Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the last few years, Portugal has been witnessing a rapid growth of tourism, which reflects positively
in many aspects, especially in what regards economic factors. Although, it also leads to a number of
challenges, all of them difficult to quantify: tourist congestions, loss of city identity, degradation of
patrimony, etc. It is important to ensure that the required foundations and tools to understand and
efficiently manage tourism flows exist, both in the city-level and country-level.
This thesis studies the potential of Big data to inform destination management organizations. To do
so, three sources of Big data are discussed: Telecom, Social media and Airbnb data. This is done
through the demonstration and analysis of a set of visualizations and tools, as well as a discussion of
applications and recommendations for challenges that have been identified in the market.
The study begins with a background information section, where both global and local trends in tourism
will be analyzed, as well as the factors that affect tourism and consequences of the latter. As a way to
analyze the growth of tourism in Portugal and provide prototypes of important tools for the
development of data driven tourism policy making, Airbnb and telecom data are analyzed using a
network science approach to visualize country-wide tourist circulation and presents a model to
retrieve and analyze social media. In order to compare the results from the Airbnb analysis, data
regarding the Portuguese hotel industry is used as control data