# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 2 Related Work
The cities were divided into clusters for the final assessment and classification using a dendrogram of similarity. The study evidenced that the best smart city scores are obtained by cities that are western and thus nearer to the European Union, implying that historical, socio-economic, political, and possibly geographic factors might influence a city's smartness. [PERSON] et al. [10] clustered 40 European smart cities depending on the activities implemented by the cities, outputting three clusters: (i) cities with emerging smart strategies, (ii) technology-oriented cities, and (iii) quality-life oriented smart cities. The clustering was performed for sustainability evaluation and ranking purposes. Clusters were obtained via principal component analysis (PCA). [PERSON] et al. [11] clustered and ranked European cities through the comparison of the number of existing projects by weighted category, considering population density. Four main categories were created via PCA: (i) infrastructure and transport, (ii) sectorial initiatives, (iii) territorial competencies, and (iv) community. PCA was applied to the projects to find the relationships between categories. [PERSON] et al. [12] assessed which Italian provincial municipalities aligned with the smart sustainable city (SSC) paradigm using the [PERSON]'s distance method (DP2). Then, the authors applied a k-means variant with five fixed centroids and discovered that clusters of SSCs form at a macro-geographical level. Northern municipalities belonged to the cluster containing cities that identified the most with the smart sustainable city model. [PERSON] et al. [13] clustered and ranked smart cities using the fuzzy c-means partitioning technique. The cities were evaluated first via partitioning them into low-, medium-, and high-performing cities and then via a comparative analysis considering a three-criteria decision model (based on sustainability, resilience, and livability) where the analytical hierarchy process (AHP) is combined with the Evaluation based on distance from average solution (EDAS) approach under a spherical fuzzy environment. Recent studies in smart city rankings and feature importance analyses have explored various methodologies to understand better the dimensions that contribute most significantly to a city's \"smartness\". One study by [PERSON] [14] combined content analysis and the analytic hierarchy process (AHP) to create a robust ranking system for sustainable smart city (SSC) indicators. This study emphasized the importance of governance, livability, and public services, while interestingly downplaying the role of technology and ICT, challenging the widespread assumption that these are the most crucial factors. This study's combined ranking system serves as a decision-making tool, helping prioritize smart city initiatives based on available resources and strategic goals. Another study by [PERSON] et al. [15] took a systematic literature review approach to identify the key criteria for smart city identification and ranking. Their work highlighted the use of ML techniques, such as dynamic factor analysis, to better understand how cities can be evaluated based on their innovation capacity, infrastructure, and social inclusion. Our approach consists of a data-centric and ML-driven approach to analyze the IMD Smart City Index dataset. The focus shifts to unsupervised and supervised learning models, specifically clustering and feature importance, to categorize cities based on \"smart\" features and determine which factors are most influential in urban development. This detailed, technical exploration contrasts with the broader literature review and ranking approaches discussed in the other two works. In particular, [PERSON] et al. adopted a more qualitative and comprehensive review of existing literature on smart city criteria. Their research identified 48 distinct metrics used across various studies to define and assess smart cities, categorizing these metrics into technology, living conditions, and environmental sustainability.