Abstract
Nowadays the tourism industry demands real-time data and advanced breakdown of indicators. The increasing availability of contents generated by users, such as Internet and Social Networks allow users to express their points of view while making them available. 22% of the time a user was online, it's in social networks, corresponding to 110 billion minutes. The study intends to show an example of an Integrated Tourism Analysis Platform (ITAP) with the use of mobile data linked to touristic venues and events; social media data; Open and Crowdsourced data with the geotagged and sensor city information related to environmental aspects, such as crowding, human movement, noise, light and pollution. Advanced Big Data techniques to produce Interactive Dashboards, Web-based Crowdsourcing Framework and a Responsive and Augmented GeoViewer will be used to ensure a more robust Integral Tourism service, enhancing the displaying of the relationship between the local attractions (events, cultural venues) and the foreign visitors in an interactive and visual immersive way.
TopIntroduction
Tourism in the European Union (EU) is the third largest socio-economic activity and the world leader in tourism destinations, making an important contribution to the EU's gross national product and employment. Tourism is an important sector for sustainable local development, by preserving and enhancing cultural and natural heritage. The European Commission estimated that European tourism industries are almost 2 million, most of them small and medium-sized (SMEs), providing work for 5% of the total EU workforce (Juul, 2015).
In the tourism sector, data analytics using information on online platforms are the order of the day, offering results and value conclusions. Even so, according to the literature review (Wong, Law & Li, 2017), the work done so far, has focused on technical aspects of the analytical, and few of them in the tourist management. Of these, a part has focused on understanding the mobility of tourists based on different criteria, such as space, time, and the profile of visitors (Vu, Leung, Rong & Miau, 2016; García-Palomares, Gutiérrez & Mínguez, 2015; Kádár & Gede; 2013; Leung, Vu, Rong & Miau, 2016). And so, understand the relationship between types of attractions and types of tourists or the difference between visitors and residents. Other works have also used the digital track, to make tourist recommendations (Xu, Chen & Chen, 2015; Okuyama & Yanai, 2013; Jiang, Yin, Wang & Yu, 2013; Kurashima, Iwata, Irie & Fujimura, 2013). But only one work (Zhao et al., 2017) has been found that analyzes the images to predict the interests of visitors and another recent work (Cai, Lee & Lee, 2018), which takes into account the factors of space and time sequentially.
Online social networks have features common to several user profiles, such as demographics (location, age, gender, education) and interests (religion, sports, politics, music, literature). Such a profile can act as an integration element with other elements (Boyd, 2008). Features such as updates and comments allow the users to evaluate the content and to serve as recommendations to other users. The metadata gives the possibility to create references such as the Twitter hashtags (#) with titles, descriptions, category and keywords, from which, it’s possible to filter or extract information.
Key Terms in this Chapter
Natural Language Processing: Sub-field of Artificial Intelligence that is focused on enabling computers to understand and process human languages, to get computers closer to a human-level understanding of language.
Data Analytics: Discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making.
Smart Data: Formatted digital information so it can be acted upon at the collection point before being sent to a downstream analytics platform for further data consolidation and analytics.
Social Network Analysis: Process of investigating social structures through the use of networks and graph theory.
Big Data: Mainly was used as a term to define a large or complex volume of structured, semi-structured and unstructured data that can be mined for information and used in machine learning projects and other advanced analytics applications.
ITAP: Integrated Tourism Analysis Platform.
Sentiment Analysis: The process of computationally identifying and categorizing opinions expressed in a text, especially in order to determine whether the writer's attitude towards a particular topic is positive, negative, or neutral.
Crowdsourcing: Obtaining information’s or inputs by enlisting the paid, or normally unpaid services of a large number of people, typically via internet.