Analyzing attraction linkage patterns through intra-destination visitor movement: A network motif perspective

• Ding Ding^1,2,
• Yunhao Zheng^1,2,
• Yi Zhang^1,2 &
• …
• Yu Liu 
  ORCID: orcid.org/0000-0002-0016-2902^1,2 

Humanities and Social Sciences Communications
volume 11, Article number:636 (2024)
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• Commerce and administration
• The interplay between science, technology, and societal development

Abstract

Tourist movement between attractions is intricate and dynamic, and comprehending these patterns can improve the management of tourist destinations. Yet, prior research on tourist movement employing complex networks has not fully investigated the network motif method. Consequently, this study applies a network motif approach with social media data to identify and examine motifs within a city network. The analysis focuses on the attractions represented as nodes in each motif, uncovering the interaction patterns among them. Additionally, motifs linking attractions of varying types and titles are explored. While popular attractions hold substantial influence in the local network, others fulfill unique roles. The results highlight the value of network motifs in studying tourist movement and provide deeper insights into recurring visitation patterns. Furthermore, they support destination managers in crafting policy tools for smart tourism marketing and planning tailored to tourist preferences.

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Introduction

Tourism has become a dominant force in the global economy, outpacing some conventional sectors and acting as a key driver for economic expansion both internationally and regionally. In particular, urban tourism forms the foundation of modern tourism and has achieved a mature phase of competitive development (Cárdenas-García et al., 2024; W. Su et al., 2003). It has opened commercial opportunities for countless businesses in iconic tourist hubs like Paris, New York, and Tokyo, while also generating substantial employment for city residents and sustaining economic vitality in these locations (Hassan, 2000). Today’s urban tourists are no longer confined by strict schedules or predetermined routes, with their mobility patterns emphasizing greater flexibility in time and space. This shift aligns with John Urry’s ‘new mobility paradigm,’ which examines the evolving dynamics of movement (Korstanje, 2018; Merriman, 2012; Tzanelli, 2021; Urry (2008)).

The swift advancement of information and communication technologies has driven the broad adoption of mobile devices integrated with positioning capabilities. The extensive user location data gathered by these devices has greatly improved insights into tourist movement patterns in recent years (Chen et al., 2022; Chuang, 2023; Jiang & Phoong, 2023; Leng et al., 2021; Nguyen & Nguyen, 2023; Park et al., 2023; Xu et al., 2024). Utilizing these datasets, various research approaches and frameworks have been applied to study tourist mobility, such as geographic information systems (Lau & McKercher, 2006), time geography (Grinberger et al., 2014; Xiao-Ting & Bi-Hu, 2012), and Markov chains (Vu et al., 2015; Xia et al., 2009). Tourism scholars have sought to grasp the core of tourist mobility, as it is crucial for attraction promotion, event coordination, and the planning and development of urban attractions. Examining tourist mobility within a single city enables more precise decision-making by managers compared to analyzing movement across larger regions, like inter-destination travel. A common practice involves compiling individual mobility data into networks, which are then used to examine the topological framework of attraction systems (Vu et al., 2015).

Tourist mobility data form a network structure where attractions serve as nodes and the movement between them constitutes bonds (Kang et al., 2018). As a result, network analysis has become a widely adopted data mining method for uncovering the linkage patterns among attractions as tourists traverse a geographic area (García-Palomares et al., 2015; Han et al., 2018; Leung et al., 2012; Mou et al., 2020a; Peng et al., 2016; Xu et al., 2022; Zeng, 2018). Analyzing the network properties of tourist attractions holds practical significance for enhancing their competitiveness, management, and strategic planning (Stienmetz & Fesenmaier, 2015).

Despite the prevalence of descriptive measurements in social network analysis for studying tourist mobility patterns, this method limits the ability to evaluate the reliability and validity of the observed trends (Park & Zhong, 2022). This research focuses on network motifs, defined as repeated and statistically meaningful subgraphs within a broader graph. As a key focus in complex network theory (Ahmed et al., 2017), motifs uncover functional attributes derived from the structural features of network systems. Analyzing motifs in tourism networks improves comprehension of destination linkages, tourist flows, and the impact of policies on network structure and behavior. Furthermore, unlike the travel motifs used in earlier research (Park & Zhong, 2022; Yang et al., 2017), network motifs offer deeper insights into aggregated tourist movement patterns. As a result, motifs prove valuable in pinpointing the most central or influential destinations within tourism networks.

