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New Trends in Tourism Business Intelligence
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New Trends in Tourism Business Intelligence

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 April 2019) | Viewed by 11535

Special Issue Editors

Dr. Agustín J. Sánchez-Medina

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Guest Editor
Instituto Universitario de Ciencias y Tecnologías Cibernéticas (IUCTC), University of Las Palmas de Gran Canaria, 30, 35001 Las Palmas de Gran Canaria, Las Palmas, Spain
Interests: business intelligence; intellectual capital; sustainability management
Special Issues, Collections and Topics in MDPI journals
Dr. Jesús B. Alonso-Hernández

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Guest Editor
Institute for Technological Development and Innovation in Communications (IDeTIC), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
Interests: patter recognition; signal processing; classification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan Manuel Benítez del Rosario

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Guest Editor
Instituto Universitario de Ciencias y Tecnologías Cibernéticas (IUCTC), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
Interests: Tourism management, technology innovation in tourism industry

Special Issue Information

Dear Colleagues,

Nobody doubts the importance of tourism in the global economy and the effect it has on the economic, social and environmental development of the regions where it is present. It should be noted that tourism activity is developed with the participation of companies with different types of activity (accommodation, transportation, catering, etc.), of different sizes—local and international—in different and changing social and environmental environments and under the control of public organizations with a very different degree of intervention. In addition, all this is intended to serve a type of customer that may be most heterogeneous. All this makes the management of tourism a highly complex task, either at the level of destinations or at the level of companies. The purpose of this Special Issue is to publish an article of empirical research and theoretical high quality reviews that approaches specific tourism problems. Thus, the articles may address issues that consider tourism from a general perspective, from the point of view of tourist destinations, from the perspective of the companies that operate in the sector, and from the point of view of the final recipient of the service, the tourist.

Prof. Agustín J. Sánchez-Medina
Prof. Jesús B. Alonso
Prof. Juan Manuel Benítez del Rosario
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart tourism
  • Monitoring of social networks
  • Tourism management using artificial intelligence
  • Technology and tourism
  • Data security in touristic companies
  • Person-computer interaction in the touristic field
  • Tourism and the Internet of Things
  • Robotics and service automation in tourism
  • Smart tourism destinations
  • Decision support systems in tourism
  • Predictive models in tourism using artificial intelligence
  • Technology innovation in tourism industry

Published Papers (3 papers)

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Research

16 pages, 2473 KiB  
Article
Astrotourism and Night Sky Brightness Forecast: First Probabilistic Model Approach
by Eleazar C-Sánchez, Agustín J. Sánchez-Medina, Jesús B. Alonso-Hernández and Augusto Voltes-Dorta
Sensors 2019, 19(13), 2840; https://doi.org/10.3390/s19132840 - 26 Jun 2019
Cited by 9 | Viewed by 3491
Abstract
Celestial tourism, also known as astrotourism, astronomical tourism or, less frequently, star tourism, refers to people’s interest in visiting places where celestial phenomena can be clearly observed. Stars, skygazing, meteor showers or comets, among other phenomena, arouse people’s interest, however, good night sky [...] Read more.
Celestial tourism, also known as astrotourism, astronomical tourism or, less frequently, star tourism, refers to people’s interest in visiting places where celestial phenomena can be clearly observed. Stars, skygazing, meteor showers or comets, among other phenomena, arouse people’s interest, however, good night sky conditions are required to observe such phenomena. From an environmental point of view, several organisations have surfaced in defence of the protection of dark night skies against light pollution, while from an economic point of view; the idea also opens new possibilities for development in associated areas. The quality of dark skies for celestial tourism can be measured by night sky brightness (NSB), which is used to quantify the visual perception of the sky, including several light sources at a specific point on earth. The aim of this research is to model the nocturnal sky brightness by training and testing a probabilistic model using real NSB data. ARIMA and artificial neural network models have been applied to open NSB data provided by the Globe at Night international programme, with the results of this first model approach being promising and opening up new possibilities for astrotourism. To the best of the authors’ knowledge, probabilistic models have not been applied to NSB forecasting. Full article
(This article belongs to the Special Issue New Trends in Tourism Business Intelligence)
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25 pages, 4436 KiB  
Article
Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks
by Alexander Bustamante, Laura Sebastia and Eva Onaindia
Sensors 2019, 19(11), 2612; https://doi.org/10.3390/s19112612 - 08 Jun 2019
Cited by 9 | Viewed by 4318
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue New Trends in Tourism Business Intelligence)
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21 pages, 3867 KiB  
Article
Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands
by Javier J. Sánchez-Medina, Juan Antonio Guerra-Montenegro, David Sánchez-Rodríguez, Itziar G. Alonso-González and Juan L. Navarro-Mesa
Sensors 2019, 19(10), 2388; https://doi.org/10.3390/s19102388 - 24 May 2019
Cited by 8 | Viewed by 3001
Abstract
The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, [...] Read more.
The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology. Full article
(This article belongs to the Special Issue New Trends in Tourism Business Intelligence)
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