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<p>Recreation service (RS) in heritage-based tourism is crucial for redefining the value of heritage resources. Yet, existing studies often neglect public perception-driven RS variation, limiting the future functionality of the scenic areas and the heritage resource valorization. Taking scenic areas in Hunan province, China, as a case study, we developed an RS assessment framework based on big data to evaluate RS and the comprehensive recreation service index, followed by K-means clustering for scenic area zoning. Key RS determinants were identified using XGBoost. Key findings include: (1) 56 recreation activities, 8 RSs, and 8 recreation scenic areas were identified; (2) Key determinants include popularity (importance = 0.114), proximity to cities (0.246), and adjacency to renowned scenic areas (0.355). This framework addresses the gap that focuses solely on spatiotemporal patterns while neglecting RS typology differences in scenic areas, and provides evidence-based insights to enhance heritage utilization and sustainable tourism in scenic areas.</p>

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Assessing recreation services for heritage resources in scenic areas based on behavioral semantic perspective

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月8日

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  • Yurou Li,
  • Qiulin Liao,
  • Weiwei Wang,
  • Shouyun Shen,
  • Yuchi Cao,
  • Jiaao Chen

摘要

Recreation service (RS) in heritage-based tourism is crucial for redefining the value of heritage resources. Yet, existing studies often neglect public perception-driven RS variation, limiting the future functionality of the scenic areas and the heritage resource valorization. Taking scenic areas in Hunan province, China, as a case study, we developed an RS assessment framework based on big data to evaluate RS and the comprehensive recreation service index, followed by K-means clustering for scenic area zoning. Key RS determinants were identified using XGBoost. Key findings include: (1) 56 recreation activities, 8 RSs, and 8 recreation scenic areas were identified; (2) Key determinants include popularity (importance = 0.114), proximity to cities (0.246), and adjacency to renowned scenic areas (0.355). This framework addresses the gap that focuses solely on spatiotemporal patterns while neglecting RS typology differences in scenic areas, and provides evidence-based insights to enhance heritage utilization and sustainable tourism in scenic areas.