IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i3p1165-d1329563.html
   My bibliography  Save this article

Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data

Author

Listed:
  • Yuanfang Fu

    (School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China)

  • Zhenrao Cai

    (School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China)

  • Chaoyang Fang

    (School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
    Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
    Nanchang Base, International Centre on Space Technologies for Natural and Cultural Heritage (HIST) under the Auspices of UNESCO, Nanchang 330022, China)

Abstract

Rural tourism serves as a crucial means for fostering rural economic prosperity and inheriting rural culture. The assessment of the quality of rural tourism development and the identification of disparities in rural tourism development among regions have become focal points in current research. This paper utilizes tourism big data to establish a system for evaluating rural tourism popularity and proposes a method for identifying rural tourism hotspots. The study explores the spatiotemporal evolution characteristics and formation mechanisms of the cold and hot patterns of rural tourism in China during two periods (pre-pandemic and post-pandemic) and on two spatial scales (provincial and municipal levels). The research findings indicate that (1) the annual variation in rural tourism popularity exhibits a fluctuating upward trend, with significant seasonal variations on a monthly basis. (2) The spatial pattern of rural tourism popularity changes with the scale effect. At the provincial level, hotspot areas form an east–west dual-core pattern, while at the municipal level, hotspot areas demonstrate an evolution from a three-core to a four-core pattern. In the post-pandemic era, rural tourism popularity in the northwest and southwest regions is experiencing a counter-trend growth. (3) At different spatiotemporal scales, influencing factors and their impact intensities vary. At the provincial level, road density and reception capacity consistently play dominant roles, and per capita disposable income significantly influences early-stage popularity enhancement. At the municipal level, resident population and tourism resources influence are the dominant factors, and the influence of air quality and regional media attention gradually strengthens. This article provides a new perspective on quantitative research in rural tourism, offering significant guidance for the rational allocation of resources in rural tourism, regional tourism collaboration, and the sustainable development of rural tourism in the post-pandemic era.

Suggested Citation

  • Yuanfang Fu & Zhenrao Cai & Chaoyang Fang, 2024. "Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data," Sustainability, MDPI, vol. 16(3), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1165-:d:1329563
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/3/1165/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/3/1165/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qinghua He & Xin Zheng & Xin Xiao & Lei Luo & Hui Lin & Shan He, 2023. "The Spatiotemporal Evolution and Influencing Factors of the Ceramics Industry in Jingdezhen in the Last 40 Years," Land, MDPI, vol. 12(8), pages 1-19, August.
    2. Walter Christaller, 1964. "Some Considerations Of Tourism Location In Europe: The Peripheral Regions‐Underdeveloped Countries‐Recreation Areas," Papers in Regional Science, Wiley Blackwell, vol. 12(1), pages 95-105, January.
    3. Xiang, Zheng & Pan, Bing, 2011. "Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations," Tourism Management, Elsevier, vol. 32(1), pages 88-97.
    4. Li, Hengyun & Gao, Huicai & Song, Haiyan, 2023. "Tourism forecasting with granular sentiment analysis," Annals of Tourism Research, Elsevier, vol. 103(C).
    5. Siliverstovs, Boriss & Wochner, Daniel S., 2018. "Google Trends and reality: Do the proportions match?," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 1-23.
    6. Lingfeng Li & Quan Gao, 2023. "Researching Tourism Space in China’s Great Bay Area: Spatial Pattern, Driving Forces and Its Coupling with Economy and Population," Land, MDPI, vol. 12(10), pages 1-24, October.
    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. Brodeur, Abel & Clark, Andrew E. & Fleche, Sarah & Powdthavee, Nattavudh, 2021. "COVID-19, lockdowns and well-being: Evidence from Google Trends," Journal of Public Economics, Elsevier, vol. 193(C).
    2. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    3. Sansone, Dario, 2019. "Pink work: Same-sex marriage, employment and discrimination," Journal of Public Economics, Elsevier, vol. 180(C).
    4. Karol Król & Dariusz Zdonek, 2023. "Cultural Heritage Topics in Online Queries: A Comparison between English- and Polish-Speaking Internet Users," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    5. Sandra Rousseau & Nick Deschacht, 2020. "Public Awareness of Nature and the Environment During the COVID-19 Crisis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 1149-1159, August.
    6. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    7. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    8. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    9. Michael Olumekor & Hossam Haddad & Nidal Mahmoud Al-Ramahi, 2023. "The Relationship between Search Engines and Entrepreneurship Development: A Granger-VECM Approach," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    10. Paul Peeters & Martin Landré, 2011. "The Emerging Global Tourism Geography—An Environmental Sustainability Perspective," Sustainability, MDPI, vol. 4(1), pages 1-30, December.
    11. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
    12. Vinaitheerthan Renganathan & Amitabh Upadhya, 2021. "Dubai Restaurants: A Sentiment Analysis of Tourist Reviews," Academica Turistica - Tourism and Innovation Journal, University of Primorska Press, vol. 14(2), pages 165-174.
    13. Yongquan Li & Rui Li & Wenqi Ruan & Chih-Hsing Liu, 2020. "Research of the Effect of Tourism Economic Contact on the Efficiency of the Tourism Industry," Sustainability, MDPI, vol. 12(14), pages 1-16, July.
    14. Hamid Ahaggach & Lylia Abrouk & Eric Lebon, 2024. "Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions," Forecasting, MDPI, vol. 6(3), pages 1-31, July.
    15. O. Cenk Demiroglu & Dieter K. Müller, 2021. "Managing Emerging Destinations: the Case of Azerbaijan," Journal of Tourismology, Istanbul University, Faculty of Economics, vol. 7(1), pages 1-27, June.
    16. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    17. Yunxing Zhang & Weizhen Li & Ziyang Li & Meiyu Yang & Feifei Zhai & Zhigang Li & Heng Yao & Haidong Li, 2022. "Spatial Distribution Characteristics and Influencing Factors of Key Rural Tourism Villages in China," Sustainability, MDPI, vol. 14(21), pages 1-26, October.
    18. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    19. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
    20. Dimitrios Anastasiou & Konstantinos Drakos, 2021. "Nowcasting the Greek (semi‐) deposit run: Hidden uncertainty about the future currency in a Google search," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1133-1150, January.

    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:16:y:2024:i:3:p:1165-:d:1329563. 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.