Nonlinear and threshold effects of the built environment, road vehicles and air pollution on urban vitality

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.