The initiative aims to couple different sources of tourism big data to obtain an improved understanding of tourist activity patterns in and cross cities. The insights will be further used to support local and regional tourism planning, and develop tourism recommendation systems for domestic and international travelers. For more information, please visit https://yangxu-git.github.io, or contact Dr. Yang Xu via yang.ls.xu@polyu.edu.hk.
Characterizing destination networks through mobility traces of international tourists
We demonstrate how large-scale tourist mobility data can be linked with network science approaches to better understand tourism destinations and their interactions. By analyzing a mobile positioning dataset that captures the nationality and movement patterns of foreign tourists to South Korea, we employ a few metrics to quantify the network properties of tourism destinations, aiming to reveal the collective dynamics of tourist movements and key differences across nationalities.
Measure inter-city tourist flows
Quantify destination attractiveness
Preferences across nationalities
“Travel Communities” extracted from inter-city tourist movements
Travel Recommendation System
Leveraging navigation data, vehicle trajectories, consumer data and Point of Interest (POI), we have developed algorithms as proof of concept for travel recommendation system for Jeju, South Korea.
Tourism movement patterns on weekdays vs. weekends
Location recommendation
Activity recommendation
Understanding tourist time use
By using a large-scale mobile phone data set collected in three cities in South Korea (Gangneung, Jeonju, and Chuncheon), we develop a computational framework to enable accurate quantification of tourist time use, the visualization of their spatiotemporal activity patterns, and systematic comparisons across cities. The framework consists of several approaches for the extraction and semantic labeling of tourist activities, visual-analytic tools (time use diagram, time–activity diagram) for examining their time use, as well as quantitative measures that facilitate day-to-day comparisons.
Daily time-use diagram of travelers in cities
Spatial patterns of tourist activities
Acknowledgement:
School of Hotel and Tourism Management, The Hong Kong Polytechnic University
Jeju Special Self-Governing Province
Korea Tourism Organization
Jeju Tourism Organization
Publications:
[1] Xu, Y., Li, J., Xue, J., Park, S. and Li, Q., 2021. Tourism Geography through the Lens of Time Use: A Computational Framework Using Fine-Grained Mobile Phone Data. Annals of the American Association of Geographers (in press)
[2] Xu, Y., Xue, J., Park, S. and Yue, Y., 2021. Towards a multidimensional view of tourist mobility patterns in cities: A mobile phone data perspective. Computers, Environment and Urban Systems, 86, p.101593.
[3] Xu, Y., Li, J., Belyi, A. and Park, S., 2021. Characterizing destination networks through mobility traces of international tourists—A case study using a nationwide mobile positioning dataset. Tourism Management, 82, p.104195.
[4] Park, S., Xu, Y., Jiang, L., Chen, Z. and Huang, S., 2020. Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data. Annals of Tourism Research, 84, p.102973.
