Sequential Pattern Mining of Tourist Spatiotemporal Movement




data mining, sequence pattern mining, spatiotemporal, flowmaps, Aprioriall


The study aimed to develop a software application to capture tourist activity information, extract movement patterns from the dataset through sequential pattern mining (SPM), and visualize spatiotemporal movement. Tourist activity information was captured through crowdsourced trajectory movements by scanning unique QR (Quick Response) codes for each visited tourist spots. The AprioriAll algorithm was used to find frequent trajectory patterns on tourist visits. The resulting maximal k-sequences and their subsequences represent the recommended trip itinerary. The spatial and temporal movements were visualized through a flow map and a heat map, respectively. The directed edges in the flow map show the recommended sequence of tourist sites to visit. The heat map shows the density of tourist visits in different areas at time intervals. The application was validated with selected tour planning experts to verify functional suitability, usability, and acceptability. Experimental results show positive indicators that the application met the users’ expectations.


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How to Cite

Abucejo, M. I. R., & Cuizon, J. C. (2021). Sequential Pattern Mining of Tourist Spatiotemporal Movement. Recoletos Multidisciplinary Research Journal, 9(1), 69–78.