Tourist Movement Analysis Based on Social Network Information Using Association Rules

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Sirichanya Janmee
Kraisak Kesorn,


This research aims to develop a model capable of illustrating the movement pattern of tourists in the lower part of northern Thailand. The main novelty of this framework is the usage of check-in information available in a social network system, e.g., Foursquare. However, to be able to analyze the movement pattern of the tourists in the lower part of northern Thailand 1, the linearity model is deployed to represent the movement patterns. The result found that the movement pattern of the tourists is mostly the point-to-point. Therefore, this research could be beneficial to tourism authorities of Thailand, local tour companies, hotels, or any nearby tourism business sectors, development of products and services, and promotion to attract tourists.


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Janmee, S., & Kesorn, K. (2022). Tourist Movement Analysis Based on Social Network Information Using Association Rules. Journal of Science Ladkrabang, 31(1), 120–141. Retrieved from
Research article


Harun, A. 2012. The impact of tourism sector to Thai’s Gross Domestic Product (GDP). Proceedings of the 2nd International Conference on Business, Economics, Management and Behavioral Sciences, Bali, Indonesia, 90-95.

World Economic Forum. 2012. Fostering prosperity and regional integration through travel and tourism. The ASEAN Travel and Tourism Competitiveness Report.

Kesorn, K., Juraphanthong, W. and Salaiwarakul, A. 2017. Personalized attraction recommendation system for tourists through check-in data. IEEE Access, 5(1), 26703-26721.

Lew, A. and McKercher, B. 2006. Modeling tourist movements: A local destination analysis. Annals of Tourism Research, 33(2), 403-423.

Pitchayadejanant, K. and Nakpathom, P. 2018. Data mining approach for arranging and clustering the agro-tourism activities in orchard. Kasetsart Journal of Social Sciences, 39(3), 407-413.

Gallo, G., Signorello, G., Farinella, G.M. and Torrisi, A. 2017. Exploiting social images to understand tourist behaviour. Proceedings of international conference on image analysis and processing, Catania, Italy, 707-717.

Höpken, W., Müller, M., Fuchs, M. and Lexhagen, M. 2020. Flickr data for analysing tourists’ spatial behaviour and movement patterns: A comparison of clustering techniques. Journal of Hospitality and Tourism Technology, 11(1), 69-82.

Benckendorff, P. 2014. Attraction, Tourism, Encyclopedia of Tourism. Jafari J., Xiao H. ed, Springer, Cham.

Dietrich, D., Heller, B. and Yang, B. 2015. Data Science & Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data. 1st ed, John Wiley & Sons, Indianapolis, IN.

Olson, D.L. and Lauhoff, G. 2019. Descriptive Data Mining. 2nd ed, Springer, Singapore.