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Open AccessArticle

Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks

Department Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
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Sensors 2019, 19(11), 2612; https://doi.org/10.3390/s19112612
Received: 19 May 2019 / Revised: 3 June 2019 / Accepted: 6 June 2019 / Published: 8 June 2019
(This article belongs to the Special Issue New Trends in Tourism Business Intelligence)
Promoting a tourist destination requires uncovering travel patterns and destination choices, identifying the profile of visitors and analyzing attitudes and preferences of visitors for the city. To this end, tourism-related data are an invaluable asset to understand tourism behaviour, obtain statistical records and support decision-making for business around tourism. In this work, we study the behaviour of tourists visiting top attractions of a city in relation to the tourist influx to restaurants around the attractions. We propose to undertake this analysis by retrieving information posted by visitors in a social network and using an open access map service to locate the tweets in a influence area of the city. Additionally, we present a pattern recognition based technique to differentiate visitors and locals from the collected data from the social network. We apply our study to the city of Valencia in Spain and Berlin in Germany. The results show that, while in Valencia the most frequented restaurants are located near top attractions of the city, in Berlin, it is usually the case that the most visited restaurants are far away from the relevant attractions of the city. The conclusions from this study can be very insightful for destination marketers. View Full-Text
Keywords: urban tourism; social networks; GIS; business intelligence; tourism behaviour urban tourism; social networks; GIS; business intelligence; tourism behaviour
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Bustamante, A.; Sebastia, L.; Onaindia, E. Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors 2019, 19, 2612.

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