## 4 Analysis, Results and Discussion
### Incorporating the Noise Data
#### 4.2.2 Relationship between Noise Levels and the Number of People  
The methodology to identify the relationship between the noise levels and number of people on a nightlife grid proceeded as follows:  
1. Divide the noise dataset into periods of 48 h, and save those that start on a Friday and end on a Saturday (similar to with the process for the primary dataset in Section 4);
2. Plot every saved time series, and add the corresponding time series of the C1 variable (after both have been normalized);
3. Analyze the [PERSON]'s correlation coefficient between the two curves. The [PERSON]'s correlation coefficient measures the direction and the strength of the linear relationship between two given variables, in this case, the number of mobile phones, and the noise levels. Its values can range from \(-1\) to \(1\), where positive values indicate that the increase in one variable is followed by an increase in the other, and negative values indicate that the increase is followed by a decrease. Zero means that the relationship between the two variables is non-existent, and the closer the coefficient is to 1 (or \(-1\)), the greater the impact a variable has on the other. The expectation is for the coefficient values to be high and positive, meaning that great noise levels are associated with large concentrations of people. Table 3 shows the descriptive statistics of the coefficients obtained for every sensor in each Friday-Saturday period. On average, the only sensors whose noise levels are moderately correlated with the number of mobile phones are RULAEQ0014 and RULAEQ0077, with some values showing strong correlations (greater than 0.7), and the standard deviation being relatively low. RULAEQ0076 shows an average weak correlation, and the remaining sensors show  
Figure 13: Noise sensors and their respective locations, where the clouds represent the sensors and the squares represent the clusters identified by the algorithm with more people. negligible means (the fact that there are high max and minimum values shows that there is no consistency in the direction of the relationship; thus, the average coefficient is close to zero). The reason for these values has mostly to do with the location of the sensors. In general, those with high correlations are placed in areas with a lot of pedestrians, and those with low correlations are located in areas with a lot of car traffic. This reflects Lisbon City Council's strategy of collecting a wide variety of data. Figure 14 shows an example of a strong correlation, whereas Figure 15 shows one that is weak. In the plot of RULAEQ0077, it is evident that the increase in noise is closely related to the increase in the number of people. On the other hand, the plot of RULAEQ0076 shows that an increase in the noise levels may not necessarily lead to an increase in the number of people. The low average coefficients do not necessarily indicate that the noise levels will not improve the accuracy of the LSTM models. Given the max and minimum values in Table 3, one can see that there were often moderate (or even high) correlation coefficients, but the correlations are not consistent, with this inconsistency caused by some factors that the LSTM model may identify and use to improve its forecasts. This potential for the noise levels is evident in Figure 15. The increase in the noise levels in the first half is not matched with an increase in the number of people, but the sudden decrease is strongly correlated with the decrease in the number of people. Figure 14: Strong correlation between the noise sensor and the number of mobile phones.