Clustering of Cities Based on Their Smart Performances: A Comparative Approach of Fuzzy C-Means, K-Means, and K-Medoids

Smart City is recognized as a potential approach to address serious urban issues such as traffic, pollution, energy use, and waste management. Therefore, it is vital to evaluate how smart cities are in order to put these methods into practice. To offer advice on these matters, numerous reports are created, and one of which is Smart City Index (SCI). The Institute for Management Development (IMD) and the Singapore University of Technology and Design collaborate on SCI every year (SUTD). The report evaluates how locals view the buildings and technological applications that are available in their towns. Although the study offers a thorough examination of the cities for evaluation, the city clusters should be more sensitive and not be created using strict clustering techniques. In order to address this problem, the clustering algorithms namely K-Medoids, Fuzzy C-Means, and K-Means, which outperform hard clustering approaches in terms of robustness to vagueness and knowledge retention, are used. The main goal of this study is to categorize cities using a scientific manner (clustering algorithms) based on SCI data and to present how the chosen approaches work for dealing with the associated problems. The primary innovation of the present study is the use of clustering techniques in reports where the indexes are used. The results indicate that grouping the cities on the basis of their smart indicators would not be as effective as using the three clustering techniques that are suggested in this paper. These results add to the analysis of the dynamic capacities of smart cities and highlight the sustainability of these tactics.

Automated summary

Smart cities are seen as a potential solution to urban problems, and it is important to evaluate their effectiveness. This study uses clustering algorithms to categorize smart cities, and the results suggest that this method is more effective than grouping cities based on indicators.