and qualitatively using the concept of the space–time path to explain behavioral patterns based on temporal behavior factors, spatial behavior factors, activity choice factors, and path characteristics. Birenboim et al. (2013) analyzed time–space trajectories recorded by GPS devices and described tourists' temporal activity patterns at the PortAventura theme park in Catalonia, Spain. Taking Marwell Zoo as a sample case, East, Osborne, Kemp, & Woodfine (2017) combined GPS data with survey data and found that different types of visitors behaved differently when exploring attractions, such as when deciding where to go next and how long to linger at particular locations. To meet design parameter requests of agent-based simulations software, these studies aimed to collect precise temporal–spatial data. However, classifying tourists according to temporal and spatial behavior characteristics requires further investigation and improvement, as do the visualization and quantification of tourists' temporal and spatial behavior data. Theme parks are a unique type of small-scale tourism destination from a spatial perspective. A theme park, where themes are incorporated to offer visitors interesting experiences distinct from everyday life, is an aggregation of attractions involving architecture, landscaping, rides, shows, food services, costumed personnel, and retail shops (Pearce, 1988). As a form of artificial engineering, theme park planning and construction should place a high value on tourists’ preferences, different from other natural or historical landscapes. Visitor behavior is a fundamental tenet of theme park design, renovation, and expansion, reinforcing the importance of learning about tourist behavior in these types of parks. Amidst rising competition from other leisure and tourism products, theme park business has become dependent on a higher proportion of return visitors (Braun & Soskin, 1999). To survive in a rapidly changing environment, understanding consumers' spatial–temporal behavior patterns and adjusting facilities' spatial arrangements based on visitors' preferences has become a useful way to improve theme park management. Research on tourists' spatial-temporal behavior patterns in theme parks have primarily focused on methodological experiments or activity pattern exploration by tracking data (Birenboim et al., 2013, 2015; Russo et al., 2010). Comparatively little attention has been paid to associations between tourists’ behavior patterns and their demographic and emotional characteristics (Huang, Loo, Zhao, & Chow, 2019). This study therefore sets out to combine multi-source data to identify the link between these factors.

3.2. Questionnaire and measures The questionnaire consisted of four sections. The first section explored visitors' motivation for visiting Ocean Park. Six motivations were presented as multiple-choice options: ‘entertainment’, ‘parent–child experience’, ‘stimulation’, ‘education’, ‘relaxation/recreation’, and ‘close to animals’. The second section addressed visitors' travel behavior, namely companion types and consumption. Participants responded to a multiple-choice question consisting of five companion types: ‘family’, ‘friends’, ‘colleague’, ‘package tour’, and ‘alone’. Regarding consumption behavior, respondents were asked to calculate their total expenses at Ocean Park and to identify the kind of ticket they purchased: adult ticket; half-price ticket; or ‘other’, such as an annual membership or admission via comprehensive social security assistance. The third section included information about visitors' emotional characteristics. Visitors' satisfaction, as ‘an emotional state of mind after exposure to the opportunity’ (Baker & Crompton, 2000, p. 787), is measured by a five-point scale. Respondents were also asked to answer a multiple-choice question about the reasons behind their satisfaction level, including ‘engaging theme’, ‘variety of facilities’, ‘interesting exhibits’, ‘reasonable layout’, ‘reasonable cost’, and ‘friendly service’. The last section gathered respondents' demographics (i.e. gender, age, education level, place of residence, and travel experience). 3

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Fig. 1. GPS Real-time Monitoring Platform.

