College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China.
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China.
PLoS One. 2024 Feb 7;19(2):e0298035. doi: 10.1371/journal.pone.0298035. eCollection 2024.
This study addresses the critical need for regional tourism integration and sustainable development by identifying cooperation opportunities among tourist attractions within a region. We introduce a novel methodology that combines association rule mining with complex network analysis and utilizes search index data as a dynamic and contemporary data source to reveal cooperative patterns among tourist attractions. Our approach delineates a potential cooperative network within the destination ecosystem, categorizing tourist attractions into three distinct communities: core, intermediary, and periphery. These communities correspond to high, medium, and low tourist demand scales, respectively. The study uncovers a self-organizing network structure, driven by congruences in internal tourist demand and variances in external tourist experiences. Functionally, there is a directed continuum of cooperation prospects among these communities. The core community, characterized by significant tourist demand, acts as a catalyst, boosting demand for other attractions. The intermediary community, central in the network, links the core and periphery, enhancing cooperative ties and influence. Peripheral attractions, representing latent growth areas within the destination matrix, benefit from associations with the core and intermediary communities. Our findings provide vital insights into the dynamics, systemic characteristics, and fundamental mechanisms of potential cooperation networks among tourist attractions. They enable tourism management organizations to employ our analytical framework for real-time monitoring of tourism demand and flow trends. Additionally, the study guides the macro-control of tourism flows based on the tourism network, thereby improving the tourist experience and promoting coordinated development among inter-regional tourist attractions.
本研究通过确定区域内旅游景点之间的合作机会，解决了区域旅游一体化和可持续发展的关键需求。我们引入了一种新颖的方法，将关联规则挖掘与复杂网络分析相结合，并利用搜索索引数据作为动态和现代的数据来源，揭示旅游景点之间的合作模式。我们的方法描绘了目的地生态系统内的潜在合作网络，将旅游景点分为三个不同的社区：核心、中介和外围。这些社区分别对应于高、中、低旅游需求规模。该研究揭示了一个自组织的网络结构，由内部旅游需求的一致性和外部旅游体验的差异驱动。从功能上讲，这些社区之间存在着合作前景的有向连续体。以显著的旅游需求为特征的核心社区充当催化剂，提升了对其他景点的需求。处于网络中心的中介社区连接着核心和外围社区，增强了合作联系和影响力。代表目的地矩阵中潜在增长区域的外围景点则受益于与核心和中介社区的关联。我们的研究结果深入了解了旅游景点之间潜在合作网络的动态、系统特征和基本机制。它们使旅游管理组织能够运用我们的分析框架实时监测旅游需求和流量趋势。此外，该研究还指导了基于旅游网络的旅游流量宏观调控，从而改善游客体验，促进区域间旅游景点的协调发展。
