# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 1 Introduction
The Smart City Strategy Index (SCSI, www.rolandberger.com/en/Insights/Publications/Smart-City-Strategy-Index-Vienna-and-London-leading-in-worldwide-ranking.html accessed on 1 September 2024) evaluates the extent and ambition of urban centers based on the essential components of a Smart City since 2017. The index considers 12 criteria (i.e., categories, with a total of 31 subcategories) that are thought to be representative of the development of a successful smart city strategy: budget, buildings, energy and environment, mobility, education, health, government, infrastructure, policy and legal framework, stakeholders, coordination, and plan. Each category has a weight depending on its overall importance, and a final weighted score is calculated. The index attributes a high importance to infrastructure and policy and legal framework as indicators of smartness. The Smart EcoCity Index (SEC, www.smartecocity.com/smartecocity-index/ accessed on 1 September 2024) assesses three dimensions of a smart city: equity (i.e., the city's quality of life and social inclusion), environment (i.e., the city's sustainable practices and reduction of environmental impact), and economy (i.e., involvement of citizens, governance, innovation and resilience plans). The index considers eight categories of factors for accessing the final score: (i) transport and mobility, (ii) sustainability, (iii) governance, (iv) innovation economy, (v) digitalization, (vi) cybersecurity, (vii) living standards, and (viii) expert perception. Each accounts differently in the computation of the final score. Transport and mobility are thought to play the most prominent role in determining the smartness of a city. The majority of the indexes discussed so far consider sustainability, smartness, urbanization, and quality of life as key factors to characterize smart cities. Table 1 reports the categories for each indicator and their weights. The IMD Smart City Index (SCI) provides a comprehensive set of variables that reflect different aspects of a city's smartness, including smart living, smart mobility, smart governance, smart economy, smart people, and smart environment. These dimensions form the core of the data used in the analysis. The focus then moves into the unsupervised learning phase, where clustering techniques such as k-means, hierarchical clustering, Gaussian mixture models (GMMs), and self-organizing maps (SOMs) are employed. These methods are used to categorize cities into clusters based on their performance across these smart city dimensions. This step helps identify similarities and patterns among cities, grouping them into categories that exhibit common traits in their smartness profiles. Once the clusters are identified, the research shifts focus to determining the relative importance of the features contributing to a city's classification. At this stage, supervised learning models like random forest, support vector machines (SVMs), and gradient boosting are used. These models are trained on the data to assess which dimensions of smartness--such as smart mobility or smart governance--have the greatest impact on a city's overall ranking. This analysis reveals which aspects are the most critical in determining whether a city is classified as highly smart or not. The results from these models highlight that some dimensions, like smart living, consistently play a significant role in driving a city's smartness score, offering key insights for policymakers. The flow of the research thus moves from categorization through \begin{table}
\begin{tabular}{l l c} \hline \hline
**Indicator** & **Category** &
\begin{tabular}{c} **Weight** \\ **(If Available)** \\ \end{tabular} \\ \hline SEC & Transport and Mobility & 22.5\% \\  & Sustainability & 12.5\% \\  & Governance & 15.0\% \\  & Innovation Economy & 5.0\% \\  & Digitalisation & 17.5\% \\  & Cyber Security & 7.5\% \\  & Living Standard & 10.0\% \\  & Expert Perception & 10.0\% \\ \hline \hline \end{tabular}
\end{table}