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Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data

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  • Beiqi Shi
  • Jinlin Zhao
  • Po-Ju Chen

Abstract

Urban tourism is booming and, as a result, crowding is now recognized as a social constraint in many tourist cities. When related to sustainability, tourism crowding must be considered. However, the way tourists experience crowding is still a neglected topic in urban tourism research. In this study, we proposed a new approach to exploit tourism crowding from crowdsourcing geospatial data which goes beyond the scale, timeliness, and cost of traditional on-site questionnaire surveys. The new approach is based on analysis of 446,273 ‘check-in’ geotagged data from Weibo in Shanghai. The data provided a hotspot distribution of popular urban tourist attractions and a range of factors related to tourism crowding. These data provided deep insights into the relationship between crowdedness and popularity of tourist attractions. This empirical work can be extended to urban tourism crowding management environments for sustainable development of tourist attractions.

Suggested Citation

  • Beiqi Shi & Jinlin Zhao & Po-Ju Chen, 2017. "Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data," Current Issues in Tourism, Taylor & Francis Journals, vol. 20(11), pages 1186-1209, August.
  • Handle: RePEc:taf:rcitxx:v:20:y:2017:i:11:p:1186-1209
    DOI: 10.1080/13683500.2016.1224820
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    Citations

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    Cited by:

    1. Jie Yin & Xiang-min Zheng & Ruey-Chyn Tsaur, 2019. "Occurrence mechanism and coping paths of accidents of highly aggregated tourist crowds based on system dynamics," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-16, September.
    2. Borowiecki, Karol Jan & Pedersen, Maja U. & Mitchell, Sara Beth, 2023. "Using big data to measure cultural tourism in Europe with unprecedented precision," Discussion Papers on Economics 5/2023, University of Southern Denmark, Department of Economics.
    3. Jie Yin & Yahua Bi, 2020. "Benign or disordered development? Assessment and simulation of security of highly aggregated tourist crowds in China," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-21, October.
    4. Cheng Shi & Yujia Zhai & Dongying Li, 2023. "Urban tourists’ spatial distribution and subgroup identification in a metropolis --the examination applying mobile signaling data and latent profile analysis," Information Technology & Tourism, Springer, vol. 25(3), pages 453-476, September.
    5. Ding Ding & Yunhao Zheng & Yi Zhang & Yu Liu, 2024. "Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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