| ABSTRACT | Global warming affects and controls the tourism potential in traditional touristic destinations and that is the case of the Mediterranean, which is one of the most favorable region worldwide to visit. It is known that tourism, especially summer tourism, can be described by the Triple S (Sun, Sea and Sand), which is an abundant feature of the Mediterranean region. Many of the controlling tourism factors for the triple S are depending on weather and local climate. Therefore, there is a strong concern of the impacts of global warming on Mediterranean climate, based on future climate projections by regional climate models. So a legitimate question sounds to an increasing extent: Do the tourists turn their back to Mediterranean resorts due to global warming? What is the best place to go for summer holidays? Do the weather conditions affects their touristic destination preferences directly? The answers to these queries could be addressed by analyzing the trends of the tourists’ preference to Mediterranean touristic destinations against northern competitive resorts. To this objective, this study investigates the Big Data, which is a brand new yet powerful tool which provide a vast potential on scientific research. According to past and contemporary studies the atmospheric conditions could alter the perception, the psychology and the way the citizens behave. The Google Trends is a facility of the Google company that counts the frequency of particular search-terms around the world incorporating temporal and spatial criteria. This study aims to investigate via the Google Trends’ data the way the citizens choose summer holiday destinations. For the analysis of the searching behavior the times series of specific terms were correlated with the thermal conditions respectively. More specifically the analysis of Google Trends data reveals a striking influence of the weather conditions on the searching frequency of specific popular Mediterranean destinations. Additionally, this study notifies the potential of this Big Data dataset on the biometeorological research as a connection between the atmospheric conditions and the subsequent human’s decision making and behavior. |