3.3. Data analysis

4. Analysis and findings

Data analysis was performed using four steps. First, based on GPS tracking data, GIS-oriented analysis was conducted to explore the characteristics of visitors' space–time paths at Ocean Park. Using ArcGIS 10.2, the spatial analyst and data management tools were used to visualize visitors’ space–time paths. Hotspot analysis was based on a point-density module, and space–time paths were built in ArcScene to depict a 3D visualization with the help of the XY-to-line and points-toline modules. Second, from a functionalist perspective, movement is an important criterion in tourism spatial-temporal behavior evaluation and includes three aspects: moving distance, moving speed, and moving range (Huang, Li, Zhang, & Qing, 2016). Five indicators can be used to profile tourists' spatial–temporal behavior, including path length, tour time, tour speed, coverage area, and oval circumference; however, as tour speed equals path length divided by tour time, tour speed was excluded as an indicator and the four remaining features were used to evaluate tourists’ spatial–temporal behavior. Third, based on these four indicators, tourists' spatial–temporal behavior patterns were clustered via unsupervised clustering analysis based on density center clustering. Rodriguez and Laio (2014) proposed this method in Sciences with the aim of characterizing clustering centers. In this paper, clustering centers should possess the following characteristics simultaneously: (1) high density (i.e. the centers should be surrounded by neighbors with lower density); and (2) the ‘distance’ is relatively larger than other denser data points. Based on these features, clustering centers can be identified and non-clustering centers classified. Although clustering is a traditional approach to examining behavior patterns, general and structural knowledge is also needed to consider behavioral complexity. Accordingly, one-way analyses of variance (ANOVAs) were used for cluster validation. Post-hoc tests were conducted when ANOVA results were significant at the p < 0.05 level. If the assumption of homogeneity of variance was valid, a Tukey test was performed; otherwise, Tamhane's T2 was used. Finally, to explore relationships between visitors' spatial-temporal patterns and other attributes, Pearson's chi-square analysis, ANOVA, and correlation analysis were performed to determine the demographic, emotional, and behavioral characteristics of respondents in each cluster.

4.1. Spatial-temporal path visualization Based on the concept of spatial–temporal paths from time geography science, GPS tracking data for 474 tourists at Ocean Park were depicted via 3D visualization. First, using ArcGIS, over 560,000 pieces of GPS positioning data were transformed into geospatial points according to their latitude and longitude. Second, 2D graphics were converted into 3D versions based on their temporal and spatial attributes. Third, data points in each point set were connected into a complete line in accordance with temporal sequences. Fourth, the generated line layers were transformed into a plane projection coordinate system to facilitate subsequent calculation. The trajectory length, time spent on the tour, coverage area, and ellipse perimeter were selected as basic indicators to reflect tourists’ spatial-temporal behavior. Table 1 displays the partial calculation results. The definitions and calculation methods for each indicator are outlined below. Length refers to the length of each tourist's trajectory line (i.e. the total distance each tourist moved in the X-Y axis plane coordinate system). The trajectory point of each sample is connected to a complete trajectory line in a time sequence, and the shape of the trajectory length is calculated as follows:

Table 1 Calculation results.

4

ID

Shape Length/m

Time/s

Area/m2

Perimeter/m

C706A021 C706A031 C706A041 C706A051 C706A061 C706A091 C706A101 C706A151 C706A161 C706A181

3297.89933 6060.61598 13914.8146 9894.23983 7807.18671 12289.5206 12130.3697 14237.6015 10876.3311 11445.172

15117 14431 53722 27469 28544 25507 30560 30123 19526 22590

341291.906 853289.587 4847997.46 1778092.8 732908.956 778198.821 904397.798 851055.06 1028979.83 986629.338

3607.42201 4607.66373 17926.76 5979.13211 4460.9451 4527.70751 4675.97446 4633.03667 4781.08892 4715.63403

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Fig. 2. Trajectory and coverage of typical tourist. Table 2 ANOVA results for spatial-temporal patterns.

Shape length Area Perimeter Time

Cluster 1 N = 211

Cluster 2 N = 190

Cluster 3 N = 73

Total N = 474

Test Score

7608.16 602584.20 4191.44 18045.29

11348.45 959324.06 4752.69 27294.59

12616.48 880852.71 4731.05 29155.86

9878.76 788436.89 4499.52 23463.94

F F F F

= = = =

212.467, p < 0.001** 159.855, p < 0.001** 71.547, p < 0.001** 214.952, p < 0.001**

**p < 0.01. n

d=

∑2

Area refers to the projected area of the tourist's trajectory reflected in the X-Y axis plane coordinate system. Many calculation methods are available for this; we used the area of the minimum ellipse to cover all trajectory points. The trajectory coverage was generated in ArcGIS based on the point set of each track. Perimeter refers to the circumference of the ellipse covering all

(x i − x i − 1)2 + (yi − yi − 1)2

i=2

Time refers to the duration of each tourist's trajectory. The calculation method is relatively simple, equal to the difference between the starting and ending time of each GPS trajectory. 5

