IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12184-d925527.html
   My bibliography  Save this article

The Impacts of COVID-19 on the Rank-Size Distribution of Regional Tourism Central Places: A Case of Guangdong-Hong Kong-Macao Greater Bay Area

Author

Listed:
  • Xiaohui Xu

    (Faculty of Tourism and Culture, Nanning Normal University, Nanning 530001, China)

Abstract

It is well known that Zipf’s rank-size law is powerful to investigate the rank-size distribution of tourist flow. Recently, widespread attention has been drawn to investigating the impacts of COVID-19 on tourism for its sustainability. However, little is known about the impacts of COVID-19 on the rank-size distribution of regional tourism central places. Taking Guangdong-Hong Kong-Macao Greater Bay Area as a research case, this article aims to examine the fractal characteristics of the rank-size distribution of regional tourism central places, revealing the impacts which COVID-19 has on the rank-size distribution of regional tourism central places. Based on the census data over the years from 2008 to 2021, this paper reveals that before COVID-19, the rank-size distribution of the tourism central places in Guangdong-Hong Kong-Macao Greater Bay Area appears monofractal, and the difference in the size of the tourism central places has a tendency to gradually decrease; in 2020, with the outbreak of COVID-19, the characteristic of the rank-size distribution shows that the original monofractal is broken into multifractal; in 2021, with COVID-19 becoming under control, the structure of tourism size distribution, changes into bifractal based on the original multifractal, showing that the rank-size distribution of tourism central places in Guangdong-Hong Kong-Macao Greater Bay Area becomes more ideal and the tourism order becomes better than the last year. The results obtained not only fill in the gap about the impacts of COVID-19 on tourism size distribution, but also contribute to the application of fractal theory to tourism size distribution. In addition, we propose some suggestions to the local governments and tourism authorities which have practical significance to tourism planning.

Suggested Citation

  • Xiaohui Xu, 2022. "The Impacts of COVID-19 on the Rank-Size Distribution of Regional Tourism Central Places: A Case of Guangdong-Hong Kong-Macao Greater Bay Area," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12184-:d:925527
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12184/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/19/12184/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cribari-Neto, Francisco, 2004. "Asymptotic inference under heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 215-233, March.
    2. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    3. Rosen, Kenneth T. & Resnick, Mitchel, 1980. "The size distribution of cities: An examination of the Pareto law and primacy," Journal of Urban Economics, Elsevier, vol. 8(2), pages 165-186, September.
    4. Li, Xun & Gong, Jian & Gao, Baojun & Yuan, Peiwen, 2021. "Impacts of COVID-19 on tourists' destination preferences: Evidence from China," Annals of Tourism Research, Elsevier, vol. 90(C).
    5. Blank, Aharon & Solomon, Sorin, 2000. "Power laws in cities population, financial markets and internet sites (scaling in systems with a variable number of components)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(1), pages 279-288.
    6. Chen, Yanguang, 2011. "Fractal systems of central places based on intermittency of space-filling," Chaos, Solitons & Fractals, Elsevier, vol. 44(8), pages 619-632.
    7. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    8. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    9. Davide Provenzano, 2014. "Power laws and the market structure of tourism industry," Empirical Economics, Springer, vol. 47(3), pages 1055-1066, November.
    10. Guo, Jinzhong & Xu, Qi & Chen, Qinghua & Wang, Yougui, 2013. "Firm size distribution and mobility of the top 500 firms in China, the United States and the world," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(13), pages 2903-2914.
    11. Miguéns, J.I.L. & Mendes, J.F.F., 2008. "Travel and tourism: Into a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2963-2971.
    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. Yongrui Guo & Jie Zhang & Honglei Zhang, 2016. "Rank–size distribution and spatio-temporal dynamics of tourist flows to China’s cities," Tourism Economics, , vol. 22(3), pages 451-465, June.
    2. Francisco Cribari-Neto & Maria da Gloria Lima, 2010. "Approximate inference in heteroskedastic regressions: A numerical evaluation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(4), pages 591-615.
    3. Hausman, Jerry & Palmer, Christopher, 2012. "Heteroskedasticity-robust inference in finite samples," Economics Letters, Elsevier, vol. 116(2), pages 232-235.
    4. Uchôa, Carlos F.A. & Cribari-Neto, Francisco & Menezes, Tatiane A., 2014. "Testing inference in heteroskedastic fixed effects models," European Journal of Operational Research, Elsevier, vol. 235(3), pages 660-670.
    5. Francisco Cribari-Neto & Wilton Silva, 2011. "A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 129-146, June.
    6. Hartigan, Luke, 2018. "Alternative HAC covariance matrix estimators with improved finite sample properties," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 55-73.
    7. Ke-Hai Yuan & Ying Cheng & Scott Maxwell, 2014. "Moderation Analysis Using a Two-Level Regression Model," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 701-732, October.
    8. Sin, C.Y. (Chor-yiu) & Lee, Cheng-Few, 2021. "Using heteroscedasticity-non-consistent or heteroscedasticity-consistent variances in linear regression," Econometrics and Statistics, Elsevier, vol. 18(C), pages 117-142.
    9. James G. MacKinnon, 2012. "Thirty Years Of Heteroskedasticity-robust Inference," Working Paper 1268, Economics Department, Queen's University.
    10. José Curto & José Pinto & Ana Morais & Isabel Lourenço, 2011. "The heteroskedasticity-consistent covariance estimator in accounting," Review of Quantitative Finance and Accounting, Springer, vol. 37(4), pages 427-449, November.
    11. Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš, 2022. "YOLO trading: Riding with the herd during the GameStop episode," Finance Research Letters, Elsevier, vol. 46(PA).
    12. Wen, Miin-Jye & Chen, Shun-Yi & Chen, Hubert J., 2007. "On testing a subset of regression parameters under heteroskedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5958-5976, August.
    13. Francisco Cribari-Neto & Maria Lima, 2010. "Sequences of bias-adjusted covariance matrix estimators under heteroskedasticity of unknown form," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 1053-1082, December.
    14. Maria Iacovou, 2002. "Class Size in the Early Years: Is Smaller Really Better?," Education Economics, Taylor & Francis Journals, vol. 10(3), pages 261-290.
    15. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages C32-C61, 03.

    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:14:y:2022:i:19:p:12184-:d:925527. 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.