To summarize, this research addresses three key questions: (1) What categories of motifs do attractions form? (2) In what ways are motifs associated with particular attractions? (3) How do motifs correlate with attraction characteristics? As the inaugural study employing network motifs to analyze group tourist movement, Suzhou City serves as the case study location, utilizing social media data to track tourist movements, facilitating the linkage of network nodes to specific attractions. The paper is structured as follows: The Literature Review section provides an overview of existing research on tourist mobility and network motifs. The Methodology section details the dataset and the motif discovery technique applied for analysis. The Results section examines the findings from motif discovery. The Discussion section explores the study’s outcomes and their significance for tourism. Finally, the Conclusion section outlines the study’s key takeaways.

A review of existing literature

Studies on network motifs

Network motifs are recurring patterns of connections found in complex networks at frequencies substantially greater than those observed in randomized networks (Milo et al., 2002). These motifs help describe the functional and dynamic properties of a network, allowing networks to be categorized through statistical evaluation (Roy et al., 2023). Their applications extend to social interactions, protein structures, and information systems (Yu et al., 2020). Present techniques for identifying network motifs fall into two groups: network-centric and motif-centric methods. Network-centric approaches, like Mfinder (Kashtan et al., 2002), FanMod (Wernicke & Rasche, 2006), Kavosh (Kashani et al., 2009), and G-tries (Ribeiro & Silva, 2010), systematically list all subgraphs within a network. In contrast, motif-centric strategies, such as Grochow (Grochow & Kellis, 2007) and MODA (Omidi et al., 2009), focus on locating a specific query graph and then determining its occurrence rates instead of exhaustive enumeration, leveraging subgraph symmetry.

In the context of applying complex network science to tourism research, earlier works have explored tourist flow networks by analyzing both inter-destination (Liu et al., 2017; Peng et al., 2016; Shih, 2006; Wang et al., 2020) and intra-destination dynamics (Gao et al., 2022; Hwang et al., 2006; Leung et al., 2012; Mou et al., 2020; Zeng, 2018; Zheng et al., 2021). A review of these studies reveals two primary categories of metrics: those focused on the network level and those centered on individual nodes. Network-level measures encompass density, efficiency, diameter, average shortest path, average clustering coefficient, and centralization. Node-level metrics consist of degree (out and in), degree centrality (out and in), closeness centrality, and betweenness centrality. Additionally, techniques like structural holes and core-periphery analyses have been utilized to examine network structures.

While motif discovery is a vital field in network science, its use in tourism research has remained relatively unexplored. Pioneering work by Cao et al. (2019), Schneider et al. (2013), and R. Su et al. (2020) has advanced the application of network motifs to human mobility analysis. Rather than focusing exclusively on subgraphs in mobility networks, scholars have developed the idea of travel motifs, extending their scope from topological structures to incorporate temporal and semantic aspects (Yang et al., 2017). The earliest known reference to tourism network motifs appeared in a global tourism network investigation (Lozano & Gutiérrez, 2018), which identified several motifs such as transitive feedforward loops and various one and two mutual-dyad subgraphs. Additionally, research in South Korea proposed a network motif algorithm to analyze place-based travel pattern relationships in tourism (Park & Zhong, 2022), though it excluded the spatial behavior of domestic tourists. Furthermore, due to the limitations of cell tower data in pinpointing exact user locations and associated tourist attractions, this study utilizes social media data to map each traveler’s movements to specific attractions, uncovering the underlying patterns of attraction connectivity.

Analyzing tourist movement patterns and network structures

Tourist mobility refers to the movement, distribution, and travel behaviors of tourists in both spatial and temporal dimensions (Hardy et al., 2020; Shoval et al., 2020). A critical component of this concept is the examination of spatial movement (Oppermann, 1995). Insights into tourists’ temporal and spatial movements are vital for infrastructure and transportation projects, product innovation, destination strategy, attraction development, and managing tourism’s social, environmental, and cultural effects (Lew & McKercher, 2006). While quantitative methods enhance the accuracy and dependability of tourist mobility research, the outcomes of such analyses are heavily influenced by the scales applied in these studies (Jin et al., 2018; Zhang et al., 2023).

Several studies on intra-destination mobility have performed analyses at the individual level. For example, Fennell (1996) investigated tourist movements across the Shetland Islands by evaluating space, time, perception, region, and core-periphery dynamics. Lew and McKercher (2006) employed an inductive approach rooted in urban transportation to pinpoint factors that shape tourist mobility within destinations. Early studies leaned toward abstract methodologies grounded in foundational tourist theories. A widely adopted framework for modeling tourist mobility, known for its reproducibility, is the Markov model. Xia et al. (2009) applied Markov chains to represent tourist mobility as a stochastic process, computing the likelihoods of movement patterns on an island. For a more practical and applicable method, semi-Markov models prove valuable in estimating both tourist movement probabilities and the appeal of specific attractions (Xia et al., 2011). Additionally, time geography serves as a conceptual tool for interpreting tourist mobility. By combining time geography with geographic information systems, Grinberger et al. (2014) categorized tourists using time-space allocation metrics, uncovering their decision-making processes and strategies under time and space limitations.

