IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i16p7182-d1720355.html

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/16/7182/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/16/7182/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yong Liu & Xiang-jie Fu & Jeffrey Lin Yi Forrest, 2025. "Forecasting tourism demand with pre-holiday attribute," Information Technology & Tourism, Springer, vol. 27(3), pages 613-648, September.
    2. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    3. Jorge V Pérez-Rodríguez & Juan M Hernández & Julián Andrada-Félix, 2024. "Modelling prices and volatilities in the sharing economy," Tourism Economics, , vol. 30(5), pages 1189-1215, August.
    4. Dong Zhang & Pengkun Wu & Chong Wu & Eric W. T. Ngai, 2024. "Forecasting duty-free shopping demand with multisource data: a deep learning approach," Annals of Operations Research, Springer, vol. 339(1), pages 861-887, August.
    5. Cheng, Chiang-Yu, 2025. "Determinants of firms' decisions to block AI crawlers: Strategic insights from the BEDC framework," Technological Forecasting and Social Change, Elsevier, vol. 219(C).
    6. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    7. Ming Yin & Feiya Lu & Xingxuan Zhuo & Wangzi Yao & Jialong Liu & Jijiao Jiang, 2024. "Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short‐term memory network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 344-365, March.
    8. Jordi Grau-Escolano & Salvador Anton Clavé & Joan Borràs, 2026. "Daily tourism demand forecasting via card transactions: a multi-source, interpretable, framework for diverse destinations and markets," Information Technology & Tourism, Springer, vol. 28(1), pages 1-29, June.
    9. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.
    10. Bi, Jian-Wu & Li, Hui & Fan, Zhi-Ping, 2021. "Tourism demand forecasting with time series imaging: A deep learning model," Annals of Tourism Research, Elsevier, vol. 90(C).
    11. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2023. "Fine-grained tourism demand forecasting: A decomposition ensemble deep learning model," Tourism Economics, , vol. 29(7), pages 1736-1763, November.
    12. Chien-Jung Ting & Hsing-Mei Juan, 2025. "Assessing the Association of Popular Attractions with Taiwan’s Inbound Tourist Numbers: The Case of Night-Market Keywords," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 15(6), pages 1-4.
    13. Xu, Shilin & Liu, Yang & Jin, Chun, 2023. "Forecasting daily tourism demand with multiple factors," Annals of Tourism Research, Elsevier, vol. 103(C).
    14. He, Ling-Yang & Li, Hui & Bi, Jian-Wu & Yang, Jing-Jing & Zhou, Qing, 2022. "The impact of public health emergencies on hotel demand - Estimation from a new foresight perspective on the COVID-19," Annals of Tourism Research, Elsevier, vol. 94(C).
    15. Guangming Yang & Guofang Gong & Yao Luo & Yunrui Yang & Qingqing Gui, 2022. "Spatiotemporal Characteristics and Influencing Factors of Tourism–Urbanization–Technology–Ecological Environment on the Yunnan–Guizhou–Sichuan Region: An Uncoordinated Coupling Perspective," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    16. Ulrich Gunter & Egon Smeral, 2025. "A novel suggestion on how to adequately treat stochastic non-stationary seasonality in tourism export forecasting," Tourism Economics, , vol. 31(4), pages 579-592, June.
    17. Jian-Wu Bi & Tian-Yu Han & Hui Li, 2022. "International tourism demand forecasting with machine learning models: The power of the number of lagged inputs," Tourism Economics, , vol. 28(3), pages 621-645, May.
    18. Arpan Kumar Kar & Sunil Kumar & P. Vigneswara Ilavarasan, 2021. "Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(4), pages 267-288, December.
    19. Zheng, Weimin & Huang, Liyao & Lin, Zhibin, 2021. "Multi-attraction, hourly tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 90(C).
    20. Jianxin Zhang & Yuting Yan & Jinyue Zhang & Peixue Liu & Li Ma, 2023. "Investigating the Spatial-Temporal Variation of Pre-Trip Searching in an Urban Agglomeration," Sustainability, MDPI, vol. 15(14), pages 1-17, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7182-:d:1720355. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.