Abstract
Information provided by geotagged photos allow us to know where and when people have been, supporting a better understanding about tourist’s movement patterns across a destination. The aim of this paper is to study tourists’ movement patterns during their staying in Porto through the analysis of geotagged photos in order to fulfill marketing segmentation in an innovative way. For that purpose, the SPADE algorithm was used to find sequence patterns of tourists paths based on the time and location of the photos collected. Then, the K-Mode clustering algorithm was applied to these sequences in order to find identical behaviors in terms of paths followed by tourists. At the same time, in order to understand the influence of the different attractions on tourists’ paths, we performed a Social Network Analysis of the touristic attractions (spots, museums, streets, monuments, etc.). Based on the time and location of the photos collected, along with personal information, it was possible to understand tourists’ frequent movements across the city and to identify market segments based on a hybrid strategy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fyall, A., Garrod, B.: Tourism Marketing: A Collaborative Approach. Channel View Publications, Bristol (2005)
Newman, M.E.J.: Networks: An Introduction, 1st edn. Oxford University Press, Oxford (2010)
Zaki, J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001)
Xia, J., Evans, F., Spilsbury, K., Ciesielski, V., Arrowsmith, C., Wright, G.: Market segments based on the dominant movement patterns of tourists. Tourism Manag. 31(4), 464–469 (2010)
Gilaninia, S., Mohammadi, M., Rasht, I.: Impact of marketing on tourism industry. Tourism Manag. 5(2), 39–46 (2015)
Kotler, P., Sidney, J.: Broadening the concept of marketing. J. Market. 33, 10–15 (1969)
Leung, X., Wang, F., Wu, B., Billy, B., Stahura, K., Xie, Z.: A social network analysis of overseas tourist movement patterns in Beijing: the impact of the Olympic Games. Int. J. Tourism Res. 14(5), 469–484 (2012)
Zheng, Y.-T., Zha, Z.-J., Chua, T.: Mining travel patterns from geotagged photos. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 56 (2012)
Memon, I., Chen, L., Majid, A., Mingqi, L., Hussain, I., Chen, G.: Travel recommendation using geo-tagged photos in social media for tourist. Wirel. Pers. Commun. 80(4), 1347–1362 (2015)
Kádár, B., Mátyás, G.: Where do tourists go? Visualizing and analysing the spatial distribution of geotagged photography. Cartographica: Int. J. Geogr. Inf. Geovisualization 48(2), 78–88 (2013)
Wind, J., Bell, D.: Market Segmentation. The Marketing Book, p. 222 (2007)
Vu, Q., Li, G., Law, R., Ye, H.: Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. Tourism Manag. 46, 222–232 (2015)
Spyrou, E., Mylonas, P.: An overview of Flickr challenges and research opportunities. In: 2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 88–93. IEEE, Corfu (2014)
Crandall, D., Snavely, N.: Networks of photos, landmarks, and people. Leonardo 443, 240–243 (2011)
Girardin, F., Dal Fiore, F., Blat, J., Ratti, C.: Understanding of tourist dynamics from explicitly disclosed location information. In: Symposium on LBS and Telecartography. Citeseer (2017)
McKercher, B., Shoval, N., Ng, E., Birenboim, A.: First and repeat visitor behaviour: GPS tracking and GIS analysis in Hong Kong. Tourism Geographies 141, 147–161 (2012)
Huang, J.Z.: Clustering Categorical Data with k-Modes (2019)
Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 23, 283–304 (1998)
Ralambondrainy, H.: A conceptual version of the K-means algorithm. Pattern Recogn. Lett. 1611, 1147–1157 (1995)
Fotis, J., Buhalis, D., Rossides, N.: Social media use and impact during the holiday travel planning process. Inf. Commun. Technol. Tourism 1324, 13–24 (2012)
Munar, M., Jacobsen, J.: Motivations for sharing tourism experiment through social media. Tourism Manag. 43, 46–54 (2014)
Bermingham, L., Lee, F.: Spatio-temporal sequential pattern mining for tourism sciences. Proc. Comput. Sci. 29, 379–389 (2014)
Acknowledgments
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, A., Campos, P., Ferreira, C. (2019). Sequence and Network Mining of Touristic Routes Based on Flickr Geotagged Photos. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-30244-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30243-6
Online ISBN: 978-3-030-30244-3
eBook Packages: Computer ScienceComputer Science (R0)