| Title |
Unsupervised Machine Learning Methods for City Vitality Index |
| ID_Doc |
42358 |
| Authors |
Dessureault, JS; Simard, J; Massicotte, D |
| Title |
Unsupervised Machine Learning Methods for City Vitality Index |
| Year |
2022 |
| Published |
|
| DOI |
10.1007/978-3-031-10464-0_15 |
| 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. The results are applied to the city of Trois-Rivieres. Each VI is defined using a magnitude of vitality and a cluster that can be used to compare districts. The consistency of the clusters are presented using a Silhouette index (SI). The results show the VI and a clustering membership for each district. Many tables and graphics display different analysis of the data, drawing the conclusion that this method can be a powerful insight for urbanists and inspire the redaction of a city plan in the smart city context. |
| Author Keywords |
Smart city; Intelligent urbanism; District vitality index; k-Mean Algorithm; Random forest algorithm; Genetic algorithm |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000889454000015 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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