# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## Experiments
### Gaussian Mixture Models  
As per Table 6, the clustering performance results for the Gaussian mixture model reveal several insights. The silhouette score of 0.4010 suggests that the clusters are reasonably well-defined, although there may still be some overlap between them, while the result points to moderate clustering quality, there is room for improvement in terms of distinct separation.  
The Davies-Bouldin index, with a value of 0.7514, indicates that the clusters are fairly distinct from each other.  
The Calinski-Harabasz index is 87.15, which indicates that the model has created clusters with reasonable separation, but as with the silhouette score, there could be further improvement. Overall, these metrics together suggest that the model is performing moderately well, but optimizing the clustering approach or tuning parameters may enhance the results.  
Cluster Analysis  
In the GMM clustering plot of Figure 5 and Table 7, the cities in Cluster 0 are primarily from Europe and North America, including Oslo, Amsterdam, Boston, Zurich, and Paris. These cities are known for their strong infrastructure, high quality of life, and leadership in smart city initiatives, particularly in sustainability, public services, and governance. Cluster 1 features cities like San Francisco, Milan, Brussels, Dublin, and Rome, which are prominent cultural and economic centers. These cities face the challenge of balancing historical preservation with modern smart city technologies, focusing on tourism, cultural heritage, and enhancing urban living standards. Cluster 2 contains large global metropolitan areas such as Los Angeles, New York, Berlin, Tokyo, and Sydney. These cities are major financial hubs and urban megacities with diverse populations. They are investing heavily in smart infrastructure and technology to address challenges like urban density and sustainability. Lastly, Cluster 3 includes cities from rapidly developing regions like Abu Dhabi, Dubai, Hong Kong, Moscow, and Shanghai. These cities are characterized by rapid urbanization, large-scale investments in modern infrastructure, and technology-driven economic growth. Each cluster reflects cities with similar levels of urbanization, technological advancement, and governance, showcasing their approaches to smart city development.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metric** & **Value** \\ \hline Silhouette Score & 0.4010 \\ Davies–Bouldin Index & 0.7514 \\ Calinski–Harabasz Index & 87.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Clustering performance metrics for the GMM model.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Cluster** & **Smart Cities** \\ \hline Cluster 0 & Oslo, Bergen, Amsterdam, Copenhagen, Stockholm, Montreal, Vienna, Odense, Singapore, Boston, Zurich, Trondheim, Vasteras, Aalborg, Ottawa, Washington, DC, Stavanger, Helsinki, Vancouver, Toronto, \\ Düsseldorf, Reykjavik, Lyon, Helsingborg, Munich, Hamburg, Geneva, \\ Luxembourg,Aarhus, Turku, Espoo, Paris, Hannover, Strasbourg, Joensuu, \\ Frankfurt am Main, Oulu, Cologne, Bochum, Stuttgart, \\ Häemenelnina, Tampere, Jyvaskyla, Vantaa, Bordeaux, Nantes, Lahti, Nice, \\ Calgary, Edmonton \\ Cluster 1 & San Francisco, Milan, Brussels, Marseille, Leeds, Dublin, Verona, Tallinn, \\ Catania, Bayreuth, Turin, Florence, Birmingham, Bari, Bratislava, Ljubljana, \\ Perugia, Tel Aviv, Barcelona, Ancona, Lisbon, \\ Rome, Genova, Naples, Riga \\ \hline \hline \end{tabular}
\end{table}
Table 7: GMM clustering of smart cities.