# Unsupervised Machine learning methods for city vitality index

arXiv: https://arxiv.org/abs/2012.12082 · HTML: https://mlsanity.com/paper/2012.12082/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Feature selection or extraction decision process for clustering using PCA and FRSD](https://mlsanity.com/paper/2111.10492/) (2021)
- [District Vitality Index Using Machine Learning Methods for Urban Planners](https://mlsanity.com/paper/2503.01878/) (2025)

## Abstract (arXiv)

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.