Communications in Humanities Research ISSN: 2753-7072
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Research Article

Comparing the Spatial Distribution of Popular Destinations in the Era of Mobile Internet: Case Studies of 9 Cities in the Pearl River Delta

Xinyue GuSchool of Architecture, South China University of Technology, Wushan Street, Tianhe district, Guangzhou, China* Siyan LinSchool of Public Management, South China Agricultural University, Wushan Street, Tianhe district, Guangzhou, China

* Corresponding author: [email protected]

Abstract

In the era of mobile Internet, the development of the influencer economy has given rise to emerging consumer spaces and popular destinations, which are new hot topics for urban public space research. Based on TikTok popular destinations check-in data and using statistical analysis, kernel density analysis, and optimized hotspot method, this study quantitatively measures different spatial distributions of popular destinations in the Pearl River Delta and nine cities within it and explores the relationship between spatial structure and built environment and the causes. The results show that the spatial distribution of popular destinations shows an obvious dual structure, and the spatial hotness spreads and decreases from the central cities (Guangzhou and Shenzhen) to the peripheral cities. Meanwhile, the spatial distribution characteristics of popular destinations within different cities have obvious heterogeneity, and the heterogeneity of developed cities is weaker than that of less developed cities.

Keywords: popular destinations; spatial distribution; TikTok; the Pearl River Delta; mobile internet
Published: May 17, 2023
DOI: 10.54254/2753-7064/4/20220881
Volume: CHR Vol.4
pp. 578-589
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References

  1. Yang, Q., Liu, Y., Yang, L.: Commercial gentrification in China and its distribution, development, and correlates: The case of Chengdu. Front. Environ. Sci. 10, 992092 (2022). https://doi.org/10.3389/fenvs.2022.992092.
  2. Song, H., Qiu, R.T.R., Park, J.: A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Ann. Tour. Res. 75, 338–362 (2019). https://doi.org/10.1016/j.annals.2018.12.001.
  3. Ma, X., Yang, Z., Zheng, J.: Analysis of spatial patterns and driving factors of provincial tourism demand in China. Sci. Rep. 12, 2260 (2022). https://doi.org/10.1038/s41598-022-04895-8.
  4. Mou, N., Zheng, Y., Makkonen, T., Yang, T., Tang, J., Song, Y.: Tourists’ digital footprint: The spatial patterns of tourist flows in Qingdao, China. Tour. Manag. 81, 104151 (2020). https://doi.org/10.1016/j.tourman.2020.104151.
  5. Silva, D., Bento, L.S.: The impact of TikTok on an online business strategy. The case of e-Bloom. (2022).
  6. Warnken, J., Russell, R., Faulkner, B.: Condominium developments in maturing destinations: potentials and problems of long-term sustainability. Tour. Manag. 24, 155–168 (2003). https://doi.org/10.1016/S0261-5177(02)00063-8.
  7. Rátz, T., Smith, M., Michalkó, G.: New Places in Old Spaces: Mapping Tourism and Regeneration in Budapest. Tour. Geogr. 10, 429–451 (2008). https://doi.org/10.1080/14616680802434064.
  8. Bull, P.J., Church, A.: The hotel and catering industry of Great Britain during the 1980s: sub-regional employment change, specialization and dominance. Hotel Cater. Ind. G. B. 1980s Sub-Reg. Employ. Change Spec. Domin. 248–269 (1994).
  9. Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., Prasad, S.: Extracting and understanding urban areas of interest using geotagged photos. Comput. Environ. Urban Syst. 54, 240–254 (2015). https://doi.org/10.1016/j.compenvurbsys.2015.09.001.
  10. Liu, F., Zhang, J., Zhang, J., Chen, D., Liu, Z., Lu, S.: Roles and functions of tourism destinations in tourism region of south anhui: A tourist flow network perspective. Chin. Geogr. Sci. 22, 755–764 (2012). https://doi.org/10.1007/s11769-012-0557-6.
  11. Baggio, R., Scott, N., Cooper, C.: Network science: A Review Focused on Tourism. Ann. Tour. Res. 37, 802–827 (2010). https://doi.org/10.1016/j.annals.2010.02.008.
  12. Yang, Y., Wong, K.K.F.: Spatial Distribution of Tourist Flows to China’s Cities. Tour. Geogr. 15, 338–363 (2013). https://doi.org/10.1080/14616688.2012.675511.
  13. Yubero, C., Condeço-Melhorado, A.M., García-Hernández, M., Fontes, A.C.: Comparing spatial and content analysis of residents and tourists using Geotagged Social Media Data. The Historic Neighbourhood of Alfama (Lisbon), a case study. Análisis espacial y de contenido comparado entre residentes y turistas con el uso de datos geolocalizados de redes sociales. El caso del barrio histórico de Alfama (Lisboa). (2021). https://doi.org/10.14198/INTURI2021.22.5.
  14. Li, J., Xu, L., Tang, L., Wang, S., Li, L.: Big data in tourism research: A literature review. Tour. Manag. 68, 301–323 (2018). https://doi.org/10.1016/j.tourman.2018.03.009.
  15. Önder, I., Koerbitz, W., Hubmann-Haidvogel, A.: Tracing Tourists by Their Digital Footprints: The Case of Austria. J. Travel Res. 55, 566–573 (2016). https://doi.org/10.1177/0047287514563985.
  16. Yeh, A.G.-O., Chen, Z.: From cities to super mega city regions in China in a new wave of urbanisation and economic transition: Issues and challenges. Urban Stud. 57, 636–654 (2020). https://doi.org/10.1177/0042098019879566.
  17. Hui, E.C.M., Li, X., Chen, T., Lang, W.: Deciphering the spatial structure of China’s megacity region: A new bay area—The Guangdong-Hong Kong-Macao Greater Bay Area in the making. Cities. 105, 102168 (2020). https://doi.org/10.1016/j.cities.2018.10.011.
  18. Statistical Bulletin of National Economic and Social Development. (2021).