The impact of factors such as the built environment, road vehicles, and air quality on urban vitality attracts increasing interest in urban planning and design research. However, tacit assumptions of linear relationships between these factors have been embedded in most studies, leading to biased estimations of their effects on urban vitality. This study addresses the gap by using machine learning models and SHAP (SHapley Additive exPlanations) to investigate the non-linear and threshold effects of the built environment, road vehicles and air pollution on urban vitality, using Manhattan as a study case. Urban vitality was represented by pedestrian presence in 29,540 street-view images. Results showed that Extreme Gradient Boosting outperformed Ordinary Least Squares, Random Forest, and Gradient Boosting Decision Trees in urban vitality estimation. It reveals that while the built environment variables explained a significant portion (77.5 %) of the variance in urban vitality, road vehicles (such as bicycles, buses, cars and motorbikes) and ozone concentrations accounted for 15.18 % and 1.46 %, respectively. The built environment and road vehicle factors exhibit positive nonlinear relationships with urban vitality. Meanwhile, ozone concentration demonstrated a negative threshold effect on urban vitality with a threshold at 27.5 ppb. This study advances our understanding of the threshold effect mechanism of the factors on urban vitality, offering insights into fostering sustainable urban environment.

中文翻译：

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建筑环境、道路车辆和空气污染对城市活力的非线性和阈值效应


建筑环境、道路车辆和空气质量等因素对城市活力的影响吸引了人们对城市规划和设计研究的日益增长的兴趣。然而，这些因素之间线性关系的默许已经嵌入到大多数研究中，导致对它们对城市活力影响的估计存在偏差。本研究通过使用机器学习模型和 SHAP（SHapley 加法解释）来研究建筑环境、道路车辆和空气污染对城市活力的非线性和阈值影响，以曼哈顿为研究案例，从而解决了这一差距。城市活力由 29,540 张街景图像中的行人形象来代表。结果表明，在城市活力估计中，极端梯度提升优于普通最小二乘法、随机森林和梯度提升决策树。它揭示了虽然建筑环境变量解释了城市活力差异的很大一部分 （77.5 %），但道路车辆（如自行车、公共汽车、汽车和摩托车）和臭氧浓度分别占 15.18 % 和 1.46 %。建筑环境和道路车辆因素与城市活力呈正非线性关系。同时，臭氧浓度对城市活力表现出负阈值效应，阈值为 27.5 ppb。本研究进一步加深了我们对因素对城市活力阈值效应机制的理解，为培育可持续的城市环境提供了见解。