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

Jorge Rodríguez,Ivana Semanjski,Nico Van De Weghe,Daniel Ochoa,Sidharta Gautama

doi:10.3390/s18092972

Jorge Rodríguez, Ivana Semanjski + Show 3 more

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https://doi.org/10.3390/s18092972

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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.

Highlights

People are travelling for many purposes such as going to school, work or shopping, visiting family and friends or going on holidays
Extending the current research in the tourism management domain, and in the meantime addressing the above-mentioned limitations, the aim of this study is to explore how ubiquitous massive sensing systems can be used in tourism population segmentation and what additional tourism mobility related insights can be extracted based on such approach
The fundamental research contributions of this work can be situated in the following areas: (i) we demonstrate the applicability of the ubiquitous massive sensing for tourism management purposes and related decision-making processes; (ii) we develop a big data based market segmentation approach for tourism applications; and (iii) we provide the first big data based insight into mobility behaviour of different tourism market segments

Summary

Introduction

People are travelling for many purposes such as going to school, work or shopping, visiting family and friends or going on holidays. Some of these activities, such as tourism related travel, have a strong seasonal character [1]. Understanding tourism related mobility behaviour has a strong impact on tourist destination mobility system planning and its reachability [2]. Understanding the patterns that visitors utilize to explore the destination region, whether this is a day visit or a whole vacation period, can serve as a basis for the creation of tourism related activities. The literature shows that provision of the higher level of service at these locations influences the selection of places visited by tourists and the Sensors 2018, 18, 2972; doi:10.3390/s18092972 www.mdpi.com/journal/sensors

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