## 2 Theoretical Framework
### Uncovering the Mobility Patterns of Clusters of Tourists  
Previous studies have shown that different types, sub-groups, or clusters of tourists may present different travel behaviors [76; 77; 78; 79]. [PERSON] et al. [80], for instance, identified that cruise passengers with different expenditure levels have different mobility patterns in port destination cities. However, this kind of studies usually apply an ad-hoc combination of analytic techniques that is not easy to generalize. From a complementary perspective, many researchers have followed the digital footprint of tourists [35] to know their mobility in destinations, but the current research shows that it is difficult to analyze all these data by segmenting tourists. In fact, many studies have analyzed tourists' mobility patterns [73; 74] without taking into account the diversity of tourists [37] because of the difficulty of obtaining their socio-demographic data. This aspect can be considered only in those cases in which user information is available, such as the one described by [PERSON] and [PERSON] [81]. In that case, they use information produced by a recommender system to define patterns of visitors depending on the similarities in their observed visit trajectories. Previous GSMD-based studies have focused on the clustering of tourists according to diverse factors. Following [PERSON] et al. [37], studies that segment visitors according to their mobility patterns can be based on non-spatial factors (socio-economic status, gender, age, income, education, race) or on spatial factors. For instance, [PERSON] et al. [82] focused on spatial data and [PERSON] et al. [41] on spatial and temporal data. Nevertheless, very few studies have focused on the analysis of mobility patterns according to the socio-demographic data in order to segment visitors in a destination because, with the currently applied methods of analysis, very little socio-demographic data can be obtained from users. In this vein, several SMA studies based on GSMD analysis have claimed to obtain demographic data from tourists to better understand who they are and to be able to classify them [26; 69; 83]. However, the available information is still very limited [60] and, in some cases, it is even reduced to the country of origin [56]. To name some of those studies, [PERSON] et al. [26] focused on spatial, temporal, and also demographic data from Twitter to discover the tourists flows in a destination, creating tourist profiles and segmenting them by country of origin. Similarly, [PERSON] et al. [77] analyzed the different mobility patterns, popular locations, and routes in Hong Kong of Western and Asian tourists. In the same line, [PERSON] et al. [84] analyzed geo-tagged picture data from Flickr, segmenting domestic and foreign tourists, and [PERSON] et al. [18] also analyzed the mobility of tourists in destinations and their most visited attractions by classifying tourists into foreign tourists and domestic tourists. [PERSON] and [PERSON] [85] analyzed the spatial behavior of visitors at a destination through a relational approach and Trip Advisor data, classifying visitors as local, national, European, and non-European. [PERSON] et al. [86] analyzed the activities carried out in a destination by different groups of tourists also segmenting them by their country of origin. [PERSON] et al. [87] analyzed mobility patterns of tourists in a country, South Korea, and its diverse destinations, according to their nationality or country of origin. [PERSON] et al. [37] analyzed mobility via GSMD from Twitter by considering homogeneous segments of users (state visitors, national visitors, and international visitors), created according to their past visits. However, despite the difficulty of obtaining other socio-demographic data than the origin of users from the GSMD, some mobility studies have tried to take a step further in segmenting visitors. [PERSON] and [PERSON] [88], for example, analyzed their mobility segmenting them by their socio-economic status through Twitter's GSMD.