A Global Classification Model for Cities using ML (Papers Track)
Doron Hazan (MIT); Mohamed Habashy (Massachusetts Institute of Technology); Mohanned ElKholy (Massachusetts Institute of Technology); Omer Mousa (American University in Cairo); Norhan M Bayomi (MIT Environmental Solutions Initiative); Matias Williams (Massachusetts Institute of Technology); John Fernandez (Massachusetts Institute of Technology)
This paper develops a novel data set for three key resources use; namely, food, water, and energy, for 9000 cities globally. The data set is then utilized to develop a clustering approach as a starting point towards a global classification model. This novel clustering approach aims to contribute to developing an inclusive view of resource efficiency for all urban centers globally. The proposed clustering algorithm is comprised of three steps: first, outlier detection to address specific city characteristics, then a Variational Autoencoder (VAE), and finally, Agglomerative Clustering (AC) to improve the classification results. Our results show that this approach is more robust and yields better results in creating delimited clusters with high Calinski-Harabasz Index scores and Silhouette Coefficient than other baseline clustering methods.