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<p>With the rapid growth of cultural heritage tourism, understanding visitor density is crucial for sustainable site management. This study examines how natural landscapes, artificial landscapes, and visitor perceptions influence visitor density at nature-based cultural heritage sites (NCHS) in China. Using kernel density analysis and the optimal parameter geographical detector (OPGD), we explore spatial heterogeneity and influencing factors. For visitor density, regression and spatial error models indicate that artificial landscapes exert the strongest influence, followed by natural landscapes, while perception variables have a lesser impact. XGBoost-SHAP analysis further confirms the importance of cultural heritage density in predicting visitor density. SHAP and OPGD analyses reveal interaction effects and threshold dynamics among key variables. Findings suggest that cultural heritage destinations integrating ancient trees, cooler climates, and dispersed natural perceptions enhance visitor appeal. These insights inform landscape planning and spatial strategies for optimizing visitor flow and improving heritage site management.</p>

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Sustainable tourism at nature-based cultural heritage sites: visitor density and its influencing factors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月16日

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  • Xingxing Jia,
  • Yucheng Xu,
  • Bing Zhao,
  • Hongyi Li,
  • Lingyu Li,
  • Jinguang Zhang

摘要

With the rapid growth of cultural heritage tourism, understanding visitor density is crucial for sustainable site management. This study examines how natural landscapes, artificial landscapes, and visitor perceptions influence visitor density at nature-based cultural heritage sites (NCHS) in China. Using kernel density analysis and the optimal parameter geographical detector (OPGD), we explore spatial heterogeneity and influencing factors. For visitor density, regression and spatial error models indicate that artificial landscapes exert the strongest influence, followed by natural landscapes, while perception variables have a lesser impact. XGBoost-SHAP analysis further confirms the importance of cultural heritage density in predicting visitor density. SHAP and OPGD analyses reveal interaction effects and threshold dynamics among key variables. Findings suggest that cultural heritage destinations integrating ancient trees, cooler climates, and dispersed natural perceptions enhance visitor appeal. These insights inform landscape planning and spatial strategies for optimizing visitor flow and improving heritage site management.