IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1626-d1035506.html
   My bibliography  Save this article

The Clustering Characteristics and Driving Mechanisms of Tourist Preference for 5A Scenic Spots from the Dynamic Spatio-Temporal Perspective: A Case of Jiangsu in Eastern Coastal Area of China

Author

Listed:
  • Guiling Wang

    (School of Geographic Science, Nantong University, Nantong 226007, China
    Yangtze River Economic Zone Research Institution of Jiangsu, Nantong 226007, China)

  • Yuenan Meng

    (Yangtze River Economic Zone Research Institution of Jiangsu, Nantong 226007, China)

Abstract

In the era of tourism 4.0, the subjective preferences and experiences of tourists directly affects the future development of scenic spots. Among them, 5A scenic spots represent China’s world-class boutique tourist scenic ranking, which pays more attention to humanization and detailing, and better reflect tourists’ general psychological demand for tourist attractions. Therefore, it becomes an important scientific issue to explore the spatial and temporal characteristics of 5A tourists’ preferences and their deeper reasons. China’s eastern coastal provinces have played a very important role in promoting regional tourism culture, promoting regional economic development, and building a strong coastal province. Taking Jiangsu as an example, this study introduces the standard deviation ellipse and kernel density estimation to explore the characteristics and trends of tourist preference clustering in 5A scenic spots and their internal and external driving mechanisms from 2012–2021, which is based on a new perspective of geotemporal dynamic analysis. The empirical results show that: (1) the network attention of 5A scenic spots in Jiangsu generally showed a “barb-type” trend in 2012–2021, the highest network attention is concentrated in April and October, accounting for 82%, and tourists pay the most attention to cultural relics scenic spots, peaking on Sunday; (2) the tourist preference for 5A scenic spots in Jiangsu shows a relative clustering distribution pattern in general, with a slightly higher clustering trend in the northwest-southeast direction than in the northeast-southwest direction. Furthermore, the nucleus of the density values range from 0 to 115.43, showing the spatial pattern of “one belt and two cores”; (3) the internal driving factors include the types, the culture nature, the characteristics nature, the spatial proximity, and the infrastructure of scenic spots, and the external driving factors include geographical location, industrial development policy, climate comfort, economic development level, traffic accessibility, and the impact of COVID-19 epidemic. On this basis, this study puts forward feasible suggestions for improving scenic area management and increasing the reception capacity, so as to provide fine scientific guidelines for the high-quality development of 5A scenic spots in Jiangsu and provide reference for further enhancing the attractiveness of 5A scenic spots and their synergistic development in the eastern coastal provinces.

Suggested Citation

  • Guiling Wang & Yuenan Meng, 2023. "The Clustering Characteristics and Driving Mechanisms of Tourist Preference for 5A Scenic Spots from the Dynamic Spatio-Temporal Perspective: A Case of Jiangsu in Eastern Coastal Area of China," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1626-:d:1035506
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1626/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1626/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    2. Sofi, Arfat Ahmad & Bhat, Mohammad Younus & Ahmad, Laraib & Aara, Ruhi Refath & Aswani, RS, 2022. "Renewable energy and transitioning towards sustainable tourism: Inferences from kernel density and nonparametric approach," Renewable Energy, Elsevier, vol. 193(C), pages 963-975.
    3. Weiwei Zhang & Lingling Jiang, 2021. "Effects of High-Speed Rail on Sustainable Development of Urban Tourism: Evidence from Discrete Choice Model of Chinese Tourists’ Preference for City Destinations," Sustainability, MDPI, vol. 13(19), pages 1-19, September.
    4. Jing Qin & Ci Song & Mingdi Tang & Youyin Zhang & Jinwei Wang, 2019. "Exploring the Spatial Characteristics of Inbound Tourist Flows in China Using Geotagged Photos," Sustainability, MDPI, vol. 11(20), pages 1-17, October.
    5. Andersson, Jessica E.C., 2007. "The recreational cost of coral bleaching -- A stated and revealed preference study of international tourists," Ecological Economics, Elsevier, vol. 62(3-4), pages 704-715, May.
    6. Louviere, Jordan J & Hensher, David A, 1983. "Using Discrete Choice Models with Experimental Design Data to Forecast Consumer Demand for a Unique Cultural Event," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(3), pages 348-361, December.
    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. Tin Cheuk Leung, 2013. "What Is the True Loss Due to Piracy? Evidence from Microsoft Office in Hong Kong," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 1018-1029, July.
    2. Tianjiao Zhao & Xiang Xiao & Qinghui Dai, 2021. "Transportation Infrastructure Construction and High-Quality Development of Enterprises: Evidence from the Quasi-Natural Experiment of High-Speed Railway Opening in China," Sustainability, MDPI, vol. 13(23), pages 1-23, December.
    3. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    4. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    5. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    6. 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.
    7. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    8. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    9. Mohammed H. Alemu & Søren Bøye Olsen & Suzanne E. Vedel & John Kinyuru & Kennedy O. Pambo, 2016. "Integrating sensory evaluations in incentivized discrete choice experiments to assess consumer demand for cricket flour buns in Kenya," IFRO Working Paper 2016/02, University of Copenhagen, Department of Food and Resource Economics.
    10. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    11. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    12. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    13. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    14. Leung, Tin Cheuk, 2015. "Music piracy: Bad for record sales but good for the iPod?," Information Economics and Policy, Elsevier, vol. 31(C), pages 1-12.
    15. Yangkun Huang & Xiaoping Xu & Sini Su, 2021. "Diverging from News Media: An Exploratory Study on the Changing Dynamics between Media and Public Attention on Cancer in China from 2011–2020," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    16. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    17. Siikamki, Juha, 2001. "Valuing Benefits of Finnish Forest Biodiversity Conservation: Fixed and Random Parameter Logit Models for Pooled Contingent Valuation and Contingent Rating/Ranking Survey Data," Western Region Archives 321696, Western Region - Western Extension Directors Association (WEDA).
    18. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2017. "The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations," Papers 1701.05632, arXiv.org.
    19. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    20. Sean Coogan & Zhixian Sui & David Raubenheimer, 2018. "Gluttony and guilt: monthly trends in internet search query data are comparable with national-level energy intake and dieting behavior," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-9, December.

    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:15:y:2023:i:2:p:1626-:d:1035506. 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.