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Sensors 2018, 18(9), 2972; https://doi.org/10.3390/s18092972

Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data

1
Department of Telecommunications and Information Processing, Ghent University, St-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
2
ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, EC090112 Guayaquil, Ecuador
3
Department of Geography, Ghent University, Krijgslaan 281 (S8), B-9000 Ghent, Belgium
Address: St-Pietersnieuwstraat 41, B-9000 Ghent, Belgium.
*
Author to whom correspondence should be addressed.
Received: 9 July 2018 / Revised: 17 August 2018 / Accepted: 3 September 2018 / Published: 6 September 2018
(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)
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Abstract

Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region. View Full-Text
Keywords: tourism management; big data analytics; smartphones; human mobility; behavioural clustering; market segmentation; crowdsourcing tourism management; big data analytics; smartphones; human mobility; behavioural clustering; market segmentation; crowdsourcing
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Rodríguez, J.; Semanjski, I.; Gautama, S.; Van de Weghe, N.; Ochoa, D. Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data. Sensors 2018, 18, 2972.

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