The practice of compiling individual-level mobility data into networks to study the topological framework of attraction systems is becoming more prevalent (Smallwood et al., 2011). These mobility patterns can be interpreted as networks, making them suitable for network-based analysis (Shih, 2006). Zach and Gretzel (2011) investigated the architecture of attraction networks through a core-periphery analysis, offering valuable insights for technology development and tourism marketing strategies. In their study, Leung et al. (2012) utilized social network and content analyses to identify key tourist destinations and primary movement trends in Beijing across three different timeframes. Network techniques are frequently combined with other analytical approaches. For instance, Liu et al. (2017) employed a quadratic assignment procedure on an attraction network to assess the link between geographic proximity and the network shaped by tourists’ spontaneous movements. Similarly, Mou et al. (2020a) merged social network analysis with conventional quantitative techniques to create an innovative research model. Metrics like the Annual Gini Index and Pearson correlation coefficient also prove useful in examining the spatiotemporal patterns of tourist behavior (Zheng et al., 2021).

Motif discovery algorithms are widely used in gene regulation networks, electronic circuits, and neuronal systems (Yu et al., 2020). Yet, research employing motif discovery techniques to investigate tourist mobility remains scarce. Notably, some studies explore travel motifs—extended from topological spaces to temporal and semantic dimensions—to identify tourist movement patterns (Yang et al., 2017). Variations in travel mobility patterns arise not just from differences in tourists’ lengths of stay and the topological configurations of their movements but also from the distribution of each mobility type (Park & Zhong, 2022). However, travel motifs capture only individual tourist movements, failing to represent aggregated-level patterns or serve as a foundation for analyzing attraction network topologies (Jin et al., 2018). To date, no study has utilized network motifs to assess tourist mobility at the individual-aggregation level. The sole exception is Lozano and Gutiérrez (2018), who employed UCINET 6.0’s network motif analysis to study the top three global tourism flows. This study contends that network motif analysis bridges a critical research gap in aggregation-level tourist mobility while offering theoretical insights for tourism planning and attraction network design.

Methodology

Research location

We chose Suzhou, China (Fig. 1a) as the research site. Situated in eastern China, just west of Shanghai, Suzhou is home to around five million people. Known for its rich tourism offerings, the city welcomed over 100 million domestic tourists each year prior to the COVID-19 outbreak. Suzhou is celebrated for its deep cultural and historical significance, particularly its classical gardens, which earned a place on the World Heritage List in the last century. The historic attractions in Suzhou span approximately 14 km².²Suzhou also boasts a scenic natural environment, featuring verdant mountains and shimmering lakes alongside its historical sites.

aThe placement of attractions within SuzhoubThe microblogs with geographic tags located in Suzhou.

Data gathering and preliminary processing

Social media data were mainly gathered from location-based mobile applications. Sina Weibo, often referred to as China’s version of Twitter, stands as the country’s leading social media platform, boasting more than 500 million active registered users who share 300 million microblogs each day (Kim et al., 2017). Utilizing the application programming interface offered by Sina Weibo, we extracted posts published in Suzhou between 12 April, 2012, and 31 October, 2016. The collected posts via the application programming interface included diverse user data, such as post identification (ID), user ID, content text, images, geographic details (longitude and latitude), and timestamps, as illustrated in Fig. 1b. By referencing the user ID, we could also retrieve profile details while adhering to privacy policies. This profile information encompasses registration location, gender, age, post count, follower numbers, and ‘follows.’

However, just a fraction of users participated in tourism-related activities. We inferred that these individuals were not residents of the area and needed to travel back to their home cities once their visit concluded. Following the dual-filtration method introduced by Su et al. (2020), we initially excluded local users by examining the locations listed in their Weibo profiles. For this research, the duration between a user’s initial and final post is considered their stay period. Drawing on earlier work (Girardin et al., 2008; García-Palomares et al., 2015), we removed users whose stays exceeded one month.

Tourism activities often serve purposes like entertainment or relaxation, but they can also occur during official or business trips. While such visits might include tourism-related actions, our study classified only self-motivated travelers to Suzhou as tourists. For data preprocessing, we identified tourists as users who posted microblogs within attractions listed on the Suzhou Tourism Bureau’s official website (http://tjj.suzhou.gov.cn/). Geolocation data from tagged microblogs helped verify visits to these designated sites. Applying these criteria, we filtered the dataset, resulting in 234,049 Weibo posts from 54,712 confirmed tourists. By organizing these microblogs chronologically, we reconstructed visitors’ movement patterns across the city. This allowed us to link trajectories to directional routes between attractions, forming an attraction network (Fig. 2).

