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 dynamics

Abstract

Tourist movement behaviors across 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 with social media data to identify and study motifs within an urban network. This research examines the attractions represented as nodes in each motif, uncovering the relationship patterns among them. We also analyze motifs connecting attractions of varying types and classifications. Major 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 assessing tourist movement and provide deeper insights into recurring visitation patterns between attractions. Additionally, they aid destination managers in crafting policy strategies for smart tourism marketing and planning tailored to tourist preferences.

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Introduction

Tourism has become a dominant global economic force, outpacing some conventional industries and acting as a key driver for both international and local economic development. In particular, urban tourism forms the foundation of modern tourism and has achieved a mature phase of competitive expansion (Cárdenas-García et al., 2024; W. Su et al., 2003). It has opened 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 restricted 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 due to its critical influence on 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, such as inter-destination travel. A common practice involves compiling individual mobility data into networks, which form the foundation for evaluating the topological framework of attraction systems (Vu et al., 2015).

Tourist mobility data form a network structure where attractions act as nodes and the movement between them constitutes bonds (Kang et al., 2018). As a result, network analysis serves as a widely adopted data mining method for identifying 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 for studying tourist mobility patterns, most studies depend on descriptive measurements. This limitation restricts the evaluation of the reliability and validity of the observed patterns (Park & Zhong, 2022). The present research focuses on network motifs, defined as recurrent and statistically significant subgraphs within a broader network. As a key component of 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 patterns, and the impact of tourism policies on network structure and behavior. Additionally, unlike the travel motifs used in prior research (Park & Zhong, 2022; Yang et al., 2017), network motifs offer deeper insights into aggregated tourist mobility at the individual level. 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) What is the relationship between motifs and 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, 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 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 focusing 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). They hold significant relevance for 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 then determining its occurrence rates instead of exhaustive enumeration.

In the context of applying complex network science to tourism research, earlier investigations 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 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. Moving beyond simple subgraph analysis in mobility networks, scholars like Yang et al. (2017) have broadened the scope by introducing travel motifs, extending the concept from topological structures 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 identified various motifs such as transitive feedforward loops and one- and two mutual-dyad subgraphs. Additionally, Park & Zhong (2022) developed a network motif algorithm to analyze travel pattern connections between destinations in South Korea, though their work focused solely on local tourists’ spatial behavior. A significant challenge arises from mobile sensor data collected via cell towers, as pinpointing exact user locations and matching them to specific tourist attractions proves difficult. To address this, the current study utilizes social media data, mapping each tourist’s movements to corresponding attractions and uncovering patterns in attraction connectivity.

Analyzing tourist movement and network dynamics

Tourist mobility refers to the circulation, transit, distribution, and behavior of travelers as they navigate different locations over specific periods (Hardy et al., 2020; Shoval et al., 2020). A crucial component of this concept involves examining spatial movement (Oppermann, 1995). Insights into tourists’ temporal and spatial transitions play a vital role in shaping infrastructure, transportation systems, product innovation, destination strategies, and the creation of new attractions, alongside addressing the social, environmental, and cultural effects of tourism (Lew & McKercher, 2006). While quantitative methods enhance 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 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) applied an inductive method rooted in urban transportation to pinpoint factors that shape tourist mobility within destinations. Earlier studies leaned toward abstract techniques grounded in foundational tourist theories. A widely adopted method 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, computing the likelihoods of movement patterns on an island. For a more practical application, semi-Markov models prove valuable in estimating probabilities related to both tourist movement and the appeal of specific attractions (Xia et al., 2011). Additionally, time geography serves as a conceptual framework for interpreting tourist mobility. By combining time geography with geographic information systems, Grinberger et al. (2014) categorized tourists using time-space allocation metrics to uncover their decision-making processes and strategies under time and space limitations.

The collection and integration of individual mobility data into networks is becoming more prevalent as a foundation for studying the topological framework of attraction systems (Smallwood et al., 2011). These mobility patterns can be represented 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 approach, offering valuable insights for technology development and tourism marketing strategies. 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 connection between geographic proximity and the network shaped by tourists’ independent movement choices. 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 further aid in evaluating the spatiotemporal patterns 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). However, research utilizing 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 are influenced not just by differences in tourists’ lengths of stay and the topological structures of their movements but also by the relative distribution of each mobility type (Park & Zhong, 2022). Yet, travel motifs capture only individual tourist movement patterns, not aggregated-individual level trends, nor do they support the analysis of attraction network topologies (Jin et al., 2018). As far as we know, no prior research has applied network motifs to study tourist mobility at the individual-aggregation level. The sole exception is Lozano and Gutiérrez (2018), who used UCINET 6.0’s network motif analysis tool to examine the top three global tourism flows. This study contends that network motif analysis bridges a critical research gap in aggregation-level tourist mobility while also offering theoretical insights for the planning and design of tourist attraction networks.

Methodology

Research location

Suzhou, China (Fig. 1a) was chosen as the research site. Situated in eastern China, just west of Shanghai, Suzhou is home to approximately five million people. Renowned 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 earned a place on the World Heritage List in the last century. The ancient city’s attractions span an area of 14 km.²Beyond its historical sites, Suzhou boasts a scenic natural environment featuring verdant mountains and shimmering lakes.

aThe placement of attractions in SuzhoubThe microblogs tagged with geographic data in Suzhou.

