## 2 Theoretical Framework
### Uncovering the Mobility Patterns of Clusters of Tourists
They identified this status from the home and work location of the users. In addition, they showed that socio-economic status and urban spatial structure are the factors that have a stronger influence on the mobility of visitors. On the other hand, [PERSON] et al. [89] analyzed the mobility patterns of visitors from the analysis of social media check-in. They used a deep learning method to try to classify tourists by the purpose of their travel. Studies of mobility patterns that employ Artificial Intelligence techniques are still emerging, and very few of them try to identify meaningful clusters of tourists. [PERSON] [90]obtained trajectory data from different location-based services and tourism applications, and then they applied cluster analysis to identify the most popular tourist attractions. DBSCAN clustering was used to identify spatial clusters of trajectories at the points of greatest interest, but not to identify clusters of tourists. [PERSON] et al. [87] analyzed a mobile positioning data set in order to know the nationality and movement patterns of foreign tourists in South Korea. They used network analysis to identify the structure of tourism destinations based on patterns of travel flow, and clustering analysis to identify similar patterns. They identified areas of destinations with different visit patterns of tourists according to their nationality, but not clusters of tourists. Instead, [PERSON] et al. [91] used cluster analysis to identify automatically clusters of tourists around points of interest at destinations. They studied the relationship between human mobility and tourist attractions through geo-located images of Italian destinations provided by Flickr users. The results showed that social media data are a valuable source to understand the behavior of tourists in a destination. However, the study did not define or specify the different clusters of tourists and their different mobility flows between the most popular attractions. Considering that, despite the difficulties and limitations, GSMD data can be analyzed in order to make segmentations more precise than the ones based on the country of origin, and also considering that mobility patterns are a key issue for DMOs; this study aims to make a contribution to the current challenges of analyzing tourism flows in destinations. Hence, its goal is to provide and test a methodology to create tourists' clusters (groups of visitors with similar characteristics, preferences, and patterns of travel) taking into account not only their personal and cultural interests, leisure activities, and the context, but also their mobility patterns on the destination. The authors' perception is that, from an academic perspective, this is a fundamental issue in order to better understand tourists spatial and temporal behavior. Additionally, from the point of view of the developers of tourists' experiences, this methodology is a new tool that can help to offer services to tourists in a more personalized way, enhancing the communication to the appropriate targets or the attraction of new ones [37]. Finally, it can help to improve the management of critical flows in certain points of interest of the tourism destinations, helping to minimize social stresses, built environment management difficulties, transportation issues, and frictions between tourists and local population in situations of congestion and overcrowding [40].