Analysis of Smart and Sustainable Cities through K-Means Clustering

Abstract

With rampant urbanization and industrialization, the quest to become a Smart City is in vogue. Elaborate designs, ideas, and solutions to existing problems are being devised. However, in this race, city planners often neglect the most critical factor related to one’s existence on the Earth, i.e., Sustainability. Designing smart and sustainable cities is a challenge whose solutions are not easy. Also, developing a proposed smart city is a time-consuming process as it involves its foundations, analyzing the needs, latest technology, infrastructure, and topography. This paper aims to analyze the patterns followed by cities worldwide in the quest to make smart cities. This shall also show how newer cities can become innovative based on the work already done by the existing cities through the concept of Inheritance. In order to do this, a dataset comprising 102 cities is analyzed based on six metrics. A combination of the Principal Component Analysis and K-means Clustering technique is used to assist with the analysis of this unlabeled dataset. It is thereby shown that these 102 cities can be identified as belonging to 4 clusters and the cluster properties can then be inherited by any new city poised to become a future smart city.