## 4 Results and Visualization  
The calculations have been performed while using the statistical software for data science STATA ([[https://www.stata.com/](https://www.stata.com/)]([https://www.stata.com/](https://www.stata.com/))). Variables (KPIs) for each cluster, scenario and category are created while using the methodology described in Appendix A. Missing data are completed, as referred to in Section 3.4 and KPIs are then normalized, as indicated in Section 3.5 while using the function _median_. In order to calculate the composite indicator in the equal weight scenario, KPIs in each category are first averaged using the function _rowmean_. Table 5 shows the values that were obtained for each category. Subsequently, the composite indicator is achieved by applying the weights as indicated in Table 2, which boils down to the following expression:  
\[\text{Indicator}=0.2*\text{Accessibility}+0.2*\text{Retainability}+0.2*\text{ Integrity}+0.2*\text{Mobility}+0.2*\text{Quality}. \tag{1}\]  
In the case of the CS and PS scenarios, different weights are applied in order to highlight voice or data services, respectively. According to Table 3, the expression for the indicator in the CS scenario is given by:  
\[\text{Indicator}=0.25*\text{SSR CS}+0.25*\text{DROP R99}+0.25*\text{DROP CS}+0.25*\text{INTER}-\text{RAT HO}, \tag{2}\]  
while in the PS scenario, it is given by:  
\[\text{Indicator}=0.2*\text{SSR PS}+0.2*\text{DROP HSPA}+0.2*\text{ THROUGHPUT}+0.2*\text{INTRA}-\text{HO}+0.2*\text{INTER}-\text{HO}. \tag{3}\]  
Table 6 shows the composite indicator obtained in each cluster according to the different scenarios. The composite indicator of cluster 1 obtains the largest value in two out of the three scenarios considered (equal weight and PS), while cluster 5 obtains the smallest value in the three scenarios. Cluster 3 obtains the second smallest value in the equal weight and PS scenarios. Cluster 2 obtains the largest value in the case of the CS scenario. One of the advantages of composite indicators is the easiness of visualization through the ranking of the results. In the following, different visualization strategies are used in order to extract further insight into the results shown in Table 6.  
First, composite indicators values are depicted in Figure 2 while using a line plot where the clusters have been arranged according to the ranking that was obtained in the \begin{table}
\begin{tabular}{l c c} \hline \hline
**Category** & **Value** \\ \hline Accessibility & 0.9972 \\ Retainability & 1.7843 \\ Integrity & 0.9999 \\ Mobility & 0.9832 \\ Quality & 1.0151 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Composite indicator per cluster and scenario. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Cluster** & **Equal Weight** & **CS** & **PS** \\ \hline
1. urban macro & 1.2192 & 1.4979 & 1.2105 \\
2. suburban & 1.1556 & 1.5139 & 1.1002 \\
3. urban micro & 1.1007 & 1.2914 & 1.0862 \\
4. rural macro & 1.1302 & 1.2886 & 1.1559 \\
5. rural micro & 1.0783 & 1.2782 & 1.0539 \\ Average & 1.1559 & 1.3931 & 1.1448 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Values per category. equal weight scenario. This type of plot allows checking the ranking and possible shifts of positions according to the composite indicator, but it also allows for observing the distances between clusters and between composite indicators when several indicators are built (as it is this case). Figure 2 shows the robustness of the different clusters versus the considered scenarios. In this way, ranking positions do not significantly change for each scenario. It is clear that a cluster ranking can be established, helping MNO in making decisions for each cluster in order to optimize a given cluster. Note that, not only the position, but also the distance between clusters, should be evaluated here. A similar plot can be obtained arranging the clusters in the CS or PS scenarios, respectively.