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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• Enterprise and administration
• The interplay between science, technology, and societal dynamics

Abstract

Tourist movement behavior among attractions is intricate and diverse, and comprehending these patterns can improve the management of tourist destinations. Yet, prior research on tourist movement using complex networks has not fully investigated the network motif method. Consequently, we employed a network motif approach with social media data to identify and study motifs within an urban attraction network. This research examines the attractions represented as nodes in each motif, uncovering the interaction patterns among them. Additionally, we analyze motifs connecting attractions of varying types and classifications. While popular attractions hold substantial influence in the local network, 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 trends between attractions. Furthermore, they support destination managers in crafting strategic tools 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 sectors and acting as a key driver for both international and local economic expansion. 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 unlocked 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 long-term economic vitality in these locations (Hassan, 2000). Today, tourists in urban settings are no longer confined by strict schedules or predetermined routes, with their mobility patterns emphasizing greater flexibility in time and space. John Urry’s ‘new mobility paradigm’ examines this evolving dynamic 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 collection of user location data from 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 theoretical 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 unravel the core of tourist mobility, as it is crucial for attraction promotion, event organization, and the planning and design of urban attractions. Examining tourist mobility within a single city allows for more precise decision-making compared to analyzing movement across larger regions, such as inter-destination travel. A common practice involves compiling individual mobility data into networks, which are then used to examine the topological characteristics of attraction systems (Vu et al., 2015).

Tourist mobility data form a network structure where attractions serve as nodes, and the movement between them creates bonds (Kang et al., 2018). As a result, network analysis has become 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 offers valuable insights for enhancing their competitiveness, management, and strategic planning (Stienmetz & Fesenmaier, 2015).

Despite this, much of the existing literature using social network analysis depends on descriptive metrics to examine tourist mobility patterns. Yet, this method limits the ability to evaluate the reliability and validity of the observed patterns (Park & Zhong, 2022). This research focuses on the idea of network motifs, defined as repeated and statistically meaningful subgraphs within a broader graph. As a key investigative objective 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 between locations, and the impact of tourism policies on network structure and behavior. Additionally, when contrasted with 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 can help pinpoint 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 pioneering study to analyze group tourist movement using network motifs, Suzhou City serves as the case study location, with social media data employed 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 outcomes and their significance for tourism. The Conclusion section outlines the study’s key takeaways.

A review of existing literature

Studies on network motifs

Network motifs refer to recurring patterns of connections within complex networks that appear far more frequently than in randomized networks (Milo et al., 2002). These motifs help describe a network’s functional and dynamic properties, 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 examine all subgraphs in 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 investigations have explored tourist flow networks by focusing on both inter-destination (Liu et al., 2017; Peng et al., 2016; Shih, 2006; Wang et al., 2020) and intra-destination scales (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 related to the network as a whole and those specific to individual nodes. Network-level indicators 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 fundamental aspect of 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) have broadened the scope of motifs by introducing travel motifs, incorporating temporal and semantic aspects alongside topological features. 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, research in South Korea by Park & Zhong (2022) developed a network motif algorithm to analyze place-based travel pattern relationships in tourism, though it focused solely on local tourists’ spatial behavior. A key limitation arises from mobile sensor data collected via cell towers, which makes it difficult to pinpoint exact user locations and their associated attractions. To address this, the current study utilizes social media data to map tourists’ spatial behaviors to specific attractions, uncovering patterns in attraction connectivity.

Analyzing tourist movement patterns and network structures

Tourist mobility refers to the circulation, transit, distribution, and journey behaviors of tourists within 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 transitions hold substantial relevance for infrastructure and transport enhancements, product innovation, destination strategy, attraction development, and addressing the social, environmental, and cultural effects of tourism (Lew & McKercher, 2006). While quantitative methods can 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) applied an inductive method rooted in urban transportation principles to pinpoint factors shaping tourist mobility within destinations. Initial research 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 probabilities for movement patterns on an island. For a more practical application, semi-Markov models prove valuable in determining both movement probabilities and the appeal of specific attractions (Xia et al., 2011). Additionally, time geography serves as a conceptual framework to interpret tourist mobility. By combining time geography with geographic information systems, Grinberger et al. (2014) categorized tourists according to time-space behavior metrics, uncovering their decision-making processes and strategies under time and space limitations.