Data gathering and preparation

Social media data were mainly gathered through 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 included diverse user data such as post ID, user ID, content text, images, geographic coordinates (longitude and latitude), and timestamps, illustrated in Fig. 1b. By referencing the user ID, we also obtained profile details while adhering to privacy regulations. This profile information encompasses registration location, gender, age, post count, follower numbers, and ‘follows.’

However, only a fraction of users participated in tourism-related activities. We inferred that these individuals were non-locals who needed to travel back to their home cities once their journey concluded. 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 (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/). By analyzing geo-tagged microblog coordinates, we verified whether users visited any attraction from the official list. Applying these criteria, we filtered the data and collected 234,049 Weibo posts from 54,712 tourists. Arranging these microblogs chronologically allowed us to trace tourists’ movement patterns across the city. Consequently, we mapped these trajectories to directional links between attractions, constructing 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) tailored to detect k-size network motifs while consuming less memory and computational time compared to alternative methods. The Kavosh algorithm operates by counting all k-size subgraphs within a specified graph, whether directed or undirected. Figure 2 illustrates the three-step process of the Kavosh algorithm: enumeration, random network generation, and motif detection. Initially, the algorithm lists every possible mobility pattern linked to the subgraphs in the original network. Isomorphic subgraphs are then categorized using the NAUTY algorithm, streamlining the process and reducing redundancy. Since not every pattern is 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 hypothesize 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 likelihood of the class appearing by chance within the given network. For the specified motifG_pthis metric is characterized as:

P-value

This metric reflects the count of random networks where a motif,G_p, appears more frequently compared to the input network, divided by the total count of randomized networks. Hence, theP-value spans between 0 and 1. A lower value indicates a more significant result.PThe higher the value, the more important the motif.

The input network contains identifiable motifs along with relevant statistical data. As outlined earlier, the algorithm employs three distinct measures. These measures lack fixed thresholds for motif classification; however, 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, thePThe value is less than 0.01.

2. 2.

   The frequency exceeds four.

3. 3.

   The Z-score exceeds 1 when calculated across 1000 randomly generated networks.

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

The Z-score exceeds 1 when calculated across 1000 randomly generated networks.

In line with the specified criteria, striving for maximum accuracy, the patterns displaying notable metrics 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 included 104 tourist attractions 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 did not satisfy the extraction criteria, leading to the selection of three- and four-node motifs. The presence of a motif in the network was assessed using the previously defined conditions. As a result, three three-node motifs and six four-node motifs were identified, illustrated in Figs. 3 and 4.

Beneath each 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. Every node in the graph also features a corresponding label, as seen in motifs 1 and 4. The labels for the remaining motifs follow the same pattern, though they were omitted to maintain the figure’s clarity. Following earlier 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. A chain-class motif represents tourists sequentially visiting three attractions without backtracking. Likewise, a double-linked mutual dyad motif indicates bidirectional tourist movement between two pairs of attractions. The fully connected triad motif describes a trio of attractions where any two pairs exhibit two-way 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. Additional specialized forms are the centrally linked mutual dyad and the fully connected triad featuring a mutual dyad. The centrally linked mutual dyad revolves around a central attraction encircled by three nodes with reciprocal flows, yet none of the three attractions interconnect. A fully connected triad with a mutual dyad consists of one fully connected triad where a single node also establishes another mutual dyad.

Among the nine motifs previously mentioned, those labeled with IDs 1, 2, and 3 are three-node motifs, representing 37.61% of all network subgraphs. This implies that tourist movement between any three attractions in the tourism network is primarily characterized by chaining, reflecting a sequential order in most connections among 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 earlier 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 primarily configured as a fully interconnected triple attraction, featuring three tightly linked attractions allowing unrestricted tourist movement. Alongside this triple attraction, we also observed a connection 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 frequently occurring local movement patterns of tourists within the original travel network. The subsequent analysis focused on the distribution of attractions across each node of these 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 illustrated 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 local network. Zhouzhuang frequently appears in motif nodes as a transitional stop en route to other destinations, serving as a gateway. 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 considerable 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 on to other destinations via attraction 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 the distinction of featuring diverse, visually appealing floral displays and extensive vegetation, making them ideal for springtime hiking.

Types and Titles of Attractions: Understanding Motif Interpretation

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 grouped into natural, cultural, and commercial types depending on their landscape characteristics. Additionally, they are categorized as 5A, 4A, or other ratings, where a ‘5A’ designation indicates superior scenery, exceptional service, and top-tier facilities. The breakdown of attraction categories across nodes is illustrated in Fig. 5, while Fig. 6 displays the distribution of attraction ratings. Node labels in the bottom-right corner of both figures denote their positions within each motif, 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 among motifs within each primary category are minimal. This suggests that every major 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, 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 clearer distinctions. 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 concentrating tourists within the local network. Conversely, motif 6’s node A acts mainly as a transitional point between nodes B, C, and D, with no significant emphasis on 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 equitable mix of attraction types.

