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 behaviors among attractions are intricate and diverse, 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, we implemented a network motif approach leveraging social media data to identify and study motifs within a city network. This research examines the attractions represented as nodes in each motif, uncovering the linkage patterns among them. We also explore motifs connecting attractions of varying types and titles. Highly frequented attractions hold substantial influence in a local network, while others fulfill unique roles. The results of this study highlight the importance of network motifs in analyzing tourist movement and provide deeper insights into recurring visitation patterns. Additionally, 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, outstripping some conventional sectors and acting as a key driver of 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 up commercial opportunities for countless businesses in iconic tourist destinations like Paris, New York, and Tokyo, while also generating substantial employment for city residents and sustaining long-term economic vitality in these areas (Hassan, 2000). Today, tourists’ movements within cities are no longer constrained by fixed 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 analytical 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 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 structural topology 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 connection 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 social network analysis in current literature to study tourist mobility patterns, most studies depend on descriptive measurements. This limitation restricts the ability to evaluate the reliability and validity of the observed patterns (Park & Zhong, 2022). The present research focuses on network motifs, defined as recurrent and statistically meaningful subgraphs within a broader network. As a key focus in complex network theory (Ahmed et al., 2017), motifs uncover functional attributes derived from the structural features of network systems. Investigating motifs in tourism networks improves comprehension of destination connectivity, tourist movement between locations, and the impact of tourism policies on network structure and behavior. Furthermore, unlike the travel motifs used in prior research (Park & Zhong, 2022; Yang et al., 2017), network motifs offer deeper insights into aggregated individual tourist mobility. As a result, motifs serve as a valuable tool for 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 the attributes of attractions? As the inaugural study employing network motifs to analyze group tourist movement, we focus on Suzhou City as the case study location and utilize social media data to track tourists’ movements, simplifying the process of linking 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 the motif discovery process. The Discussion section explores the outcomes of the study and their significance for tourism. Finally, the Conclusion section outlines the study’s key takeaways.

A review of 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). Existing 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 calculating 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 through both inter-destination (Liu et al., 2017; Peng et al., 2016; Shih, 2006; Wang et al., 2020) and intra-destination (Gao et al., 2022; Hwang et al., 2006; Leung et al., 2012; Mou et al., 2020; Zeng, 2018; Zheng et al., 2021) lenses. 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 centralisation. Node-level metrics consist of degree (out and in), degree centrality (out and in), closeness centrality, and betweenness centrality. Additionally, analytical techniques like structural holes and core-periphery analyses have been utilized to examine network structures.

While motif discovery is a key focus in network science, its use in tourism research remains underexplored. Pioneering work by Cao et al. (2019), Schneider et al. (2013), and R. Su et al. (2020) has applied network motifs to human mobility analysis. Moving beyond simple subgraph analysis in mobility networks, scholars like Yang et al. (2017) developed the idea of travel motifs, extending the concept to incorporate temporal and semantic aspects. The earliest reference to tourism network motifs appears in a global tourism network investigation by Lozano & Gutiérrez (2018), which detected various motifs such as transitive feedforward loops and one or two mutual-dyad subgraphs. Additionally, Park & Zhong (2022) proposed a network motif algorithm in a South Korean study to analyze place-based travel pattern relationships in tourism, though it excluded local tourists’ spatial behavior. Due to limitations in mobile sensor data from cell towers—which make it difficult to pinpoint exact user locations and associated attractions—this research instead utilizes social media data to map tourists’ spatial behavior to specific attractions, uncovering patterns in attraction connectivity.