The aggregation of individual-level mobility data into networks is becoming more prevalent as a foundation for studying the topological structure of attraction systems (Smallwood et al., 2011). These mobility patterns can be represented as networks, making them suitable for network analysis techniques (Shih, 2006). Zach and Gretzel (2011) conducted a core-periphery analysis of attraction networks, offering valuable insights into their structure and establishing a robust framework for technology development and tourism marketing strategies. In a separate study, Leung et al. (2012) utilized social network and content analyses to identify Beijing’s most frequented tourist attractions and primary movement trends across three different time frames. Network approaches are frequently combined with other analytical tools; for instance, Liu et al. (2017) employed a quadratic assignment procedure on an attraction network to assess how proximity influences tourist-driven movement patterns. Similarly, Mou et al. (2020a) merged social network analysis with conventional quantitative methods to create an innovative research model. Additional metrics like the Annual Gini Index and Pearson correlation coefficient prove useful in examining the spatiotemporal behavior of tourists (Zheng et al., 2021).

Motif discovery algorithms are frequently employed in analyzing gene regulation networks, electronic circuits, and neurons (Yu et al., 2020). Yet, research utilizing motif discovery techniques to investigate tourist mobility remains scarce. It should be noted that some studies explore travel motifs (extended 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, not aggregated-level patterns, nor do they facilitate 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. Only Lozano and Gutiérrez (2018) used UCINET 6.0’s network motif analysis tool to examine the top three global tourism flows. Thus, this study contends that network motif analysis not only addresses a gap in aggregation-level tourist mobility research but also offers theoretical insights for planning and designing tourist attraction networks.

Methodology

Research region

We chose Suzhou, China (Fig. 1a) as the research location. Situated in eastern China, just west of Shanghai, Suzhou is home to around 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 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 sparkling waterways.

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

Data gathering and preparation

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). To collect posts from Suzhou, we utilized Sina Weibo’s application programming interface, covering the period from April 12, 2012, to October 31, 2016. The crawled posts included multiple data points such as post ID, user ID, text content, images, geolocation details (longitude and latitude), and timestamps, as illustrated in Fig. 1b. By referencing the user ID, we accessed profile information while adhering to privacy regulations. This profile data encompassed registration location, gender, age, post count, fan count, and ‘follows.’

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

Tourism activities often serve purposes like entertainment or leisure, but they can also occur during official or business trips. While such trips may 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/). Geo-tagged microblog coordinates helped verify visits to these designated sites. Applying these criteria, we filtered the data and collected 234,049 Weibo posts from 54,712 tourists. By arranging these microblogs chronologically, we reconstructed tourists’ 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 with reduced memory and computational demands compared to existing 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 phases: enumeration, random network generation, and motif recognition. Initially, it enumerates every possible mobility pattern linked to subgraphs in the original network. The Kavosh algorithm then categorizes isomorphic subgraphs using the NAUTY algorithm, improving efficiency and reducing redundancy. Since not every pattern is meaningful, the algorithm produces numerous random networks and assesses the frequency of these patterns across them. Finally, the statistical significance of each pattern in the input network is computed to determine motifs. This involves applying statistical measures to identify probable motifs within the original network.

Frequency

This approach provides the most straightforward way to assess the importance of a motif. In a specific network, we suppose thatG_prepresents an element within an isomorphism class that participates in that category. Frequency is characterized as the count of instances forG_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_p, this metric is calculated as follows:

P-value

This metric reflects the count of randomly generated networks where a motif,G_pappears more frequently in the input network compared to the randomized networks, divided by the total count of random networks. Thus, theP-value ranges between 0 and 1. A lower value indicates a higher level of significance.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 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.

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

2. 2.

   The value exceeds four in frequency.

3. 3.

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

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

Based on 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 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. To identify k-motifs, 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, so only three- and four-node motifs were selected. Using the previously defined conditions, we confirmed the presence of motifs in the network. As a result, three three-node motifs and six four-node motifs were identified, 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. Each 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 the motif classifications established in prior research (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 visiting three attractions in sequence without revisiting any. Likewise, the double-linked mutual dyad motif indicates bidirectional tourist 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 classifications, the mutual dyad, double-linked mutual dyad, and fully connected triad each include uplinked and downlinked versions. 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, two more specialized forms exist: 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 engaged in mutual exchange, yet these nodes lack direct connections among themselves. Meanwhile, a fully connected triad with a mutual dyad consists of one fully interconnected triad where a single node also participates in a separate 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 among any three attractions in the tourism network is primarily characterized by chaining, reflecting a sequential order in most connections within these three-attraction patterns. The other six motifs, consisting of four nodes, made up 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 earlier research (Lau & McKercher, 2006; Lue et al., 1993; Oppermann, 1995), where tourists select one attraction as a hub, venture out to explore others, and eventually return. 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. Beyond this triple attraction, we also observed a connection between one attraction and one of the three attractions, 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 of the extracted motifs. For every motif, the node with the greatest degree was chosen, and the attractions present at 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, which shape the predominant movement patterns of local tourists. Other key attractions, including Zhouzhuang and Hanshan Temple, also hold significant positions in the network. Zhouzhuang functions as a transitional node in motif patterns, 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 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 acts as a convergence point before tourists move on to other destinations. Our analysis also identifies the top three attractions for node B: Tongli National Wetland Park, China Flower Botanical Garden, and Dabaidang Ecological Park. These locations share a key feature—abundant floral diversity and extensive vegetation, making them ideal for springtime hiking.

