## 4 Analysis, Results and Discussion  
### Descriptive Analysis  
To demonstrate how the SOM algorithm worked, let us consider cluster 165, one of the 22 clusters associated with nightlife activities. This cluster includes most of the grids located in the Bairro Alto area (Figure 3), a well-established nightlife landmark in Lisbon. Figure 3: Grids corresponding to Cluster 165, the Bairro Alto area. Figure 2: Nightlife areas identified in the city of Lisbon (represented by the blue squares). Figure 4 shows all the time series that the SOM algorithm grouped in Cluster 165 in gray, with the red line depicting the trend of the data. In all these kinds of figures, the y-axis represents the normalized value of the number of people in the cluster (scale between 0 and 1). Although Bairro Alto is known for being a nightlife and bar hotspot, it also holds significant touristic value, thus explaining the first increase in mobile phones by grid during Friday's 8 a.m. to 5 p.m. timeframe. Approaching dinner time, there is a small decrease in the C1 variable, but it quickly increases again, peaking at midnight. This is because Bairro Alto is mainly frequented by people who want to start the night before going to areas where bigger nightclubs are located, during dawn hours; that is why there is a downward decrease during this period, when, presumably, people leave these grids to continue the night in more mainstream nightclubs. The line gradually grows back again from Saturday 9 a.m. up until 11:59 p.m., where the graphic ends, in a similar fashion as on the previous day. To give further perspective on how nightlife areas may vary, let us consider another cluster found by the algorithm, Cluster 8 (Figure 5). This cluster mainly encompasses the Santos dock area, where several nightclubs are located. This area consists mostly of night spots, a fact reflected by the map in Figure 5.  
As is easily observed in Figure 6, there is one big spike in mobile phone entries after 1 a.m. Having insight into how the Lisbon nightlife behaves, this peak presumably correlates with a decrease in entries happening around the same time in the previously analyzed area, Bairro Alto, as people leave the smaller bars to spend the rest of the night in the more popular nightclubs. In comparison with Bairro Alto, the Santos dock area has very little touristic value, thus having a much smaller concentration of people during every other period outside of the peak. In order to understand how tourists behave in the nightlife areas, a subsequent clustering was made, where the target variable was C2 (the number of distinct mobile phones in roaming in the grid), and only the time series corresponding to nightlife areas were included in the model. Once again, the SOM algorithm was used. Figure 7 shows one of the clusters obtained from the C2 variable, Cluster 79. This cluster includes four grids referring to the Cais do Sodre and Rua do Alecrim area. Like Bairro Alto, this is an area with popular nightlife activity, depicted by the red line in Figure 8, where the C2 variable values are plotted between Friday 12 a.m. and Saturday  
Figure 4: Time series for Cluster 165. 11:59 p.m. It can be observed that from Friday 8 a.m., there is an increase in mobile phones by grid, an ascending trend that continues until the maximum, which occurs around Saturday 1 a.m. This is quite reasonable, given that this is an area that has both tourist attractions and nightlife establishments. After 1 a.m. on Saturday, the values start to decrease, since, similarly to Bairro Alto, this is the time when people try to prolong the night in nightclubs or simply leave the area to go home. From 9 a.m. on Saturday, the Cais do Sodre area follows the trend of the previous day, once again showing demand from people of foreign nationality. Another cluster that is worth analyzing is Cluster 70 (Figure 9), which refers to the Santos area (Cluster 70), more specifically, Cais da Viscondessa, where the K Urban Beach nightclub is located.