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than male, averaging 61.4% in total. However, respondents in the three clusters were significantly differentiated by other variables. In terms of age, most respondents were under 40 years old. Whereas the distribution of respondents under 40 years old was relatively even in Cluster 1, Clusters 2 and 3 were dominated by visitors younger than 20 (42%) and between 20 and 30 years old (35.4%), respectively. With regard to education, Cluster 1 had the lowest proportion of visitors who had earned only a primary education. Compared to the other two clusters, Cluster 3 contained the highest proportion of visitors who had completed primary education (12.9%) as well as those who had completed tertiary education (53.2%). Although most respondents were international tourists, Clusters 2 and 3 consisted of more locals (χ2 = 8.938, p =0.011). These two clusters also included a significantly lower proportion of first-time visitors than Cluster 1 (χ2 = 8.727, p =0.013).

trajectory points, which describes the spatial characteristics of tourists’ travel experiences as an indicator of the area. Fig. 2 presents an example of the trajectory and coverage. 4.2. Cluster analysis Cluster analysis was generated with length, time, area, and perimeter as the four main indicators; cluster results of tourists’ spatialtemporal behavior patterns at Ocean Park are provided in Table 2. In this study, the sample was divided into three types after multiple iterations using unsupervised clustering analysis based on density center clustering. Variance analysis was employed to test the validity of clustering results. As shown in Table 2, the p-values of all four indicators (i.e. shape length, time, area, and perimeter) were less than 0.001, suggesting these indicators' feasibility and effectiveness in performing clustering analysis of tourists’ spatial-temporal behavior patterns. Three spatial–temporal behavior patterns are described as follows: Cluster 1: Cluster 1 contained the lowest index values with a shape length cluster center of 7608m, time length cluster center of 18,045s, area cluster center of 602,584m2, and perimeter cluster center of 4191m (Fig. 3). This cluster covered nearly half of the total sample; these visitors explored the park at a low intensity. Cluster 2: With a shape length cluster center of 11348m, time length cluster center of 27,294s, area cluster center of 959,324m2, and perimeter cluster center of 4752m, Cluster 2 exhibited the highest area value and moderate values of shape length and time length. Visitors in this cluster covered the largest area at a moderate intensity (see Fig. 4). Cluster 3: With a shape length cluster center of 12,616m, time length cluster center of 29,155s, area cluster center of 880,852m2, and perimeter cluster center of 4731m, Cluster 3 contained the highest values for shape length and time length with a moderate area. The perimeter value of this cluster was similar to that of Cluster 2. Visitors in Cluster 3 covered the smallest sample area over the longest trajectory and longest duration; in other words, they demonstrated a more ‘slender’ coverage pattern (Fig. 5).

4.3.2. Differences in emotional and consumer behavior characteristics The relationship between participants’ spatial-temporal patterns and emotional and behavioral characteristics were evaluated using a chi-square test and one-way ANOVA. As indicated in Table 4, visitors in the three clusters showed similar motivations for visiting Ocean Park. An exception was Cluster 2, in which the highest rates of visitors reported seeking stimulation (χ2 = 8.347, p = 0.015). Although most visitors were visiting Ocean Park with their family, some differences were found among companion types. Compared to Clusters 2 and 3, visitors in Cluster 1 traveled least often with friends and mostly alone. When accompanied by colleagues, people were least likely to exhibit the Cluster 2 pattern. In terms of consumption behavior, visitors in Cluster 1 reported the highest total cost for their trip to Ocean Park (F =3.397, p =0.034), approximately HK$150 higher than the other clusters. Although Clusters 2 and 3 spent similar amounts in total, visitors in these clusters demonstrated different consumption patterns. Cluster 2 reported the lowest rates of visitors who bought adult tickets (62.7%) but the highest rates of those who bought other-type tickets (35.7%) (e.g. an annual membership or comprehensive social security assistance ticket). Cluster 3 displayed the opposite pattern. 4.4. Relationship between tourism experience and satisfaction

4.3. Difference analyses of clusters Travel experience was measured by the number of times participants had visited Ocean Park. Satisfaction was based on tourists’ selfreport, where 1 = very dissatisfied and 5 = very satisfied. In terms of spatial-temporal efficiency, spatial efficiency was denoted by the ratio of shape length to area, and temporal efficiency was denoted by the ratio of shape length to time and the ratio of area to time. Correlation analyses were performed between travel experience/satisfaction and