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, where a ‘5A’ designation 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 correspond to 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 within each motif of the major categories are minimal. This suggests that every primary category of attraction connection pattern represents a shared group of tourists’ localized movement behaviors, with the characteristics of each attraction in these patterns remaining consistent.

Varieties of attractions

In Fig. 5, the node attraction types for the chain-type motif show minimal variation. 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 reveals more distinct features. First, nodes C and D in all three motifs share identical attraction type proportions, 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 prominence in commercial attractions. Finally, the fully connected triad shows uniform interconnectivity across motifs. Nodes C and D are predominantly cultural attractions, while node A, serving as a central link, demonstrates 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 behavior of node A in the mutual Dyad type mirrors that of the chain type, displaying a greater share of famous attractions, while nodes B and C exhibit contrasting trends. Here, if node B has a higher concentration of famous attractions, node C is predominantly filled with non-famous ones, and the reverse is also true. For the double-linked mutual dyad type, node A contains a substantially larger fraction of famous attractions than the remaining nodes. Yet, the other nodes show no meaningful variation in attraction title percentages, irrespective of their bidirectional flow with node A, and all are largely occupied by non-famous attractions. In motifs with a fully connected triad, the three interconnected nodes predominantly feature titled attractions, suggesting a significant tourist flow between 5A and 4A sites. On the other hand, most B nodes linked solely to A nodes consist of non-famous attractions, implying 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 mechanisms behind 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 according to their category and tier. Consequently, advancing Suzhou’s tourism industry in the future depends on strategically directing attractions to serve their designated functions effectively within the city.

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 their related attractions. Unlike traditional techniques for uncovering travel motifs, the findings from 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. Due to 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 simple topologies (Fig. 7). The research identified four classes and nine motifs that effectively capture the range of mobility patterns. This suggests that, regardless of varied travel histories, human movement adheres to consistent, repeatable structures (González et al., 2008). Studying these mobility patterns improves understanding of city destination systems and offers valuable guidance for urban tourism 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 allow 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, 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 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 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 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 preferences, tourists share similarities in their broader spatial behaviors, 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 growth, 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 patterns in tourist movement and illustrate the relationships between attraction systems in Suzhou, China. By creatively treating actual attractions as network nodes and concentrating on linkage patterns between them, the study offers actionable insights for managing destinations. The key findings are outlined below:

• 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 central hubs in Suzhou’s network, shaping the majority of connection patterns among local attractions. Meanwhile, sites 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 classified as 5A or 4A, with a prevalence of cultural and retail-based destinations.

Using the Kavosh motif discovery technique, we identified nine motifs within a tourist network in Suzhou. These 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 typically corresponded to prominent attractions labeled as 5A or 4A, primarily consisting of cultural and commercial sites.

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 study offers valuable theoretical insights and practical applications, certain limitations should be noted. For instance, social media data are susceptible to multiple biases, such as the varying popularity of platforms among users, and the volume of data can differ across countries, years, and demographic groups. The influence of highly engaged users may lead to an overrepresentation of these populations (Encalada-Abarca et al., 2023). Furthermore, the data primarily capture tourists’ spatial behavior within a single city. Given that spatial behavior patterns vary across destinations, future research should incorporate tourism networks from multiple locations to enable comparative analysis and enhance the generalizability of the findings. Subsequent studies 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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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 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) identified fundamental network motifs as the essential components underlying intricate networks. Their study was published in Science, volume 298, issue 5594, pages 824–827. The article is accessible via 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) An analysis of online reviews to explore customer experiences in Vietnamese hotels. 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 travel itinerary planning. J Travel Res 33(4):57–61. https://doi.org/10.1177/004728759503300409

Park S, Zhong RR (2022) Analyzing travel movement patterns in urban destinations using 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 patterns of travelers in urban destinations: Insights for designing tourist areas. 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 Business, 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: 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 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 figure design. Her unwavering support during 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, 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, Drafting the initial manuscript. Yunhao Zheng: Conceptualization, Methodology, Visualization, Revising and editing the manuscript. Yi Zhang: Conceptualization, Data curation, Validation, Reviewing the manuscript. Yu Liu: Resources, Manuscript review, Project oversight, Supervision, Securing funding.

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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.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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