Unsupervised Machine learning methods for city vitality index

TL;DR

This paper introduces an unsupervised machine learning approach combining k-means clustering, genetic algorithms, and linear regression to evaluate and predict city district vitality indices, aiding urban planning.

Contribution

It presents a novel integrated method for assessing and forecasting city vitality using unsupervised learning and optimization techniques.

Findings

Effective clustering of districts based on vitality features

Accurate prediction of future vitality indices

Insights into feature importance for urban vitality

Abstract

This paper concerns the challenge to evaluate and predict a district vitality index (VI) over the years. There is no standard method to do it, and it is even more complicated to do it retroactively in the last decades. Although, it is essential to evaluate and learn features of the past to predict a VI in the future. This paper proposes a method to evaluate such a VI, based on a k-mean clustering algorithm. The meta parameters of this unsupervised machine learning technique are optimized by a genetic algorithm method. Based on the resulting clusters and VI, a linear regression is applied to predict the VI of each district of a city. The weights of each feature used in the clustering are calculated using a random forest regressor algorithm. This method can be a powerful insight for urbanists and inspire the redaction of a city plan in the smart city context.