Abstract
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.
Highlights
Tourism is an important economic activity worldwide that significantly contributes to the growth of the economies and to a higher employment
The first type of surveys examine the economic effects of tourism activity and are undertaken by official international organizations such as World Economic Forum (WEF), the United National World Tourism Organization (UNWTO) or the World Travel & Tourism Council (WTTC), among others
For addressing any tourism-related problems, we propose a flexible and adaptable methodology integrated into a 4-layered Business Intelligence (BI) architecture that comprises: the data sources required for the problem at hand; a data integration stage for collecting data from the data warehouse and transforming it into repositories of data targeted to a particular purpose or subject; the online analytical processing (OLAP) designed for data manipulation and analysis, handling of multidimensional data structures, complex calculations, etc.; and the presentation layer that allows to interactively visualize the results from the data analysis, through OLAP cubes
Summary
Tourism is an important economic activity worldwide that significantly contributes to the growth of the economies and to a higher employment It has a great impact on the social, cultural and environmental development of recipient countries. The first type of surveys examine the economic effects of tourism activity and are undertaken by official international organizations such as World Economic Forum (WEF), the United National World Tourism Organization (UNWTO) or the World Travel & Tourism Council (WTTC), among others. These organizations collect data from national and international institutions, such as national tourism administrations, national statistical offices, central banks, the International Monetary Funds (IMF) or the World Bank, and emit statistics that measure tourism throughout the national economy [1,2,3,4]
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