## 5 Conclusions  
A methodology for building composite indicators is presented in this paper as a flexible tool for mobile communications network management. Key performance indicators are used as input data in order to build the composite indicators, covering the quality dimensions of relevance, interpretability, coherence, timeliness, accessibility, credibility, and accuracy. A Universal Mobile Telecommunications Systems network is considered as a use case, and a total of eleven key performance indicators are selected from the categories \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Cluster** & **Equal Weight** & **CS** & **PS** \\ \hline
1. urban macro & 105.47 & 107.52 & 105.74 \\
2. suburban & 99.97 & 108.67 & 96.11 \\
3. urban micro & 95.22 & 92.70 & 94.88 \\
4. rural macro & 97.77 & 92.49 & 100.97 \\
5. rural micro & 93.29 & 91.75 & 92.06 \\ Average & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Composite indicator using reference to average indicator per cluster. Average indicator = 100.00. Figure 4: Composite indicator in the three scenarios taking as a reference the average indicator. Average indicator = 100.00. Figure 5: Composite indicator in the three scenarios taking as a reference cluster 1. Cluster 1 = 100.00.  
of accessibility, retainability, integrity, mobility, and quality. From this set, three different aggregation methods are applied, an equal weight method, a public opinion method that is based on voice service users, and a public opinion method based on data service users. The aim of the use case is to build a composite indicator that could help in assessing the fairness of a mobile communications network when considering different clusters of cells. More precisely, urban macro, suburban, urban micro, rural macro, and rural micro environments are generated. The composite indicators that are obtained in the three aggregation methods are ranked for the five clusters of cells, following different strategies. In addition, visualization tools that are based on spider charts allow for comparing the results normalizing with respect to the average performance, or with respect to a given cluster, enabling the mobile network operator to identify which clusters of cells require a larger investment. The proposed methodology is able to extract useful insights and it can be extended in order to cover standards, such as LTE, by changing the basic set of key performance indicators. In fact, mobile network operators should decide which key performance indicators to include, according to their business model. As future work, the time dimension will be included in the methodology. Conceptualization, C.B.-M.; Data curation, J.S. ; Investigation, C.B.-M.; Methodology, C.B.-M. and J.S. ; Software, J.S. ; Visualization, J.P. and S.R. ; Writing--original draft, C.B.-M. and J.P.; Writing--review & editing, J.P. and S.R. All authors have read and agreed to the published version of the manuscript. [PERSON] would like to thank the Generalitat Valenciana (AICO/2020/154 Project). [PERSON] would like to thank the Spanish Ministry of Science, Innovation and Universities (RYC-2017-22101 Grant), and the Generalitat Valenciana (GV/2020/046 Project). Statistics to replicate the data are available in the paper. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript: