# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 2 Related Work
The authors highlighted the importance of aligning technology providers and policymakers with citizen needs, emphasizing ICT-enabled services. The paper's originality lies in its introduction of a practical toolkit for querying user perceptions and expectations, helping policymakers and businesses respond effectively to citizens' needs, thus enhancing the relevance and usability of smart city services. Researchers have leveraged different techniques in their studies, including clustering methods, to evaluate what makes a city \"smart\" and therefore consider and rank different available cities, commenting on their results. The analyses are conducted at various levels, including national, continental, and global scales. [PERSON] et al. [4] reviewed the current smart city software applications landscape, analyzing multiple case studies aimed at enhancing sustainability and resilience and classifying their benefits into six possible action areas: governance, economy, environment, people, mobility, and living. The authors suggested that implementing smart city software applications and integrating them across various domains are crucial for tackling all the challenges that might become apparent in the future. [PERSON] et al. [5] examined the key factors that affect a smart city's transformation readiness, suggesting that both ICT and non-ICT factors influence the outcome. The authors performed a statistical investigation on the Australian region, performing multiple regression analyses on features that affect urban smartness levels. The conclusion of the work is that 65% of smart city transformation readiness is influenced by (i) proximity to the domestic airport, (ii) low level of remoteness, (iii) high population density, (iv) low unemployment rate, and (v) high labor productivity. [PERSON] [6] observed that current city evaluation research faces challenges such as unclear goals, inadequate theoretical foundations, redundant indices, poor comparability, lack of consideration for urban diversity, excessive focus on rankings, and insufficient validation and feedback. Additionally, there is a need for a more systematic interpretation of evaluation systems' composition, principles, and uncertainties. [PERSON] et al. [7] explored possible smart city objectives and actionable future practices. The main smart city capacities are identified as (i) technology exploitation, (ii) innovation, (iii) collaboration, and (iv) ecosystem orchestration. Artificial intelligence (AI) and particularly ML play a vital role in determining the importance of various features that contribute to a city's smartness [8]. By analyzing these features, it is possible to identify the key drivers of urban performance and prioritize areas for improvement. This insight is indispensable for policymakers and urban planners aiming to enhance the quality of urban life. The integration of both supervised and unsupervised learning models is paramount. Unsupervised learning uncovers the underlying patterns without prior labels, offering a broad understanding of urban similarities and differences. Supervised learning, with its focus on feature importance, provides detailed and actionable insights. Excluding one in favor of the other would result in an incomplete analysis, missing either the holistic view provided by unsupervised methods or the specific, actionable insights from supervised techniques. Clustering plays a crucial role in the development and management of smart cities, as it involves grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Cities can be clustered in multiple ways, as explored by recent literature. The analyzed papers focus on clustering and then ranking of cities but often lack explainability on what are thought to be meaningful parameters and why. The following paragraphs show what work has been performed so far in the scope of smart city clustering. [PERSON] et al. [9] studied the advancements of the implementation of smart cities in the Visegrad Group (V4).