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Spatiotemporal Evolution and Influencing Factors of Zhangjiajie National Forest Park Tourism Network Attention

Author

Listed:
  • Yurong Wu

    (Department of Recreation and Ecotourism, Faculty of Forestry and Environment, Universiti Putra Malaysia, Kuala Lumpur 43400, Selangor, Malaysia)

  • Sheena Bidin

    (Department of Recreation and Ecotourism, Faculty of Forestry and Environment, Universiti Putra Malaysia, Kuala Lumpur 43400, Selangor, Malaysia)

  • Shazali Johari

    (Department of Recreation and Ecotourism, Faculty of Forestry and Environment, Universiti Putra Malaysia, Kuala Lumpur 43400, Selangor, Malaysia
    Faculty of Tourism & Hospitality, i-CATS University College, Jalan Stampin Timur, Kuching 93350, Sarawak, Malaysia)

Abstract

Tourism network attention, defined as the quantifiable measure of public interest toward tourism destinations through online search activities, has become a crucial indicator for understanding tourist behavior in the digital era. This study analyzes the spatiotemporal evolution of tourism network attention for Zhangjiajie National Forest Park using Baidu index data from 2013 to 2023. Results show three temporal phases: rapid rise (2013–2017), fluctuation adjustment (2018–2020), and recovery growth (2021–2023), with a “double-peak” seasonal pattern in July–August and April–May. Spatial distribution exhibits a “high East, low West” pattern with gradually increasing balance (coefficient of variation: 0.6849→0.5382). GDP, internet users, and transportation accessibility are dominant factors influencing spatial patterns.

Suggested Citation

  • Yurong Wu & Sheena Bidin & Shazali Johari, 2025. "Spatiotemporal Evolution and Influencing Factors of Zhangjiajie National Forest Park Tourism Network Attention," Sustainability, MDPI, vol. 17(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7182-:d:1720355
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    References listed on IDEAS

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    1. Xia Xie & Lei Zhang & Hui Sun & Feifei Chen & Chunshan Zhou, 2021. "Spatiotemporal Difference Characteristics and Influencing Factors of Tourism Urbanization in China’s Major Tourist Cities," IJERPH, MDPI, vol. 18(19), pages 1-21, October.
    2. Ru-Xin Nie & Chuan Wu & He-Ming Liang, 2024. "Exploring Appropriate Search Engine Data for Interval Tourism Demand Forecasting Responding a Public Crisis in Macao: A Combined Bayesian Model," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    3. Xiao-Bing Feng, 2025. "Spatio-temporal Characterization of Potential Demand for Tourist Attractions Based on Internet Search," SAGE Open, , vol. 15(1), pages 21582440251, February.
    4. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    5. Mingxin Chen & Dong Dong & Fengquan Ji & Yu Tai & Nan Li & Runyu Huang & Tieqiao Xiao, 2024. "A Study on Spatiotemporal Evolution and Influencing Factors of Chinese National Park Network Attention," Land, MDPI, vol. 13(6), pages 1-25, June.
    6. Haiyan Wang & Tao Hu & Huihui Wu, 2023. "Tourism demand with subtle seasonality: Recognition and forecasting," Tourism Economics, , vol. 29(7), pages 1865-1889, November.
    7. Sakiru Adebola Solarin, 2025. "Convergence of tourism market diversification: evidence from a new indicator based on Herfindahl–Hirschman index," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 663-681, February.
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