Analyzing connection patterns of attractions through network motif examination

In networks, attractions are depicted as nodes and flows as edges, allowing tourist mobility patterns to be modeled as complex networks (Schneider et al., 2013). Consequently, we identified all recurring mobility patterns associated with the motifs present in the tourist flow network. To achieve this, we implemented a novel algorithm (Kavosh) optimized to detect k-size network motifs while consuming less memory and computational resources compared to alternative methods. The Kavosh algorithm operates by enumerating all k-size subgraphs within a specified graph, whether directed or undirected. As illustrated in Fig. 2, the algorithm follows three key stages: enumeration, random network generation, and motif recognition. Initially, it systematically lists all potential mobility patterns linked to subgraphs in the original network. The Kavosh algorithm employs the NAUTY algorithm to categorize isomorphic subgraphs, streamlining the process and reducing redundancy. Since not every pattern holds relevance, the algorithm produces numerous random networks and assesses the frequency of pattern occurrences across them. Finally, the statistical significance of each pattern in the input network is computed to determine motif identification. This involves applying statistical measures to identify probable motifs within the original network.

Frequency

This approach offers the most straightforward way to assess the importance of a motif. In a specific network, we make the assumption thatG_pbelongs to an isomorphism class as part of that category. Frequency refers to the count of instances whereG_pwithin the input network.

Z-score

This metric indicates the degree of randomness with which the class appeared in the given network. For the specified motifG_pThis metric is characterized as follows:

P-value

This metric reflects the count of random networks where a motif,G_pappears more frequently in the input network compared to the total count of random networks. Thus, theP-value spans between 0 and 1. A lower value indicates a more significant result.PThe higher the -value, the more important the motif becomes.

The input network contains identifiable motifs along with associated statistical metrics. As outlined earlier, the algorithm employs three distinct measures. These measures lack fixed thresholds for motif identification; stricter thresholds yield more accurate motifs. Based on prior experimental findings (Milo et al., 2002), a network motif can be defined by the following criteria:

1. 1.

   By employing a set of 1000 randomly generated networks, theP-p-value is less than 0.01.

2. 2.

   The frequency exceeds four.

3. 3.

   Based on 1000 randomly generated networks, the Z-score exceeds 1.

Employing a set of 1000 randomly generated networks, theP-p-value is less than 0.01.

With 1000 randomly generated networks, the Z-score exceeds 1.

In line with the specified criteria, striving for maximum accuracy, the patterns displaying notable measures are the ones that characterize the network motifs.

Results

Distinctive patterns identified within the network of tourist attractions

The Sina Weibo data examined in this research covered 104 tourist sites in Suzhou City (Fig. 1a). Following the methodology outlined earlier, the Sina Weibo dataset formed a tourism network with 2171 edges. For k-motif identification, the occurrence of (k-1) motifs in the actual network must match their frequency in the randomized network (Yu et al., 2020). In this analysis, motifs exceeding four nodes failed to meet extraction criteria, so only three- and four-node motifs were isolated. Using the established criteria, we identified three three-node motifs and six four-node motifs, as illustrated in Figs. 3 and 4.

Beneath every motif in Fig. 3 is the percentage of that motif’s occurrence in the network, with its ID displayed in the top-right corner of each cell. Nodes in the graph also feature corresponding labels, as seen in motifs 1 and 4. The labeling for the remaining motifs follows the same pattern, though not all are marked to maintain clarity. Following prior research on motif classification (Costa et al., 2007; Yang et al., 2017), we categorized motifs into four fundamental types: chain, mutual dyad, double-linked mutual dyad, and fully connected triad. The chain-class motif represents tourists moving sequentially through three attractions without revisiting. Likewise, the double-linked mutual dyad motif indicates bidirectional movement between two pairs of attractions. The fully connected triad motif describes a group of three attractions where any two pairs exhibit bidirectional flow.

Within these, the mutual dyad, double-linked mutual dyad, and fully connected triad include both uplinked and downlinked versions in their primary classifications. For instance, when node A of a mutual dyad directs tourists to a different attraction, the motif is labeled as ‘uplinked.’ Conversely, if node A accepts tourists from another attraction, the motif is termed ‘downlinked.’ Similarly, these conventions apply to double-linked mutual dyads and fully connected triads. Additionally, the centrally linked mutual dyad and the fully connected triad with a mutual dyad represent more specialized forms. The centrally linked mutual dyad features a central attraction encircled by three nodes engaged in mutual circulation, yet none of these three attractions are interconnected. Meanwhile, a fully connected triad with a mutual dyad consists of one fully connected triad where a single node also forms an additional mutual dyad.

