# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 2 Related Work
Their emphasis is on cataloging these criteria rather than deploying statistical models to analyze them, which is the core focus of our work. On the other hand, [PERSON]'s article moves in a somewhat different direction, integrating content analysis and the analytic hierarchy process (AHP) to rank indicators for sustainable smart cities. This ranking system is meant to assist policymakers by prioritizing aspects of smart city development. [PERSON]'s work focuses on practical decision-making tools for urban planners, positioning technology as one of several factors rather than the dominant driver, which contrasts with your data-driven focus on feature importance and clustering. Finally, it may be beneficial to compare the present article against a work by [PERSON] et al. [16]. Both focus on the use of advanced technologies to improve urban environments through smart city concepts, but they approach the subject from different perspectives and methodologies. Our paper is primarily concerned with understanding how cities can be grouped or clustered based on their performance in various dimensions of smartness, such as smart mobility, smart governance, smart living, and more. The main contribution is the application of clustering algorithms, such as k-means and Gaussian mixture models, to identify patterns in how cities perform across these dimensions. The focus is also on determining the feature importance of these dimensions, identifying which aspects most significantly impact a city's classification. The goal of this study is to guide policymakers in improving their cities by focusing on the dimensions that most affect urban intelligence. The paper emphasizes machine learning techniques, especially clustering, and feature importance analysis, which provides insights into which areas cities should prioritize for becoming smarter. In contrast, the paper by [PERSON] et al. provides a technical framework for integrating heterogeneous data and managing complex urban systems. This article introduces a novel architecture that enables the seamless deployment and integration of various subsystems within smart cities, leveraging technologies such as digital twins (DT), cloud computing, and data lakes. Its main contribution lies in creating an IT infrastructure that allows urban data to be collected, stored, integrated, and visualized efficiently. This architecture also supports the deployment of real-time applications and workflows that enhance the functionality of urban intelligence systems. The paper's key focus is on the technical execution of smart city systems, ensuring they can manage and process large, heterogeneous datasets effectively, while also providing a user-friendly interface for city administrators to make data-driven decisions. As evidenced by the papers presented in this section, the literature includes various studies that employ clustering techniques in the context of smart cities. However, in most cases, a single method is utilized and applied to a specific aspect of smart cities. The emergence of issues such as unclear goals, redundant indices, and a lack of comprehensive methodological applications highlights the need for more systematic interpretation of evaluation systems and a more integrated approach to employing IT solutions in smart city development. It is also important to integrate smart city software applications across various domains. Effective implementation and coordination of these technologies are crucial for addressing future urban challenges and improving city management. A holistic approach, incorporating diverse methodologies and comprehensive analysis, is essential for advancing smart city development.