4.3.1. Differences in demographic characteristics After identifying differences in spatial-temporal patterns among the three clusters, visitors' demographics were identified by cluster. Crosstabulation was performed to determine respondents’ sociodemographic characteristics (Table 3). No significant difference in gender was found among the clusters; each cluster contained more female respondents

Fig. 3. The spatial-temporal behavior pattern of Cluster 1. 6

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Fig. 4. The spatial-temporal behavior pattern of Cluster 2.

spatial efficiency/temporal efficiency for each cluster. Table 5 reveals no significant correlation between travel experience and spatial-temporal efficiency for the whole sample and each cluster. Similarly, no significant correlation emerged between satisfaction and spatial–temporal efficiency for the whole sample. Yet for Cluster 3, which exhibited a longer visit duration, satisfaction was significantly correlated with both temporal efficiency indices (i.e. shape length: time and area: time); that is, within the same amount of time, the greater the shape length or the more area a tourist covered, the higher his or her satisfaction.

Table 3 Demographic profile of respondents. Cluster 1 Gender (N = 464, %) Female 57.9 Male 42.1 Age (N = 443, %) Under 20 30.7 20-30 26.7 31-40 27.2 41-50 11.4 Above 50 4 Education (N = 426, %) Primary 4 education Secondary 47.5 education Tertiary 48.5 education Residency (N = 406, %) HK 15.3 Non-HK 84.7 Visiting times (N = 447, %) First time 70.1 More than one 29.9 time

5. Discussion The aim of this paper has been to experimentally analyze tourists' travel paths in light of geometric features and then examine their spatial–temporal behavior patterns. Individual tourists' spatial–temporal behavior is often random and unique; as such, different tourists' GPS trajectories represent various spatial–temporal paths, making it difficult to extract noteworthy trends. The paper attempted to provide a new approach to graph analysis. First, a GPS point dataset was transformed into a geometric figure based on the line and minimum ellipse covering all points. Then, the line length, perimeter, and area of the minimum ellipse were calculated, resulting in a new algorithm that can be used to measure tourists’ behavioral trajectories. The geometric characteristics of tourists' spatial movement merely

*p < 0.05, **p < 0.01.

Fig. 5. The spatial-temporal behavior pattern of Cluster 3. 7

Cluster 2

Cluster 3

Total

Test Score

65.6 34.4

60.9 39.1

61.4 38.6

χ2 = 2.471, p = 0.291

42 21.6 26.7 5.7 4

20 35.4 27.7 10.8 6.2

33.6 26 27.1 9 4.3

χ2 = 15.661, p = 0.047*

6.6

12.9

6.3

χ2 = 13.014, p = 0.011*

54.8

33.9

48.4

38.6

53.2

45.3

27.7 72.3

27.6 72.4

21.9 78.1

χ2 = 8.938, p = 0.011*

55.4 44.6

63.2 36.8

63.3 36.7

χ2 = 8.727, p = 0.013*

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Table 4 Cross-tabulation results for visitors’ motivation, behaviour and satisfaction.

Motivation (N = 456, %) Entertainment Parent-child experience Stimulation Education Relaxation/Recreation Close to animals Companions (N = 455, %) Family Friends Colleague Package tour Alone Total Expense (N = 463, HK$) Entrance Fee (N = 460, %) Adult ticket Half-price ticket Other ticket Reasons for satisfaction (N = 455, %) Engaging theme Variety of facilities Interesting project Reasonable layout Reasonable cost Friendly service

Cluster 1

Cluster 2

Cluster 3

Total

Test Score

45.9 33.7 12.2 10.2 34.6 17.6

52.5 30.4 21.5 12.2 42.5 20.4

51.4 27.1 10 10 32.9 18.6

49.3 31.4 15.6 11 37.5 18.9

χ2 χ2 χ2 χ2 χ2 χ2

68 23.3 4.9 2 3.4 656.84

57.2 40 0.6 0.6 0 503.92

62.3 36.2 4.3 0 0 520.19

62.9 31.9 3.1 1.1 1.5 574.71

χ2 = 4.755, p = 0.093 χ2 = 13.051, p = 0.001** χ2 = 6.393, p = 0.041* χ2 = 2.620, p = 0.270 χ2 = 8.593, p = 0.014* F = 3.397, p = 0.034*