Among the nine motifs mentioned earlier, those labeled as IDs 1, 2, and 3 are three-node motifs, representing 37.61% of all network subgraphs. This implies that tourist movement among any three attractions in the tourism network is primarily characterized by chaining, reflecting a sequential order in most connections between these three-attraction patterns. The other six motifs, consisting of four nodes, make up 29.67%, with three motifs (IDs 4, 5, and 6) organized around a central point in the lower left corner. The centrally linked motif aligns with a movement pattern known as a ‘basecamp’ in prior research (Lau & McKercher, 2006; Lue et al., 1993; Oppermann, 1995), where tourists select one attraction as their base and venture out to explore others before returning. In the downlinked version, this basecamp functioned as a gateway attraction for visiting attractions B and C. The motifs with IDs 7, 8, and 9 were predominantly structured as a fully connected triple attraction, featuring three tightly interlinked attractions allowing unrestricted tourist movement. Beyond this triple attraction, we also observed a connection between one attraction and one of the three attractions, demonstrating receiving, conveying, and circulating relationships. Thus, in the context of four-attraction connection patterns, the role of key attractions is both distinct and crucial.

Interpretation of motifs: Distinctive points of interest

In this research, all identified motifs represented the common local movement patterns of tourists that appeared frequently within the original tourist network. The subsequent analysis focused on the distribution of attractions across each node of the extracted motifs. For every motif, the node with the greatest degree was chosen, and the attractions present on that node were tallied across all subgraphs in the tourist network. The three most frequently occurring attractions on the highest-degree node were then selected based on their prevalence, as displayed in Table 1. Within the table, the highest-degree nodes are highlighted in orange.

Table 1 reveals that Guanqian Street, Jinji Lake, and Pingjiang Road are the highest-ranked nodes in the subgraph, demonstrating their central role within the overall network. Essentially, the network structure revolves around these three attractions, shaping the predominant movement patterns of local tourists. Other key nodes, such as Zhouzhuang and Hanshan Temple, also hold significant positions in the network. Zhouzhuang frequently appears as a transitional node in motifs, serving as a gateway to other destinations. Notably, in motif patterns 3 and 9, visitors typically do not return to Zhouzhuang after their visit but proceed to linked attractions, likely due to its distance from Suzhou’s urban center. In contrast, Hanshan Temple follows an inverse trend, with its associated motif patterns (2, 4, and 7) showing it as a convergence point before tourists move to other destinations via node B. Our analysis also identifies the top three attractions in node B: Tongli National Wetland Park, China Flower Botanical Garden, and Dabaidang Ecological Park. These locations share a defining feature—abundant floral diversity and lush vegetation, making them ideal for springtime hikes.

Motif interpretation: Categories and names of attractions

Beyond analyzing nodes with the highest motif degrees, this research investigates the categories and ratings of attractions associated with each node. Based on the framework introduced by Xue & Zhang (2020) in Suzhou, attractions are grouped into natural, cultural, and commercial types depending on their landscape characteristics. They are also categorized by their official ratings, such as 5A, 4A, or others (a ‘5A’ designation indicates superior scenery, exceptional service, and top-tier facilities). The allocation of attraction types across nodes is displayed in Fig. 5, while Fig. 6 illustrates the distribution of attraction ratings. Node labels in the bottom-right corner of each figure denote their positions within the motifs, and these labels are referenced in later sections.

Figures 5 and 6 demonstrate that the attractions on each node vary in type, though the distinctions between node attraction types across motifs within each major category are minimal. This suggests that every primary category of attraction connection pattern represents a shared group of tourists’ local movement behaviors, with the characteristics of each attraction in these patterns remaining consistent.

Varieties of attractions

In Fig. 5, the node attraction types show minimal variation for the chain-type motif. For the mutual dyad type, node A maintains a relatively even distribution, while nodes B and C display contrasting dominance patterns. Specifically, in motif 2, node B is predominantly natural-type attractions, whereas node C consists of over 50% cultural attractions. Motif 3 reverses this trend. The double-linked mutual dyad reveals more distinct features. First, nodes C and D share identical attraction type proportions across all three motifs, with variations mainly in nodes A and B. Second, in IDs 4 and 5, node A’s commercial attractions dominate, underscoring their function in drawing tourists within the local network. Conversely, in motif 6, node A acts chiefly as a transitional point between B, C, and D, with no significant prominence in commercial attractions. Finally, the fully connected triad maintains uniform proportions of interconnected nodes across motifs. Nodes C and D are primarily cultural attractions, while node A, serving as a central link, shows a more even mix of attraction types.

