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Using mobile phone data to determine spatial correlations between tourism facilities

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  • Qian, Chen
  • Li, Weifeng
  • Duan, Zhengyu
  • Yang, Dongyuan
  • Ran, Bin

Abstract

Mobile phone data provide a more complete and accurate description of tourism transportation demand than traditional and Internet data sources. In this paper, a framework is proposed to determine spatial correlations between tourism destinations, rest places, and transportation hubs based on mobile phone data. Firstly, nine rules for identifying visitors based on four spatial and temporal features are established. Then, the spatial correlations are analyzed from three aspects. A case study of Shanghai is carried out to verify the proposed methodology, and the addition of a tour bus network based on the evaluation of transportation accessibility is discussed. It is concluded that tourists tend to rest near next-day destinations and choose transportation hubs in the city center. The rest places that are sightseeing destinations, amusement parks, and convention centers exhibit polycentric characteristics. The research framework and results of this study are useful for tourism transportation planning.

Suggested Citation

  • Qian, Chen & Li, Weifeng & Duan, Zhengyu & Yang, Dongyuan & Ran, Bin, 2021. "Using mobile phone data to determine spatial correlations between tourism facilities," Journal of Transport Geography, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:jotrge:v:92:y:2021:i:c:s0966692321000715
    DOI: 10.1016/j.jtrangeo.2021.103018
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    References listed on IDEAS

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    Cited by:

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    2. Mirela-Catrinel Voicu & Adina Bărbulescu & Denisa Abrudan, 2022. "On a City Guide App Model," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    3. Arpan Kumar Kar & Sunil Kumar & P. Vigneswara Ilavarasan, 2021. "Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(4), pages 267-288, December.
    4. Nalin, Alessandro & Vignali, Valeria & Lantieri, Claudio & Cappellari, Denis & Zamengo, Bruno & Simone, Andrea, 2024. "Assessing veracity of big data: An in-depth evaluation process from the comparison of Mobile phone traces and groundtruth data in traffic monitoring," Journal of Transport Geography, Elsevier, vol. 118(C).
    5. Yao Chang & Dongbing Li & Zibibula Simayi & Shengtian Yang & Maliyamuguli Abulimiti & Yiwei Ren, 2022. "Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest," IJERPH, MDPI, vol. 19(13), pages 1-18, June.
    6. Türk, Umut & Östh, John & Kourtit, Karima & Nijkamp, Peter, 2021. "The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data," Journal of Transport Geography, Elsevier, vol. 94(C).

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