## 5 Conclusions  
This study has demonstrated the feasibility and significance of using mobile phone data for the analysis of nocturnal urban noise patterns and the identification of distinct noise zones within a city. The integration of mobile phone technology, machine learning, and soundscape analysis has opened up new avenues for comprehensively understanding and managing urban noise pollution. Through the analysis of audio recordings collected from mobile phones placed across various urban areas during nighttime hours, it has been possible to capture the dynamic nature of urban soundscapes. The extracted noise features, combined with geographic location data, have facilitated the creation of detailed noise maps that reflect the varying intensities and sources of noise pollution. These noise maps provide a richer representation of the urban environment, enabling more informed decision making for urban planning and noise mitigation strategies. The application of clustering algorithms to the combined audio and location data allows us to delineate distinct noise zones within the city. These noise zones capture the heterogeneity of noise sources and their distribution, shedding light on areas with varying levels of noise pollution. This information is invaluable for urban planners, policy makers, and public health officials seeking to design targeted interventions that enhance quality of life for city residents. However, it is essential to acknowledge the challenges inherent in this approach. Ensuring the quality and accuracy of mobile phone-generated noise data remains a critical consideration. Privacy concerns and representativeness of the data are areas that require careful attention to ensure the credibility and validity of the findings. In essence, this research showcases the transformative potential of mobile phone data in the field of noise analysis and urban planning. By integrating this innovative approach into existing noise management strategies, cities can move towards more adaptive and data-driven policies, resulting in healthier and more livable urban environments. As cities continue to grow and evolve, the ability to harness technology to gain a deeper understanding of their sonic landscapes will play a pivotal role in shaping the future of urban living. This study provides a stepping stone towards that future, contributing to the body of knowledge that underpins sustainable and harmonious urban development. Figure 16: The map on the left shows the clusters identified by the algorithm as “nightlife”, and the map on the right shows the overlap between the clusters and nightlife establishments (represented by the pins). **Author Contributions:** Conceptualization, B.F. and L.B.E. ; methodology, L.B.E. ; software, M.N. ; validation, J.A.A. ; data curation, L.B.E. and M.N. ; writing--original draft preparation, B.F. and J.C.F. ; writing--review and editing, J.A.A. and J.C.F. ; supervision, J.C.F. and B.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Fundacao para a Ciencia e Tecnologia under Grant [UIDB/00315/2020]; and by the project \"BLOCKCHAIN.PT (RE-C05-i01.01--Agendas/Aliancas Mobilizadoras para a Reindustrializacao, Plano de Recuperacao e Resilencia de Portugal\" in its component 5--Capitalization and Business Innovation and with the Regulation of the Incentive System \"Agendas for Business Innovation\", approved by Ordinance No. 43-A/2022 of 19 January 2022). **Institutional Review Board Statement:** Not applicable. **Data Availability Statement:** Restrictions apply to the availability of these data. Data was obtained from [third party] and are available [Lisbon Municipality/ at [[https://lisboaaberta.cm-lisboa.pt/index.php/pt/lx-data-lab/desafios](https://lisboaaberta.cm-lisboa.pt/index.php/pt/lx-data-lab/desafios)]([https://lisboaaberta.cm-lisboa.pt/index.php/pt/lx-data-lab/desafios](https://lisboaaberta.cm-lisboa.pt/index.php/pt/lx-data-lab/desafios))] with the permission of Lisbon Municipality.