76 10.1 26

62.7 13 35.7

80.6 16.4 19.4

71.3 12.2 28.9

χ2 = 11.722, p = 0.003** χ2 = 2.079, p = 0.354 χ2 = 7.946, p = 0.019*

54.2 59.9 38.4 21.6 8.8 27.8

41.7 56.1 36.1 29.4 16.7 27.6

58.3 54.2 44.4 23.6 19.4 27.4

49.9 57.5 38.5 25 13.6 27.6

χ2 χ2 χ2 χ2 χ2 χ2

= = = = = =

= = = = = =

1.836, 1.161, 8.347, 4.438, 3.325, 1.836,

8.421, 0.946, 1.509, 3.251, 7.496, 0.004,

p p p p p p

p p p p p p

= = = = = =

= = = = = =

0.399 0.560 0.015* 0.803 0.190 0.399

0.015* 0.623 0.470 0.197 0.024* 0.998

Note: One-way ANOVA test for continuous variables and χ2 for categorical variables. *p < 0.05, **p < 0.01. a Scale from 1 = “not satisfied at all” to 5 = “very satisfied”. Table 5 Cross-tabulation results for visitors’ motivation, behavior, and satisfaction. Travel experience

Spatial efficiency

Shape length:area

Temporal efficiency

Shape length:time

Area:time

Pearson correlation Sig. (2-tailed) N Pearson correlation Sig. (2-tailed) N Pearson correlation Sig. (2-tailed) N

Total

Cluster 1

Cluster 2

Cluster 3

0.057 0.329 291 0.048 0.416 291 −0.040 0.496 291

−0.031 0.706 148 0.104 0.208 148 0.059 0.473 148

0.151 0.133 100 0.039 0.697 100 −0.123 0.222 100

0.279 0.070 43 −0.096 0.541 43 −0.232 0.134 43

Total

Cluster 1

Cluster 2

Cluster 3

−0.045 0.343 455 −0.012 0.796 455 0.013 0.780 455

−0.084 0.235 201 −0.106 0.135 201 0.014 0.845 201

0.067 0.368 183 0.015 0.843 183 −0.062 0.407 183

−0.008 0.949 71 0.335** 0.004 71 0.271* 0.022 71

Satisfaction

Spatial efficiency

Shape length:area

Temporal efficiency

Shape length:time

Area:time

Pearson correlation Sig. (2-tailed) N Pearson correlation Sig. (2-tailed) N Pearson correlation Sig. (2-tailed) N

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

than those in Cluster 1. Researchers seem to agree that spatial–temporal behavior research on a micro scale is valuable for applications of big data and the construction of ‘smart tourism’. Our study demonstrates the feasibility of combining multiple data sources to explore behavior patterns, while the GIS method can provide a traditional clustering perspective. Following the logic from general characteristics to device-based data, we identified three typical paths among Ocean Park tourists. The distinct features of trajectory duration and length, area, and coverage perimeter reveal

demonstrate their explicit behavior. Such behavior is triggered by tourists' intrinsic cognition and decision making, which in turn influence tourists' emotions and consumption demands. The spatial and temporal structural characteristics of geographic data can be considered an objective means of understanding tourists' socioeconomic characteristics to some extent. Moreover, tourists’ socioeconomic characteristics can also help to predict their temporal and spatial characteristics. For example, participants in Clusters 2 and 3 enjoyed a relatively in-depth trip, tended to be younger, and spent less money 8

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scientific basis for industry practices, such as service locations, guide identification systems, and intra-attraction transportation. This paper proposes an approach to classify tourists according to their temporal and spatial behavior characteristics and devises a method for visualization and quantification of tourists' temporal and spatial behavior data. From these results, researchers can ascertain relationships between spatial–temporal behavioral strategies and satisfaction outcomes. Regarding the four factors of path length, tour time, coverage area, and oval circumference, path length is the most accurate indicator of visitor behavior. Tour time is also accurate, but relevant analysis is not sufficiently precise. Compared with the total length of time, tourists' time spent in each scenic spot of a theme park is more meaningful for understanding tourist behavior and optimizing the park. How best to define and identify the points on tourists' GPS trajectories, which belong to the spatial scope of one scenic spot and further determine the duration of each scenic area visited, will be studied in the future. The coverage area and oval circumference can also be calculated to depict tourist theme park coverage. Although the calculation method of minimum ellipse can reveal differences in the spatial coverage dimensions of spatial–temporal behavior, a large error remains between calculated results and the actual coverage area. Methods to define and calculate tourists’ real and accurate visitation area represents another valuable avenue for subsequent research.

