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Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data

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  • Park, Sangwon
  • Xu, Yang
  • Jiang, Liu
  • Chen, Zhelin
  • Huang, Shuyi

Abstract

The advancement of mobile technology provides an opportunity to obtain the real-time information of travelers, such as their spatial and temporal behaviors, during their visits to a destination. This study analyzes a large scale mobile phone dataset that captures the cellphone trace of international travelers who visited South Korea. We apply a trajectory data mining approach to understand the spatial structures of tourist activities within three different destinations. Through spatial clustering analysis and sequential pattern mining, the study reveals multiple “hot spots” (or popular areas) in travel destinations and spatial interactions across these places. As a result, this paper provides important tourism implications integrating spatial models with destination planning, which is the foundation of tourism design.

Suggested Citation

  • Park, Sangwon & Xu, Yang & Jiang, Liu & Chen, Zhelin & Huang, Shuyi, 2020. "Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data," Annals of Tourism Research, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:anture:v:84:y:2020:i:c:s0160738320301171
    DOI: 10.1016/j.annals.2020.102973
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    2. Li, Cheng & Zheng, Weimin & Ge, Peng, 2022. "Tourism demand forecasting with spatiotemporal features," Annals of Tourism Research, Elsevier, vol. 94(C).
    3. Yu, Ling & Zhao, Pengjun & Tang, Junqing & Pang, Liang, 2023. "Changes in tourist mobility after COVID-19 outbreaks," Annals of Tourism Research, Elsevier, vol. 98(C).
    4. Wei Guo & Da-Fang Wu & Yue Li & Feng-Xi Wang & Yong-Qi Ye & Hua-Wei Lin & Chi-Fang Zhang, 2022. "Suitability Evaluation of Popular Science Tourism Sites in University Towns: Case Study of Guangzhou University Town," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    5. Ziyu Jia & Yan Jiao & Wei Zhang & Zheng Chen, 2022. "Rural Tourism Competitiveness and Development Mode, a Case Study from Chinese Township Scale Using Integrated Multi-Source Data," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    6. Tianzhi Liu & Fen Luo & Jiawen He, 2023. "Evolution of Spatial Structure of Tourist Flows for a Domestic Destination: A Case Study of Zhangjiajie, China," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    7. Francesco Scotti & Andrea Flori & Piercesare Secchi & Marika Arena & Giovanni Azzone, 2024. "Heterogeneous drivers of overnight and same-day visits," Papers 2402.05679, arXiv.org.
    8. Shuhua Yin & Chengzhou Fu & Guangquan Dai, 2023. "Exploring the Festival Attendees’ Experiences on Social Media: A Study on the Guangzhou International Light Festival," SAGE Open, , vol. 13(1), pages 21582440221, January.
    9. Masiero, Lorenzo & Hrankai, Richard, 2022. "Modeling tourist accessibility to peripheral attractions," Annals of Tourism Research, Elsevier, vol. 92(C).
    10. Shanshan Shi & Mi Li & Ziqiang Li & Jianchao Xi, 2023. "Spatial Heterogeneity and Influencing Factors of High-Grade Tourist Attractions in the Tibetan Plateau," IJERPH, MDPI, vol. 20(5), pages 1-16, March.
    11. 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.
    12. Yajun Cao & Jianguo Liu, 2022. "The Spatial Spillover Effect and Its Impact on Tourism Development in a Megacity in China," Sustainability, MDPI, vol. 14(15), pages 1-18, July.
    13. Zhang, Xiaowei & Yang, Yang & Zhang, Yi & Zhang, Zili, 2020. "Designing tourist experiences amidst air pollution: A spatial analytical approach using social media," Annals of Tourism Research, Elsevier, vol. 84(C).

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