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Article

Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour

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Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 46, 9052 Gent-Zwijnaarde, Belgium
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Flanders Make, B-3920 Lommel, Belgium
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Facultad de Ingeniería en Electricidad y Computación, Campus Gustavo Galindo Km 30.5 Vía Perimetral, ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, P.O. Box 09-01-5863, Guayaquil EC090112, Ecuador
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Research Centre Coastal Tourism, HZ University of Applied Sciences, Edisonweg 4, 4382NW Flushing, The Netherlands
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Author to whom correspondence should be addressed.
Current address: Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 46, 9052 Gent-Zwijnaarde, Belgium.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110686
Received: 18 September 2020 / Revised: 23 October 2020 / Accepted: 13 November 2020 / Published: 17 November 2020
(This article belongs to the Special Issue Smart Tourism: A GIS-Based Approach)
Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study. View Full-Text
Keywords: tourism management; hotspot; crowdsourcing; big data analytics; human mobility; behavioural clustering; clustering evaluation tourism management; hotspot; crowdsourcing; big data analytics; human mobility; behavioural clustering; clustering evaluation
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MDPI and ACS Style

Rodríguez-Echeverría, J.; Semanjski, I.; Van Gheluwe, C.; Ochoa, D.; IJben, H.; Gautama, S. Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour. ISPRS Int. J. Geo-Inf. 2020, 9, 686. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110686

AMA Style

Rodríguez-Echeverría J, Semanjski I, Van Gheluwe C, Ochoa D, IJben H, Gautama S. Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour. ISPRS International Journal of Geo-Information. 2020; 9(11):686. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110686

Chicago/Turabian Style

Rodríguez-Echeverría, Jorge, Ivana Semanjski, Casper Van Gheluwe, Daniel Ochoa, Harm IJben, and Sidharta Gautama. 2020. "Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour" ISPRS International Journal of Geo-Information 9, no. 11: 686. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110686

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