## 4 Results and Visualization
A second type of line plot can be obtained by focusing on the rankings for each scenario, as shown in Figure 3. In addition to the three scenarios that were considered in this work, we have tailored the Hotspot score [16] in order to include it as a baseline in the cluster rankings comparison. The Hotspot score is a traditional indicator, which indicates how problematic a given sector (cluster in our case) is. Basically, it is calculated by comparing the KPI value with the thresholds established by the vendors [35]. The higher the score, the more problematic the cluster. Hotspot score results are not separately presented for each service (CS or PS), since we found that the ranking of the clusters remains unaltered after changing the weights. Thus, this score is not sensitive to the specific service, confirming the lack of flexibility that traditional metrics exhibit. Figure 3 shows that, as stated above, cluster ranking in the composite indicator case remains similar for the three scenarios under consideration. This type of plot is useful when a composite indicator is evaluated in different time instants, so the relative position of the clusters can be determined, as well as possible variations. In addition, it is also possible to observe whether this position remains over a given time horizon. Figure 3 exemplifies one key aspect of composite indicators, which is that a 'bad' value of a cluster in a given scenario can be 'disguised' in other scenarios. That is the case of cluster 2 in the figure, which exhibits a different position in the ranking for each evaluated scenario, ranging from being the worst in the CS case to being the best in the Hotspot. Figure 2: Composite indicators according to clusters. Spider charts are powerful visualization tools in the framework of composite indicators. This tool allows for evaluating the distance of the different composite indicators with respect to an indicator that is built with normalized values. In order to obtain this plot, we can normalize by the average indicator or by the total composite indicator, and evaluate the relative distances that are obtained by each cluster. In a first approach, a normalization by the average indicator is performed by dividing the indicators obtained per cluster by their average indicator (see the values in Table 6), and multiplying by 100. For example, in the case of cluster 1, the normalized value in the equal weight scenario is obtained as \((1.2192/1.1559)\times 100\). Following this normalization, the average indicator is equal to 100 after normalization. Table 7 shows the numerical results of this normalization, while Figure 4 plots the corresponding spider chart, clearly indicating the line that is associated to the average indicator. Figure 4 shows that cluster 1 remains far from the average for the three scenarios, although cluster 2 is the one that achieves the largest value in the CS scenario, as seen before. As an example of use of this plot, let us assume a MNO that, in view of these results, decides not to invest in cluster 1 given the good results, shifting to the objective of achieving a network as homogeneous as possible in terms of the composite indicator. However, this result can also confirm the mobile network strategy if optimizing cluster 1 was its objective in the first place. This would be the case for a MNO operating a large city with a large share of the market. In this scenario, it might be of interest to continue investing in cluster 1 in order to keep its dominant position. Finally, it is also possible to obtain a spider chart, taking, as a reference, a given cluster. As an example, we will obtain the plot assuming that cluster 1 is the reference area, but the procedure is the same for the remaining clusters. By using cluster 1 as a reference, the plot indicates the effort that the remaining clusters need to do in order to reach this cluster's performance.