论文标题
城市生命力指数的无监督机器学习方法
Unsupervised Machine learning methods for city vitality index
论文作者
论文摘要
本文涉及多年来评估和预测地区活力指数（VI）的挑战。没有标准的方法可以做到这一点，在过去的几十年中追溯进行更复杂的方法。虽然，必须评估和学习过去的特征以预测将来的VI。本文提出了一种基于K均值聚类算法评估这种VI的方法。这种无监督的机器学习技术的元参数通过遗传算法方法进行了优化。基于最终的群集和VI，应用线性回归来预测城市每个地区的VI。使用随机的森林回归算法计算聚类中使用的每个特征的权重。这种方法可以成为城市主义者的有力见解，并激发在智能城市背景下的城市计划的修复。
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.
