Evaluation of tourism competitiveness is crucial for the development of destinations. However, a research gap exists in comprehending the spatial variances in the factors that influence tourism competitiveness. This study aims to fill this gap by empirically investigating the spatially heterogeneous effects of determinants related to tourism competitiveness. To achieve this, we utilized multi-source data, geographically neural network weighted regression, and Shapley additive explanations. The results revealed that market demand, physiography, and attractions are the most significant factors contributing to tourism competitiveness. Furthermore, the relative importance of these influencing factors varies across locations, based on different destination types, market contexts, and resource characteristics. These findings provide valuable implications for policymakers to enhance destination competitiveness through adopting localized strategies.

中文翻译：

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区位是否影响旅游竞争力机制？机器学习分析的证据


旅游竞争力评价对于旅游目的地的发展至关重要。然而，在理解影响旅游竞争力的因素的空间差异方面存在研究空白。本研究旨在通过实证研究与旅游竞争力相关的决定因素的空间异质效应来填补这一空白。为了实现这一目标，我们利用了多源数据、地理神经网络加权回归和 Shapley 附加解释。结果显示，市场需求、地形和景点是影响旅游竞争力的最重要因素。此外，根据不同的目的地类型、市场环境和资源特征，这些影响因素的相对重要性因地点而异。这些研究结果为政策制定者通过采取本地化战略来增强目的地竞争力提供了宝贵的启示。