Analyzing tourist movement and network patterns

Tourist mobility refers to the circulation, transit, distribution, and behavioral trends of tourists in both spatial and temporal dimensions (Hardy et al., 2020; Shoval et al., 2020). A critical component of tourist mobility involves examining spatial movement (Oppermann, 1995). Insights into tourists’ temporal and spatial movement patterns hold substantial relevance for infrastructure and transport planning, product innovation, destination strategy, attraction development, and mitigating tourism’s social, environmental, and cultural effects (Lew & McKercher, 2006). While quantitative analysis enhances the accuracy and dependability of tourist mobility research, the outcomes of such analyses are largely influenced by the scales applied in these studies (Jin et al., 2018; Zhang et al., 2023).

Several studies on intra-destination mobility have focused on individual-level analysis. 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) applied an inductive framework rooted in urban transportation to pinpoint key factors shaping tourist mobility within destinations. Initial research leaned toward theoretical methods grounded in core tourist propositions. A widely adopted technique for modeling tourist mobility, known for its reproducibility, is the Markov model. Xia et al. (2009) employed Markov chains to represent tourist mobility as a stochastic process, estimating the likelihoods of movement patterns on an island. For greater practicality, semi-Markov models prove valuable in determining 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 behavior metrics, uncovering their decision-making processes and strategies under temporal and spatial limitations.

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

Motif discovery algorithms are frequently employed in analyzing gene regulation networks, electronic circuits, and neurons (Yu et al., 2020). Nonetheless, research utilizing motif discovery techniques to investigate tourist mobility remains scarce. It should be noted that some studies explore travel motifs (expanded from topological spaces to temporal and semantic dimensions) to identify tourist movement patterns (Yang et al., 2017). Indeed, differences in travel mobility patterns arise not just from varying lengths of stay and topological structures but also from the distribution of each mobility type (Park & Zhong, 2022). However, travel motifs capture only individual tourist movements rather than aggregated-individual level patterns, much less providing a foundation for analyzing attraction network topologies (Jin et al., 2018). To date, no study has applied network motifs to assess tourist mobility at the individual-aggregation level. The sole exception is Lozano and Gutiérrez (2018), who used UCINET 6.0’s network motif tool to evaluate the top three global tourism flows. Consequently, this study contends that network motif analysis addresses a research void in aggregation-level tourist mobility while also offering theoretical insights for tourism attraction network planning and 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 five million inhabitants. Known for its abundant tourism offerings, the city welcomed over 100 million domestic tourists each year prior to the COVID-19 outbreak. Suzhou is celebrated for its rich cultural and historical legacy, particularly its classical gardens, which were added to the World Heritage List in the last century. The historic attractions in Suzhou’s old town span approximately 14 km².²Beyond its historical sites, Suzhou boasts a scenic natural environment featuring verdant hills and shimmering waterways.

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

Gathering data and preparing it for analysis

Location-based mobile phone applications served as the primary source for gathering social media data. In China, Sina Weibo, often compared to Twitter, stands as the leading social media platform, boasting more than 500 million active registered users who share 300 million microblogs each day (Kim et al., 2017). Utilizing Sina Weibo’s application programming interface, we extracted posts published in Suzhou between 12 April, 2012, and 31 October, 2016. The collected posts included multiple user-related details, such as post ID, user ID, text content, images, geographic coordinates (longitude and latitude), and timestamps, illustrated in Fig. 1b. By referencing the user ID, we additionally obtained profile data while adhering to privacy guidelines. This profile information encompassed registration location, gender, age, post count, fan numbers, and ‘follows.’

Only a fraction of users engaged in tourism-related activities. We inferred that these individuals were non-locals who needed to return to their home cities after traveling. Following the dual-filtering 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 was considered their length of stay. Drawing on prior work by Girardin et al. (2008) and García-Palomares et al. (2015), we removed users whose stays exceeded one month.

