## 4 Results
### Tourist Mobility/Flow Analysis  
To analyze the mobility of tourists within each cluster, bigrams \((A,B)\) were extracted from their sequences of visits. These bigrams are \(n\)-grams of size two that represent the movement of a tourist from A to B (i.e., the user sent a tweet from A and the next one from B). Given a sequence of items, \(n\)-grams are unique sets of \(n\) directly adjacent items. For example, a sequence \(S=\{a,b,c,d\}\) has the following 2-grams (popularly known as bigrams) \(2 grams=\{(a,b),(b,c),(c,d)\}\). \(N\)-grams must maintain the order in the original sequence and must be unique.  
Bigrams were extracted from the sequences of visited places of each tourist in a certain cluster, and we counted their frequency of occurrence (i.e., the number of times a bigram appeared in the cluster). The clusters 0, 2, 3, 7, and 16 were selected to illustrate this analysis because of their diversity. The top 20 bigrams with a higher frequency in each cluster were taken as the most relevant. Figure 3 shows heat map plots of the tourist mobility within clusters, where the color intensity represents the movement between POIs measured by the frequency of occurrence.  
As it can be seen from Figure 3, _Basilica de la Sagrada Familia_ acts as a hub in cluster 0, as all other POIs are directly connected to it. In most cases, tourists are visiting the other POIs after visiting _Basilica de la Sagrada Familia_ because its outflows exceed its inflows from other locations, except _Casa Batllo_, _Catedral de la Santa Creu i Santa Eulalia_, and _Al actor Iscle Soler_, which might be a result of route preference. The strongest connection can be seen between _Basilica de la Sagrada Familia_ and _Park Giell_ with almost equivalent inflow and outflow between them. In the case of cluster 2, _Basilica de la Sagrada Familia_ is once again the most interconnected attraction but with more inflows than in cluster 0, and the strongest connection is between _Basilica de la Sagrada Familia_ and _Camp Nou_. The other selected clusters (3, 7, and 16) show connections between various attractions with no clear hub.  
Figures 4 and 5 help to further understand tourist mobility with network graphs plotted on the Barcelona city map. Nodes represent POIs, and edges are the bigrams that connect them (the wider is the edge, the more tourists travel between those two locations). It can be seen that proximity plays a role in why _Basilica de la Sagrada Familia_ acts as a hub in clusters 0 and 2. In cluster 3, the focus is the Mediterranean Sea as most POIs are near the beach, except in the case of _Basilica de la Sagrada Familia_, _Park Giell_, and _Camp Nou_, as tourists are willing to travel out of the way to see these POIs. Clusters 7 and 16 show two different kinds of tourists. The former is focused on bars near the city center, whereas the latter visits many different kinds of places all around the city, including amusement parks, shop malls, and the beach, but also the most popular venues.  
In summary, the analyzed clusters showed inter-connected POIs which are of interest to certain groups of tourists. In some cases, they focus on the popular attractions, but in others they also visit places off the beaten track. The graphic representation in the map of the mobility of different clusters of tourists in a destination provides new knowledge to DMOs, who could take them into account to define new tourist routes, to create targeted marketing campaigns or to optimize transport routes between heavily connected POIs for different types of tourists.  
Figure 3: Mobility heat-maps for clusters 0, 2, 3, 16, and 7 representing bigrams with movement from left to bottom.  
Figure 4: Tourist mobility pattern between attractions in Barcelona for clusters 0, 2, 3, and 7.