## 5 Conclusions and Future Research Lines
For instance, the existence of big, isolated areas and the alpine setting of the province of Trento affects RBS coverage and the precision of the results because one base station could be serving several grids in the forest area. The opposite situation can occur in more populated areas, so the results can also be affected if several RBS are serving the same cell grid. Despite these limitations, the results demonstrate that labeling small, representative subsets can significantly enhance the classification accuracy. It would be an interesting future research direction to see how other datasets with different or more complex landscapes would perform with a supervised approach, especially with RF, which achieved the best results for our study. In conclusion, the results obtained in this study demonstrate that the proposed supervised approach is sufficiently effective enough to warrant its application in population analysis tasks traditionally handled with unsupervised clustering techniques. This is particularly relevant when working with large volumes of unlabeled data, such as CDRs. Our findings highlight that labeling a small, representative subset of the data can yield significant benefits by facilitating the use of supervised learning methods, leading to more accurate and reliable outcomes. This approach represents a valuable alternative to address complex analytical challenges in mobility and population studies. These promising results encourage a new research line on meaningful location identification with the help of a supervised approach leveraging knowledge extracted from mobile phone mobility data. For instance, using more complex algorithms like multi-label classification will allow us to make more precise predictions by enabling us to identify one cell as having multiple labels (e.g., home and forest or home and work). Another interesting future study direction could rely on constructing a more tailored neural network or using other advanced deep learning techniques to explore how they would perform with CDR data. Further improvements could come from classifier optimization techniques, such as those described in [39], like setting local k values in multi-label kNN (MLkNN). Another potential optimization could come from aiming at inferring or learning weights to apply them to the distance measures in order to strengthen their robustness. The use of multi-label ensemble methods also represents a promising direction for future research. Conceptualization, M.M.-H. and D.O.-B. ; methodology, M.M.-H. and D.O.-B. ; software, M.M.-H., D.O.-B. and G.C.-G.; validation, M.M.-H., D.O.-B. and G.C.-G.; formal analysis, M.M.-H. and D.O.-B. ; investigation, M.M.-H. and D.O.-B. ; resources, M.M.-H., D.O.-B. and G.C.-G.; data curation, M.M.-H. and D.O.-B. ; writing--original draft preparation, M.M.-H. and D.O.-B. ; writing--review and editing, M.M.-H., D.O.-B. and G.C.-G.; visualization, M.M.-H., D.O.-B. and G.C.-G.; supervision, [PERSON]-H., D.O.-B. and G.C.-G.; project administration, M.M.-H., D.O.-B. and G.C.-G.; funding acquisition, D.O.-B. and G.C.-G. All authors have read and agreed to the published version of the manuscript. This work was supported by the Spanish Ministry of Science and Innovation by grant number PID2022-141869 NB-I00 and the University of Cordoba (FPU-UCO-2020) for [PERSON]. [PERSON]. Not applicable  
The data that support the findings of this study are available from BigDataChallenge contest at [[https://dataverse.harvard.edu/dataverse/bigdatachallenge](https://dataverse.harvard.edu/dataverse/bigdatachallenge)]([https://dataverse.harvard.edu/dataverse/bigdatachallenge](https://dataverse.harvard.edu/dataverse/bigdatachallenge)) (accessed on 8 February 2025). Restrictions apply to the availability of these data, which were used under license for this study.