Tourism activities may serve purposes such as leisure or entertainment, but they can also occur during formal or business-related trips. While official or business visits might include tourism, our study classified only those visitors who traveled to Suzhou voluntarily as tourists. For data preprocessing, we identified tourists exclusively as users who posted microblogs from locations included in the Suzhou Tourism Bureau’s official directory (http://tjj.suzhou.gov.cn/). To verify visits to these attractions, we relied on the geographic coordinates embedded in the geo-tagged microblogs. Applying these filtering criteria, we collected 234,049 Weibo posts from 54,712 tourists. By organizing these microblogs chronologically, we reconstructed the movement patterns of tourists across the city. This enabled us to link these trajectories to directional pathways between attractions, forming a network of tourist sites (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) that efficiently detects k-size network motifs while consuming less memory and computational resources compared to alternative methods. The Kavosh algorithm operates by counting 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 identification. Initially, it enumerates every possible mobility pattern linked to the subgraphs in the original network. The Kavosh algorithm then categorizes isomorphic subgraphs using the NAUTY algorithm, streamlining the process and reducing redundancy. Since not all patterns are meaningful, the algorithm produces numerous random networks and evaluates the frequency of these patterns across them. Finally, the significance of each pattern in the input network is assessed to determine motifs. 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 the context of a specific network, we suppose thatG_pbelongs to an isomorphism class as part of that category. Frequency refers to the count of instances whereG_pin the given network.

Z-score

This metric indicates the likelihood of the class appearing by chance within the given network. For the specified motifG_pThis metric is calculated as follows:

P-value

This metric quantifies how many randomly generated networks contain a specific motif,G_pappears more frequently in the input network compared to the total count of random networks. Thus, thePThe value spans between 0 and 1. A lower value indicates a smaller magnitude.PThe higher the value, the more important the motif becomes.

Thus, the input network contains identifiable motifs along with associated statistical metrics. As outlined earlier, the algorithm employs three distinct measures. No fixed thresholds exist to definitively classify motifs; stricter thresholds yield more accurate motif identification. Based on prior experimental findings (Milo et al., 2002), a network motif can be characterized by the following criteria:

1. 1.

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

2. 2.

   The value exceeds four in frequency.

3. 3.

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

By employing a set of 1000 randomly generated networks, thePThe 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 define 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 2171 edges in the tourism network. For k-motif identification, the occurrence rate of (k-1) motifs in the actual network must match that 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 previously defined conditions, we identified the presence of motifs in the network. As a result, three three-node motifs and six four-node motifs were extracted, 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. The graph’s nodes 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 the figure’s clarity. Following earlier classifications of motifs (Costa et al., 2007; Yang et al., 2017), we categorized them into four fundamental types: chain, mutual dyad, double-linked mutual dyad, and fully connected triad. A chain-class motif represents tourists moving sequentially through three attractions without revisiting. Likewise, a double-linked mutual dyad motif indicates bidirectional tourist flow between two pairs of attractions. The fully connected triad motif describes a set 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 as primary classifications. For instance, when node A in a mutual dyad directs tourists to a different attraction, the motif is labeled ‘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 featuring a mutual dyad represent more specialized forms. The centrally linked mutual dyad revolves around a central attraction encircled by three nodes with reciprocal flows, yet none of these three attractions are interconnected. A fully connected triad with a mutual dyad consists of one fully connected triad where a single node also participates in an additional mutual dyad.

Among the nine motifs mentioned earlier, those labeled with IDs 1, 2, and 3 represent three-node motifs, making up 37.61% of all network subgraphs. This demonstrates that tourist movement between any three attractions in the tourism network is primarily chain-based, implying a sequential order in most connections among these three-attraction patterns. The other six motifs, consisting of four nodes, represent 29.67%, with three motifs (IDs 4, 5, and 6) organized around a central point in the lower left corner. The centrally connected motif aligns with a movement pattern known as a ‘basecamp’ in prior research (Lau & McKercher, 2006; Lue et al., 1993; Oppermann, 1995), where tourists designate 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 interconnected triple attraction, featuring three tightly linked attractions allowing unrestricted tourist movement. Alongside this triple attraction, we also observed a relationship between one attraction and one of the three, displaying interactions of receiving, conveying, and circulating. Thus, in the context of four-attraction connection patterns, the role of key attractions is both distinct and critical.