unique spatial–temporal strategies. For example, participants in Cluster 3 shared a similar perimeter with those in Cluster 2 but covered a smaller area, implying a preference for a more detailed visit. Our results also show that within enclosed micro-scale sites, tourists' behavioral measurement should include spatial and temporal elements. By combining statistical clustering with GIS visualization, behavior patterns can be delineated more precisely. 6. Conclusion This paper follows a time geography framework by using many objects related to space–time paths. Using GPS tracking technology and a geometric method, space–time paths could be visualized and calculated by time, length, and coverage area and perimeter. Participants were then clustered into three categories based on these parameters and exhibited notable differences in demographic, emotional, and consumer behavior characteristics. The method of data integration presented herein provides an important theoretical contribution; this type of analysis, which is grounded in objective tracking data and subjective responses in questionnaire, will likely become common in the future. Although tourism researchers may attend more closely to experience compared to scholars in other fields, tracking technologies only provide factual information rather than emotional data, which limits these technologies’ potential applications. Combining two data sources can leverage the advantages of each data type. The presented analytical framework could also be extended to other tracking techniques, such as cell phones or social media. For example, self-reported data sources (e.g. users marking their locations on Twitter) combined with experiences and attitudes can enlighten tourist behavior studies. Such an approach also shows promise for comparing different data sources at a fixed site during a specific period. A better understanding of tourists' spatial–temporal behavior patterns can contribute to theme park attraction management and tourist experience enhancements. The empirical case of Ocean Park also offers practical insights into the development of micro-scale and elaborate tourism environments. On the one hand, the operating and maintenance costs of theme parks are very high as theme parks have highdensity areas hosting various facilities, such as exhibitions, games, rides, and shops. Since the utilization of venues and game facilities in a theme park usually varies from each other, managers need to take measures to improve the use of inefficient venues, or contrarily, make decisions to close down them in order to reduce energy consumption (Kataoka, Kawanura & Kurumatani, 2005). In the past, managers relied on tripod turnstiles to know the number of visitors of each venue every day in Ocean Park Hong Kong. By using GPS data collection technology and the analysis method described in this paper, managers can know not only the number of visitors to each venue, but also the time spent by visitors in each venue (Russo et al., 2010; Birenboim et al., 2013; Birenboim et al., 2015). Based on the more accurate analysis of individual tourists' spatial-temporal behavior information, it can provide not only a direction for future planning and upgrading of the park, but also a real-time support for park managers to improve park operation. For example, it can be used for controlling visitor flow and reducing the waiting time of venues, facilities, games and transportation. On the other hand, visitors have chances to obtain targeted products and services based on their own behavioral strategies. Although general patterns can be deduced using traditional methods, park managers also need information to engage in effective market segmentation (e.g. VIP groups or short-stay groups). Clustering analysis with path visualization offers a clear system that can indicate different groups' patterns. Visitors' demographic characteristics, emotional states, and consumption preferences can be detected by identifying their spatial-temporal behavior, thus facilitating promotion of appropriate products and personalized services. Visitors’ demographic characteristics and consumption records can then also be predicted; such data may provide a more

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Xiaoting Huang is a Professor in Department of Tourism Management, College of Management, Shandong University. Her research interests include tourist behavior, tourist activity space and health & educational Value of Tourism. Email: satinhuang@sdu. edu.cn Minxuan Li is a master graduated from Department of Tourism management, College of Management, Shandong University. Her research interests include tourist behavior and tourism planning. Email: [email protected] Jingru Zhang is a PhD candidate in Department of Tourism, University of Otago, New Zealand. His research interests include backpacking tourism, lifestyle mobility, identity construction and community development. Email: [email protected] Linlin Zhang is a master candidate in Department of Tourism management, College of Management, Shandong University. Her research interests include tourist behavior and tourism planning. Email: [email protected] Haiping Zhang is a PhD candidate in School of Geography, University of Nanjing Normal, Nanjing, China. His research interests include spatial analysis and modeling, tourist behavioral geography and cultural geography. Email: [email protected]. cn Shen Yan is a PhD student in Department of Statistics, University of Illinois UrbanaChampaign, Illinois, US. His research interests include time series analysis and state space model. Email: [email protected]

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