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

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.

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

“…Some LBSNs such as TripAdvisor or Gowalla provide these labels, but in other cases they must be inferred. The option that was chosen in the work reported in this paper was to make queries to the Open Street Map (OSM) server using the Overpass engine to assign POIs to tweets based on proximity and usefulness to tourists [25].…”

BITOUR: A Business Intelligence Platform for Tourism Analysis

“…The variables considered for this ML task are: posting period, time zone, number of posted tweets, number of assigned tweets, percentage of tweets in each category, among others. Details about the clustering method can be found in [46].…”

“…[45]; It has also been used in specific domains such as tourism to know the image that tourists have of a destination, identification of tourists and residents, etc. [46]; Use of geographic information that can be extracted from tweets to determine the routes of users, places of concentration of people and the length of stay in a place [19].…”

Análisis de datos colaborativos e inteligencia de negocio: aplicación al sector turístico

“…Twitter también se ha utilizado en dominios específicos como el del turismo para conocer la imagen que tienen los turistas sobre un destino, identificación de turistas y residentes, etc. [4,87,24].…”

“…Los resultados muestran una precisión de clasificación global de alrededor del 76 %. El autor de esta tesis también realizó un estudio para comparar la información turística presente en OSM con la información oficial de fuentes como la Organización Mundial del Turismo[24] y el Foro Económico Mundial (FEM)[23,24]: Se evaluó la consistencia de la información contenida en el Compendio de Estadísticas Turísticas de la Organización Mundial del Turismo[156] con respecto a la información publicada en OSM, especialmente la información de lugares de alojamiento, comidas y bebidas y de agencias de viajes. Dentro de los resultados obtenidos está la alta correlación que existe entre los datos de ambas fuentes con respecto a la información del alojamiento (0.81), de los sitios de comidas y bebidas (0.87) y las agencias de viaje (0.82) [24]…”