## 3 Experimental Setup
### Data Description  
Accessing data on communications is very problematic for researchers, as accessing such data usually requires signing non-disclosure agreements, research contracts with private companies, etc. Consequently, the limited availability of open datasets restricts scientific research in this domain. The emergence of research challenges that offer access to datasets to a wider academic community has emerged as an interesting solution. For example, Orange Telecom proposed in 2013 [33] and 2014-2015 [34] the \"Data for Development\" (D4D) challenges, whose datasets are no longer available. For this study, we will use an openly available dataset provided by Telecom Italia in collaboration with various academic and research institutions. This dataset is particularly valuable as it combines multiple data sources, including telecommunications, weather, news, social networks, and electricity data from the province of Trento [27]. Our analysis focuses on the telecommunications component, which consists of call detail record (CDR) data aggregated in 10 min intervals across a grid overlay of 6575 squares (each cell measuring approximately 235 \(\times\) 235 m) covering the province of Trento (Figure 1). When users interact with the telecommunications network, the operator records these interactions through CDRs, which include the handling of the radio base station (RBS) and timestamp. Each RBS covers a given territorial area v and enables user geolocation through RBS coverage maps (\(RBSc\_map\)). Grid squares may be overlapped by more than one RBS coverage area or vice versa. To aggregate the spatial data, the number of interactions or records \(S_{i}(t)\) within a grid square \(i\) at time \(t\) is calculated according to the methodology outlined in the original study [27]:  
\[S_{i}(t)=\sum_{v\in RBSc\_map}R_{v}(t)\frac{A_{v\cap i}}{A_{v}}, \tag{1}\]  
where \(R_{v}(t)\) is the number of records in \(v\) at time \(t\), \(A_{v}\) is the area of \(v\), and \(A_{v\cap i}\) is the area of the intersection of \(v\) and the square \(i\). The number of records in the datasets \(S^{\prime}_{i}(t)\) is calculated as follows:  
\[S^{\prime}_{i}(t)=S^{\prime}_{i}(t)k, \tag{2}\]  
where \(k\) is a constant that hides the true number of records. The CDR system logs the following types of user activities. The system records both incoming (smsin) and outgoing (smsout) SMS messages, generating a separate CDR for each message sent or received. For voice calls, the system  
Figure 1: Grid system of Trento. creates individual CDRs for both incoming (callin) and outgoing (callout) calls at the moment they occur. Internet activity tracking is more complex. The system generates CDRs at both the beginning and end of each internet connection. During active connections, additional CDRs are created under two conditions: when the connection duration reaches 15 min or when data usage exceeds 5 MB. Pre-aggregation and anonymization of the data guarantee that no individual's private activities or personal mobility patterns can be tracked. The data itself consist of text files, each containing entries in the following specific fields: cell identifier, date, time, number of incoming SMS messages (smsin), sent SMS messages (smsout), incoming calls (callin), outgoing calls (callout), and the Internet. A sample of the file is shown in the following table (Table 1). It shows registers of the cellid=5201 from the area of the historic center of Trento (_Centro Storico_). The country code (cc) represents the nation from which the SMS or calls are initiated or received, depending on whether it is an incoming or outgoing call. The country code indicates the nation from which an SMS or call is initiated or received, depending on whether it is incoming or outgoing. For this study, we chose a working day (Monday) without any festivities within the two-month period that we have data from to perform classification and clustering on the map.