Names of attractions

In Fig. 6, the proportions of 5A and 4A attractions on nodes lacking attraction titles are notably smaller compared to nodes featuring famous attraction titles. When examining the share of attraction titles per node, the disparity in the percentages of B and C node levels within the chain-type motif is minimal. Additionally, these nodes are primarily composed of lesser-known attractions. Conversely, node A serves as a transit point for famous attractions at a markedly higher rate than nodes B and C. The pattern for node A in the mutual Dyad type mirrors that of the chain type, displaying a greater prevalence of famous attractions, while nodes B and C exhibit an inverse relationship. If node B has a higher concentration of famous attractions, node C is predominantly filled with non-famous ones, and the opposite holds true. For the double-linked mutual dyad type, node A contains a substantially larger share of famous attractions than the remaining nodes. Yet, the other nodes show no significant variation in attraction title percentages, irrespective of their bidirectional flow with node A, and are overwhelmingly populated by non-famous attractions. In motifs with a fully connected triad, the three interconnected nodes display a high proportion of titled attractions, suggesting substantial tourist movement between 5A and 4A attractions. On the other hand, most B nodes linked exclusively to A nodes consist of non-famous attractions, demonstrating that attractions without prominent titles struggle to form strong connections with 5A and 4A sites.

Discussion

We employed network motif analysis as an innovative method to investigate the local structure of tourist networks using social media data. The broader structural characteristics of the network were derived from localized relational patterns. Understanding the dynamics of tourist network formation requires attention to both the global network view and the intricacies of local connections. The findings revealed that attractions hold significant influence in local networks, with their impact varying based on their category and prominence. Consequently, advancing Suzhou’s tourism industry depends on strategically directing attractions to serve their designated roles effectively within the city.

Traveler movement trends

This research utilizes the theory of motifs, derived from complex network science, as a novel method for analyzing tourist movement behaviors. Initially developed in biology, the network motif algorithm for complex systems was adapted here to explore connections between excessive tourism mobility trends and related attractions. Unlike traditional techniques for identifying travel motifs, the outcomes of motif analysis within the network do not directly reveal individual tourists’ city itineraries. Instead, the motif-based approach emphasizes the movement trends of tourist groups among closely linked attractions. Given this feature, network motif analysis proves more effective for examining localized patterns.

To analyze tourist movement in urban destinations, a directed graph can be created to map travel between attractions based on network science principles. Using motif extraction on this graph reveals that the broader network is composed of recurring basic structures (Fig. 7). The research identified four categories and nine motifs that effectively capture the range of movement behaviors. This suggests that, regardless of individual travel variations, people adhere to consistent, predictable patterns (González et al., 2008). Studying these mobility trends improves the understanding of urban destination systems and offers valuable guidance for planning and development (Ashworth & Page, 2011).

Patterns of connectivity among attractions

This research analyzes the linkage patterns within a tourist network to pinpoint attractions with specific roles, such as core, transit, and gateway attractions. The importance of tourist attractions varies across destinations, and the hierarchical organization of urban destination systems depends on their appeal to visitors (Golledge, 1978). From a marketing perspective, these insights enable marketers to grasp the role of attractions in travel itineraries and lay the groundwork for creating tailored tourism offerings. For instance, comprehensive tourism planning should focus on core attractions, while additional transit routes should be designed for transit attractions. Gateway attractions, on the other hand, would benefit from improved hotel and guide services in their vicinity. However, the findings highlight that the key nodes in the nine motifs predominantly consist of a handful of the most famous attractions. From a risk management standpoint, an overly concentrated destination could lead to ‘overtourism’ (Peeters et al., 2018).

Considerations for managing tourism effectively

Analyzing tourist movement is crucial for tourism managers to design and execute successful sustainability initiatives (Shi et al., 2017). The tourist network explored in this research tracks visitor flows between attractions, offering an innovative method to study movement trends at destinations and precisely represents tourists’ digital traces (Fan et al., 2024). Findings reveal that while visitors’ specific travel routes between locations are intricate, the relationships among local attractions in the network can be categorized into distinct patterns. This indicates that despite varying tourist preferences, shared spatial behaviors exist, enhancing the collective profile of visiting groups. Consequently, destinations can optimize attraction development and alternatives by identifying favored travel paths through movement pattern analysis (Vu et al., 2015).