Analysis 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 within the extracted motifs. For every motif, the node with the greatest degree was chosen, and the attractions present in 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. In 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. This suggests that the network is structured primarily around these three attractions, which form the core of local tourist movement patterns. Other key attractions, such as Zhouzhuang and Hanshan Temple, also hold significant positions in the local 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 exhibits the opposite trend in its associated motif patterns (2, 4, and 7), where it often functions as a convergence point before tourists move to other locations. The top three attractions identified for node B are Tongli National Wetland Park, China Flower Botanical Garden, and Dabaidang Ecological Park. These sites share the distinction of featuring diverse, visually appealing flowers and extensive greenery, making them ideal for springtime hikes.

Motif analysis: Categories and names of attractions

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

Figures 5 and 6 demonstrate that while the attractions on each node vary, the distinctions in node attraction types across motifs within each primary category are minimal. This suggests that every major category of attraction connection pattern represents a shared class 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 within the chain-type motif. For the mutual dyad type, node A maintains a fairly even distribution, while nodes B and C display contrasting dominance patterns. Specifically, motif 2’s node B is primarily natural-type attractions, whereas node C consists of over 50% cultural attractions. Motif 3 reverses this trend. The double-linked mutual dyad demonstrates more distinct features. First, nodes C and D share identical attraction type proportions across all three motifs, with key variations occurring 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, motif 6’s node A acts mainly as a transitional point between B, C, and D, with no significant prevalence of commercial attractions. Finally, in the fully connected triad, the interconnected nodes maintain uniform proportions across motifs. Nodes C and D are predominantly 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 percentage of attraction titles per node, the disparity in B and C node levels for 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 mutual Dyad type exhibits a comparable pattern for node A, with a greater share of famous attractions, while nodes B and C display an inverse relationship. If node B has a higher concentration of famous attractions, node C is predominantly non-famous, and the opposite holds true. For the double-linked mutual dyad type, node A contains a substantially larger fraction of famous attractions relative to the other nodes. Yet, the remaining nodes show no significant variation in attraction title percentages, irrespective of their bidirectional flow with node A, and are mainly populated by non-famous attractions. In motifs with a fully connected triad, the three interconnected nodes exhibit a high prevalence of titled attractions, suggesting robust tourist movement between 5A and 4A sites. On the other hand, B nodes linked solely to A nodes are mostly non-famous, demonstrating that attractions without prominent titles struggle to form strong connections with 5A and 4A attractions.

Discussion

We utilized network motif analysis as an innovative method to examine the localized structure of tourist networks, drawing on 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 macro-level network view and micro-level connections. The findings revealed that attractions hold significant influence in local networks, with their impact varying based on their category and tier. Consequently, advancing Suzhou’s tourism industry depends on strategically directing attractions to perform their optimal service roles within the city.

Patterns of tourist movement and travel behavior

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 their linked attractions. Traditional approaches to identifying travel motifs differ, as network-based motif analysis does not directly reveal individual tourists’ city itineraries. Instead, the motif-driven analytical technique emphasizes collective tourist movement trends among closely interconnected attractions. Given this trait, network motif analysis proves more effective for examining localized patterns.

In urban tourist destinations, tourist movement between attractions can be represented as a large directed graph based on network science principles. Using the motif extraction approach on this graph reveals that the overall network is composed of basic recurring topologies (Fig. 7). The research identified four classes and nine motifs that effectively capture the varied mobility patterns. This suggests that, regardless of individual travel histories, people adhere to straightforward, repeatable patterns (González et al., 2008). Analyzing tourist mobility patterns improves the understanding of city destination systems and offers critical insights 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 functions, 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 create a basis for tailored tourism offerings. For instance, comprehensive tourism planning should focus on core attractions; transit attractions require additional transportation routes; and gateway attractions need improved hotel and guide services nearby. However, the findings highlight that key nodes in the nine motifs predominantly consist of a handful of the most famous attractions. In terms of risk management, an overly centralized destination could lead to ‘overtourism’ (Peeters et al., 2018).

