Sequential Pattern Mining of Tourist Spatiotemporal Movement

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

Abstract

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.

References

Basiri, A., Amirian, P., & Mooney, P. (2016). Using crowdsourced trajectories for automated OSM data entry approach. Sensors, 16(9), 1510. 10.3390/s16091510

Basiri, A., Amirian, P., Winstanley, A., & Moore, T. (2018). Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data. Journal of Ambient Intelligence and Humanized Computing, 9, 413–427. https://doi.org/10.1007/s12652-017-0550-0

Bermingham, L., & Lee, I. (2014). Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Science, 29, 379–389. https://doi.org/10.1016/j.procs.2014.05.034

Caldeira, A.M., & Kastenholz, E. (2015). Spatiotemporal behaviour of the urban multi-attraction tourist: Does distance travelledfrom country of origin make a difference? Tourism & Management Studies, 11(1), 91-97. https://www.researchgate.net/publication/277863124_Spatiotemporal_behaviour_of_the_urban_multi-attraction_tourist_does_distance_travelled_from_country_of_origin_make_a_difference

Fournier-Viger, P. (2010). A hybrid model for supporting learning in procedural and ill-defined areas [Doctoral dissertation, Université du Québec à Montréal]. https://www.researchgate.net/publication/49121771_Un_modele_hybride_pour_le_support_a_l'apprentissage_dans_les_domaines_proceduraux_et_mal_definis

Grinberger, A., Shoval, N., & Mckercher, B. (2014). Typologies of tourists’ time–space consumption: A new approach using GPS data and GIS tools. Tourism Geographies, 16(1), 105 - 123. https://doi.org/10.1080/14616688.2013.869249

Kumar, K.M.V.M., & Srinivas, P.V.S. (2011). Algorithms for mining sequential patterns. International Journal of Information Sciences and Application, 3(1), 59-69. http://www.irphouse.com/ijisa/ijisav3n1_9.pdf

Lew, A., & McKercher, B. (2006). Modeling tourist movement: A local destination analysis. Annals of Tourism Research, 33(2), 403–423. doi:10.1016/j.annals.2005.12.002

Li, Z. (2014). Spatiotemporal pattern mining: Algorithms and applications. In C.C. Aggarwal, & J. Han (Eds.), Frequent pattern mining (pp. 283-306). Springer International Publishing. 10.1007/978-3-319-07821-2_12

Ni, B., Shen, Q., Xu, J., & Qu, H. (2017). Spatio-temporal flow maps for visualizing movement and contact patterns. Visual Informatics, 1(1), 57-64. https://doi.org/10.1016/j.visinf.2017.01.007

Raun, J., Ahas, R., & Tiru, M. (2016). Measuring tourism destinations using mobile tracking data. Tourism Management, 57, 202-212. https://doi.org/10.1016/j.tourman.2016.06.006

Talpur, A., & Zhang, Y. (2018, October 23-26). A study of tourist sequential activity pattern through Location Based Social Network (LBSN) [Conference session]. 2018 International Conference on Orange Technologies (ICOT), Nusa Dua, BALI, Indonesia. 10.1109/ICOT.2018.8705895

The Tourism Act. (2004). 14th Congress. 2nd Regular Session. Metro Manila. http://tourism.gov.ph/Downloadable%20Files/Updated_RA_9593_and_IRR_(as_of_01_Nov_2020).pdf

Thimm, T., & Seepold, R. (2016). Past, present and future of tourist tracking. Journal of Tourism Futures, 2(1), 43-55. 10.1108/JTF-10-2015-0045

Xia, J., Ciesielski, V., & Arrowsmith, C. (2005). Data mining of tourists’ spatio-temporal movement patterns - a case study on Phillip Island [Paper presentation]. 8th International Conference on GeoComputation, University of Michigan, USA. http://www.geocomputation.org/2005/Xia.pdf
Published
2021-05-29
How to Cite
AbucejoM. I., & CuizonJ. (2021). Sequential Pattern Mining of Tourist Spatiotemporal Movement. Recoletos Multidisciplinary Research Journal, 9(1), 69-78. https://doi.org/10.32871/rmrj2109.01.07
Section
Articles