Names of attractions

In Fig. 6, the proportions of 5A and 4A attractions on nodes lacking attraction titles are notably lower compared to nodes with well-known 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 less popular attractions. Conversely, node A serves as a transit point for famous attractions at a much higher rate than nodes B and C. The mutual Dyad type shows a comparable pattern for node A, with a greater share of famous attractions, while nodes B and C exhibit the opposite trend. If node B has a higher concentration of famous attractions, node C is mainly filled with non-famous ones, and the reverse is also true. For the double-linked mutual dyad type, node A contains a substantially larger proportion of famous attractions than the remaining nodes. Yet, the other nodes show no notable variation in attraction title percentages, whether linked bidirectionally to node A or not, and are predominantly non-famous. In fully connected triads, the three nodes are highly likely to feature titled attractions, suggesting significant tourist movement between 5A and 4A attractions. On the other hand, B nodes connected solely to A nodes are mostly non-famous, demonstrating that attractions without strong titles struggle to form meaningful connections with 5A and 4A attractions.

Discussion

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

Traveler movement behaviors

This research utilizes the theory of motifs, derived from complex network science, as a novel method for exploring tourist movement behaviors. Initially developed in biology, the network motif algorithm for complex systems was employed here to analyze connections between excessive tourism mobility trends and related attractions. Unlike earlier techniques for identifying travel motifs, the outcomes of motif analysis within the network do not directly reveal individual tourists’ city itineraries. The motif-based approach emphasizes movement trends among tourist groups moving between highly interconnected 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 overall network is composed of recurring simple topologies (Fig. 7). The research identified four classes and nine motifs that effectively capture varied mobility trends. This suggests that, regardless of individual travel differences, people adhere to consistent, repeatable patterns (González et al., 2008). Studying these mobility patterns improves understanding of urban destination systems and offers critical 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 arrangement 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 transit attractions require additional transportation routes. Gateway attractions, on the other hand, should see improvements in nearby hotel and guide services. However, the findings highlight that the key nodes in the nine motifs predominantly consist of a small number of highly popular attractions. From a risk management standpoint, an overly concentrated destination could lead to ‘overtourism’ (Peeters et al., 2018).

The impact on tourism management strategies

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 the flow of visitors between attractions, offering an innovative method to study movement trends within destinations and precisely represents tourists’ digital traces (Fan et al., 2024). Findings reveal that while individual travel routes between destinations 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 for unique, varied, and customized experiences (Füller & Michel, 2014). This research utilizes motif analysis within complex network science to uncover visitor movement trends and illustrate the relationships between attraction networks in Suzhou, China. By innovatively treating actual attractions as network nodes and concentrating on patterns of attraction linkages, 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 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 landmarks, often designated as 5A or 4A sites, which were primarily cultural or commercial in nature.

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, destinations like the Zhouzhuang and the Hanshan Temple fulfill distinct roles within the network.

The varieties and ratios of attractions were analyzed by mapping regional tourist movement trends within the network. Findings indicated that nodes exhibiting a greater motif degree were typically renowned sites labeled as 5A or 4A, primarily consisting of cultural and commercial destinations.

The findings establish a novel analytical framework for studying connectivity patterns in local attraction networks, while also offering a foundation for managing attractions in urban tourism areas.

Although this research offers valuable theoretical and practical contributions, certain limitations should be noted. For instance, social media data can be influenced by biases, including the varying popularity of platforms among users, as well as discrepancies in data volume across countries, years, and demographics. The tendency for highly active users to dominate datasets may lead to an overrepresentation of these groups (Encalada-Abarca et al., 2023). Furthermore, the data primarily capture tourists’ spatial movements within a single urban area. Since spatial behavior patterns differ 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 factors driving attraction selection through mobility motifs, such as tourists’ preference for optimizing satisfaction when designing their travel routes.

Accessibility of data

The data produced and/or examined in this research can be obtained from the corresponding author upon reasonable request.

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Hassan SS (2000) Factors influencing competitive advantage in a tourism sector focused on environmental sustainability. J Travel Res 38(3):239–245. https://doi.org/10.1177/004728750003800305

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Jin C, Cheng J, Xu J (2018) Investigating temporal variations in tourist movement patterns through user-generated content. J Travel Res 57(6):779–791. https://doi.org/10.1177/0047287517714906

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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 social media tourism information quality 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

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 international tourist mobility in Beijing using social network methods: Assessing 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 tourist travel patterns. 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 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) A framework for analyzing travel itineraries. 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 movement trends of travelers in urban locations: Insights for designing destinations. 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 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 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: 2010 ACM symposium on applied computing proceedings, 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 both 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 lead author, Ding, is deeply grateful to his fiancée, Lanqi Liu, for her assistance with coding and creating figures. 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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Authors and Institutional Affiliations

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

   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 houses the Institute of Remote Sensing and Geographical Information Systems, located in 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, 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 procurement.

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Ding, D., Zheng, Y., Zhang, Y.et al.Analyzing attraction linkage patterns through intra-destination visitor movement: A network motif perspective.Humanit Soc Sci Commun 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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