# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 1 Introduction  
Rapid urbanization and the increasing complexity of city management have brought smart cities to the forefront of urban planning discussions. However, despite the growing attention to smart city rankings, there is still a limited understanding of what makes a city truly'smart'. Existing methodologies for comparing cities often rely on aggregate indices or subjective assessments, which fail to capture the nuanced contributions of various factors like governance, infrastructure, and mobility. This lack of clarity leaves policymakers with incomplete guidance on which areas to prioritize for smart city development. This study addresses this gap by introducing a dual-analysis framework that leverages unsupervised and supervised learning models to both cluster cities and assess the importance of various features contributing to their smartness. By focusing on data-driven insights, we aim to provide a more precise and actionable understanding of urban intelligence, enabling policymakers to make informed decisions about where to allocate resources and which urban dimensions to prioritize. This focused approach provides a clearer roadmap for improving city smartness and ensuring sustainable urban growth. Smart cities have emerged as a pivotal paradigm in contemporary urban policy, driven by the rapid urbanization projected by the United Nations Department of Economic and Social Affairs. By 2050, it is estimated that two-thirds of the global population will reside in urban areas, necessitating innovative urban planning and management approaches. Smart cities leverage technology and data to enhance the quality of life, improve the efficiency of urban services, and promote sustainability. The study of smart cities greatly benefits from the application of artificial intelligence (AI) and machine learning (ML) models, which are essential for understanding urban environments' complex and multifaceted nature. Unsupervised learning models, such as clustering algorithms, allow us to identify inherent patterns and groupings within vast datasets, revealing hidden structures and relationships among cities. The United Nations Department of Economic and Social Affairs ([[https://www.un.org/development/desa/pdf/](https://www.un.org/development/desa/pdf/)]([https://www.un.org/development/desa/pdf/](https://www.un.org/development/desa/pdf/)), accessed on 1 September 2024) predicts that by 2050 at least two persons out of three will be living in urban areas. Figure 1 shows the past and future percentages of the urban population. The plot highlights the increasing trend in the world's more- and less-developed regions. The barplot was created using data from United Nations, Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects: The 2018 Revision (ST/ESA/SERA/420). New York: United Nations ([[https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf](https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf)]([https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf](https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf)) accessed on 1 September 2024). While presently there are 31 megacities (i.e., cities with more than 10 million inhabitants), their number will grow to about 43 by 2030. The major city growth rate is predicted to be in developing countries (population.un.org/wup/Maps/ accessed on 1 September 2024). Urbanization (i.e., the socio-economic process of environmental and social mutation from rural to urban) is the result of many factors, among which spatial and urban planning, investments in building and infrastructure, and economy. The increase in urban population will be driven by several factors, including natural population growth, rural-to-urban migration, and the geographic expansion of urban areas through annexation and conversion of rural localities into urban settlements.