Conclusions

As urban areas grow into hubs of economic development, emerging types of city-based tourism are gaining traction, with more travelers selecting metropolitan destinations to seek unique, varied, and customized vacation experiences (Füller & Michel, 2014). This research applies motif analysis from complex network science to uncover visitor movement trends and illustrate the relationships between attraction networks in Suzhou, China. By uniquely treating actual tourist sites as network nodes and concentrating on attraction linkage patterns, the study offers actionable insights for managing destinations. Key findings are outlined below:

• Using the motif detection technique called Kavosh, we identified nine motifs within a tourist network in Suzhou. These nine motifs fall into four primary categories: chain, mutual dyad, double-linked mutual dyad, and fully connected triad.

• The motifs’ nodes correspond to distinct attractions, each analyzed in detail. Guanqian Street, Jinji Lake, and Pingjiang Road serve as the central hubs in Suzhou’s network, shaping the majority of linkage structures among local sites. Meanwhile, destinations like Zhouzhuang and Hanshan Temple fulfill specialized roles within the network.

• The variety and distribution of attractions were analyzed by mapping regional tourist movement trends within the network. Findings indicated that nodes exhibiting a greater motif density typically corresponded to prominent 5A or 4A-rated sites, primarily consisting of cultural and commercial destinations.

Using the motif detection approach called Kavosh, we identified nine motifs within a tourist network in Suzhou. These nine motifs fall into four primary categories: chain, mutual dyad, double-linked mutual dyad, and fully connected triad.

The motifs’ nodes correspond to particular attractions, which are examined in detail. Guanqian Street, Jinji Lake, and Pingjiang Road serve as the core attractions structuring Suzhou’s network, shaping the majority of connection patterns among local sites. Meanwhile, locations like the Zhouzhuang and the Hanshan Temple fulfill distinct roles within the network.

The variety and distribution of attractions were analyzed by mapping regional tourist movement trends within the network. Findings indicated that nodes exhibiting a greater motif density typically corresponded to prominent 5A or 4A-rated sites, primarily consisting of cultural and commercial destinations.

The findings offer a novel methodological approach for analyzing connectivity patterns in local attraction networks, while also establishing a foundation for managing tourist sites in urban destinations.

While this research offers valuable theoretical and practical contributions, certain limitations should be noted. Social media data, for instance, are subject to multiple biases, including the varying preferences for platforms among users, as well as disparities in data volume across countries, years, and demographics. The influence of highly active users may lead to an exaggerated representation of these groups (Encalada-Abarca et al., 2023). Furthermore, the dataset primarily captures tourists’ spatial movements within a single urban area. Since spatial behavior patterns vary across destinations, future studies should incorporate tourism networks from multiple locations to enable meaningful comparisons and enhance the generalizability of these results. Subsequent research could also investigate the underlying factors in attraction selection through mobility motifs, such as tourists’ pursuit of satisfaction when designing their travel routes.

Accessibility of data

The data collected and examined in this study can be obtained from the corresponding author upon reasonable request.

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Kashtan N, Itzkovitz S, Milo R, Alon U (2002).Mfinder Tool Guide: Technical Documentation

Kim S-E, Lee KY, Shin SI, Yang S-B (2017) The influence of tourism information quality within social media on shaping destination image: A study focusing on Sina Weibo. Inf Manag 54(6):687–702. https://doi.org/10.1016/j.im.2017.02.009

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Lau G, McKercher B (2006) Analyzing tourist movement patterns within a destination using GIS. Tour Hosp Res 7(1):39–49. https://doi.org/10.1057/palgrave.thr.6050027

Leng Y, Babwany NA, Pentland A (2021) Exploring the link between socioeconomic diversity and the consumer price index within a tourism-driven nation. Humanit Soc Sci Commun 8:157. https://doi.org/10.1057/s41599-021-00822-w

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Lew A, McKercher B (2006) Analyzing patterns of tourist mobility. Ann Tour Res 33(2):403–423. https://doi.org/10.1016/j.annals.2005.12.002

Liu B, Huang S, Fu H, Fu H, Fu H (2017) Utilizing network analysis to examine tourist attractions: A case study of Xinjiang, China. Tour Manag 58:132–141. https://doi.org/10.1016/j.tourman.2016.10.009

Lozano S, Gutiérrez E (2018) An examination of worldwide tourism movements using complex network analysis. Int J Tour Res 20(5):588–604. https://doi.org/10.1002/jtr.2208

Lue C-C, Crompton JL, Fesenmaier DR (1993) developed a framework for understanding multi-destination leisure travel. Their study was published in Annals of Tourism Research, volume 20, issue 2, pages 289–301. The article can be accessed via https://doi.org/10.1016/0160-7383(93)90056-9.

