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Exploring the Spatial Characteristics of Inbound Tourist Flows in China Using Geotagged Photos

Author

Listed:
  • Jing Qin

    (School of Tourism Sciences, Beijing International Studies University, Beijing 100024, China
    Research Center for Beijing Tourism Development, Beijing 100024, China)

  • Ci Song

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Mingdi Tang

    (School of Tourism Sciences, Beijing International Studies University, Beijing 100024, China
    Research Center for Beijing Tourism Development, Beijing 100024, China)

  • Youyin Zhang

    (Institute of Regional Tourism Planning and Development, China Tourism Academy, Beijing 100005, China)

  • Jinwei Wang

    (School of Tourism Sciences, Beijing International Studies University, Beijing 100024, China
    Research Center for Beijing Tourism Development, Beijing 100024, China)

Abstract

As important modern tourist destinations, cities play a critical role in developing agglomerated tourism elements and promoting urban life quality. An in-depth exploration of tourist flow patterns between destination cities can reflect the dynamic trends of the inbound tourist market. This is significant for the development of tourism markets and innovation in tourism products. To this end, photos with geographical and corresponding metadata covering the entire country from 2011 to 2017 are used to explore the spatial characteristics of China’s inbound tourist flow, the spatial patterns of tourist movement, and the tourist destination cities group based on data mining techniques, including the Markov chain, a frequent-pattern-mining algorithm, and a community detection algorithm. Our findings show that: (1) the strongest flow of inbound tourists is between Beijing and Shanghai. These two cities, along with Xi’an and Guiling, form a “double-triangle” framework, (2) the travel between emerging destination cities in Central and Western China have gradually become frequently selected itineraries, and, (3) based on the flow intensity, inbound tourist destination cities can be divided into nine groups. This study provides a valuable reference for the development of China’s inbound tourism market.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5822-:d:278494
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Xingshan Wang & Lu Tang & Wei Chen & Jianxin Zhang, 2022. "Impact and Recovery of Coastal Tourism Amid COVID-19: Tourism Flow Networks in Indonesia," Sustainability, MDPI, vol. 14(20), pages 1-17, October.

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