# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 6 Conclusions and Future Work  
In this study, we have analyzed the smartness of cities using both unsupervised and supervised learning models. Applying unsupervised learning techniques such as k-means clustering, hierarchical clustering, SOM, and GMM provided valuable insights into the natural groupings and similarities among the cities based on their smart city indices. These clustering methods revealed intrinsic patterns and allowed for a comparative analysis of the cities, highlighting the distinct characteristics of each cluster.  
Furthermore, the use of an SVM model enabled us to determine the importance of various features contributing to a city's smartness. The integration of these methodologies offers a robust framework for understanding what makes a city'smart' and provides actionable insights for policymakers and urban planners to enhance urban living standards.  
This research opens several avenues for future work. Firstly, expanding the dataset to include more recent data and additional features can provide a more comprehensive analysis and capture the dynamic nature of smart city development. Incorporating real-time data streams from IoT devices and social media could further enhance the granularity and accuracy of the analysis.  
Secondly, exploring advanced ML techniques such as deep learning and reinforcement learning could offer new perspectives and improve the performance of clustering and feature importance analysis. Techniques like graph-based clustering and network analysis could be particularly useful in understanding the complex interdependencies and relationships between different aspects of smart cities.  
Thirdly, a deeper investigation into the impact of socio-economic and cultural factors on the smartness of cities can provide a more holistic view. This includes examining how these factors influence residents' perceptions and how they can be integrated into the existing indices to create a more inclusive and representative measure of smart city performance.  
Lastly, developing interactive and user-friendly visualization tools can aid in the dissemination of findings to a broader audience, including policymakers, urban planners, and the general public. These tools can help stakeholders better understand the data and make informed decisions to drive the smart city agenda forward.  
Conceptualization, E.B.; methodology, E.B.; software, E.B. and A.G.; validation, A.G.; formal analysis, E.B.; investigation, A.G.; resources, E.B.; writing--original draft preparation, E.B.; writing--review and editing, A.G.; supervision, E.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data and software are available at [[https://github.com/EBarbierato/smart_cities_clust_feat_imp](https://github.com/EBarbierato/smart_cities_clust_feat_imp)]([https://github.com/EBarbierato/smart_cities_clust_feat_imp](https://github.com/EBarbierato/smart_cities_clust_feat_imp)), accessed on 1 September 2024.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MDPI & Multidisciplinary Digital Publishing Institute \\ DOAJ & Directory of open access journals \\ TLA & Three letter acronym \\ LD & Linear dichroism \\ \end{tabular}