Merriman P (2012) Mobility, space and culture. Routledge, New York. https://doi.org/10.4337/9781800881426

Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: Fundamental components of intricate networks. Science 298(5594):824–827. https://doi.org/10.1126/science.298.5594.824

Mou N, Zheng Y, Makkonen T, Yang T, Tang J, Song Y (2020) The digital traces of tourists: Spatial distribution of visitor movements in Qingdao, China. Tour Manag 81:104151. https://doi.org/10.1016/j.tourman.2020.104151

Nguyen HTT, Nguyen TX (2023) Exploring guest perceptions of Vietnamese hotels through an examination of digital feedback. Humanit Soc Sci Commun 10:618. https://doi.org/10.1057/s41599-023-02098-8

Omidi S, Schreiber F, Masoudi-Nejad A (2009) MODA: A high-performance algorithm for identifying network motifs in biological networks. Genes Genet Syst 84(5):385–395. https://doi.org/10.1266/ggs.84.385

Oppermann M (1995) proposed a framework for analyzing travel routes. J Travel Res 33(4):57–61. https://doi.org/10.1177/004728759503300409

Park S, Zhong RR (2022) Identifying travel movement patterns in urban destinations: Utilizing network motif analysis. J Travel Res 61(5):1201–1216. https://doi.org/10.1177/00472875211024739

Park S, Zu J, Xu Y, Zhang F, Liu Y, Li J (2023) Examining mobility trends in urban travel: Insights for destination planning. Tour Manag 96:104718. https://doi.org/10.1016/j.tourman.2022.104718

Peeters P, Gössling S, Klijs J, Milano C, Novelli M, Dijkmans C, Eijgelaar E, Hartman S, Heslinga J, Isaac R, Mitas O (2018) examined the effects of overtourism and explored potential policy solutions. Their findings were published in Res Trans Comm, volume 23, page 19.

Peng H, Zhang J, Liu Z, Lu L, Yang L (2016) Examining tourist movement patterns through network analysis: A study across provincial borders. Tour Geogr 18(5):561–586. https://doi.org/10.1080/14616688.2016.1221443

Ribeiro P, Silva F (2010) g-tries: A high-performance data structure for identifying network motifs. In: Proceedings of the 2010 ACM symposium on applied computing, pp 1559–1566. https://doi.org/10.1145/1774088.1774422

Roy S, Al Musawi AF, Ghosh P (2023) Predicting connections in directed complex networks using feed forward loop motifs. Humanit Soc Sci Commun 10:358. https://doi.org/10.1057/s41599-023-01863-z

Schneider CM, Belik V, Couronné T, Smoreda Z, González MC (2013) Decoding the recurring patterns in daily human mobility. J R Soc Interface 10(84):20130246. https://doi.org/10.1098/rsif.2013.0246

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Acknowledgements

We extend our sincere gratitude to Prof. Yang Xu for his guidance in writing and conceptual development. This research received funding from the National Natural Science Foundation of China (Grant No. 41830645) and the Yunnan Provincial Science and Technology Project at Southwest United Graduate School (Grant No. 202302AO370012). The first author, Ding, is deeply grateful to his fiancée, Lanqi Liu, for her assistance with coding and figure preparation. Her unwavering support during Ding’s challenging periods was instrumental in the successful completion and publication of this work.

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1. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

   Ding Ding, Yunhao Zheng, Yi Zhang, and Yu Liu

2. Southwest United Graduate School, located in Kunming, China

   Ding Ding, Yunhao Zheng, Yi Zhang, and Yu Liu

Peking University’s School of Earth and Space Sciences, located in Beijing, People’s Republic of China, houses the Institute of Remote Sensing and Geographical Information Systems.

Southwest United Graduate School, located in Kunming, China

1. Ding Ding
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Contributions

Ding Ding: Study design, Methodology, Formal analysis, Research, Visualization, Draft preparation. Yunhao Zheng: Conceptualization, Methodology, Visualization, Manuscript revision. Yi Zhang: Conceptualization, Data management, Validation, Reviewing. Yu Liu: Resource provision, Reviewing, Project oversight, Supervision, Funding acquisition.

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Reference this article

Ding, D., Zheng, Y., and Zhang, Y.et al.Analyzing attraction linkage patterns through intra-destination visitor movement: A network motif perspective.Humanities and Social Sciences Communications 11, 636 (2024). https://doi.org/10.1057/s41599-024-03093-3

• Received: 19 October 2023

• Accepted: 22 April 2024

• Published: 17 May 2024

• DOI: https://doi.org/10.1057/s41599-024-03093-3

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