Strategies for effective tourism governance

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 captures how visitors navigate between attractions, offering a fresh perspective for studying movement trends at destinations and precisely mapping their digital traces (Fan et al., 2024). Findings reveal that while individual travel routes are intricate, attractions in the network can be categorized into distinct patterns. This indicates that despite varying preferences, tourists share similarities in their spatial behaviors, enhancing the collective profile of visitor groups. Consequently, destinations can optimize attractions and develop 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 for unique, varied, and customized experiences (Füller & Michel, 2014). This research applies motif analysis from complex network science to uncover patterns in tourist movement and illustrate the relationships between attraction systems in Suzhou, China. By innovatively treating actual attractions as network nodes and concentrating on connection patterns between them, the study offers actionable insights for managing destinations. The 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 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 degree typically corresponded to prominent attractions designated as 5A or 4A, primarily consisting of cultural and commercial sites.

Using the Kavosh motif detection technique, 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, destinations 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 degree were typically prominent landmarks, often designated as 5A or 4A sites, and primarily consisted 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 urban tourism destinations and their attractions.

While this research offers valuable theoretical and practical contributions, certain limitations should be noted. Social media data, for instance, are subject to biases—such as the varying popularity of platforms among users—and the volume of data can differ across countries, years, and demographics. Highly active users may introduce bias, leading to an overrepresentation of certain groups (Encalada-Abarca et al., 2023). Furthermore, the data primarily capture tourists’ spatial movements within a single city. Since spatial behavior patterns vary across destinations, future studies should incorporate tourism networks from multiple locations to enable comparisons and enhance the generalizability of these findings. Subsequent research could also investigate the mechanisms behind attraction selection through mobility motifs, including how tourists prioritize satisfaction when designing their travel itineraries.

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) examined how the quality of tourism-related content on social media influences the development of destination image, with a focus on Sina Weibo. The study was published in Information & Management, volume 54, issue 6, pages 687–702. The article is available at https://doi.org/10.1016/j.im.2017.02.009.

Korstanje ME (2018) The theory of mobilities: an analytical critique. In: The Mobilities Paradox. Edward Elgar Publishing, Cheltenham, UK, pp. 10-37. https://doi.org/10.4337/9781788113311.00005

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

Leung XY, Wang F, Wu B, Bai B, Stahura KA, Xie Z (2012) An analysis of social networks examining international tourist flows in Beijing: The influence of the Olympic Games. Int J Tour Res 14(5):469–484. https://doi.org/10.1002/jtr.876

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 study focused on 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) A framework for understanding multi-destination leisure travel. Ann Tour Res 20(2):289–301. 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 customer perceptions of Vietnamese hotels through an analysis of online 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 potential solutions through policy measures. Their findings were published in Research in Transportation and Commerce, volume 23, page 19.

Peng H, Zhang J, Liu Z, Lu L, Yang L (2016) Examining tourist movements through network analysis: A cross-provincial boundary approach. 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 movement. 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 design. Her unwavering support during Ding’s challenging periods played a crucial role in the successful completion and publication of this work.

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1. Institute of Remote Sensing and Geographical Information Systems, Peking University’s School of Earth and Space Sciences, Beijing, People’s Republic of 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

Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Sciences, Peking University, Beijing, People’s Republic of China

Southwest United Graduate School, located in Kunming, China

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Contributions

Ding Ding: Study design, Methodology, Formal analysis, Investigation, Visualization, Draft preparation. Yunhao Zheng: Conceptualization, Methodology, Visualization, Reviewing and Editing. Yi Zhang: Conceptualization, Data Curation, Validation, Reviewing. Yu Liu: Resources, Reviewing, Project administration, 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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