An Interpretable Machine Learning Approach to Studying Environmental Safety Perception Among Elderly Residents in Pocket Parks

This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image semantic segmentation and explainable AI models (e.g., SHAP and PDP), the study transforms subjective environmental perception into measurable indicators and constructs an environmental safety perception model using the LightGBM algorithm. Results indicate that sufficient pedestrian areas and moderate crowd activities significantly enhance safety perception among the elderly. Conversely, the presence of cars emerges as the most substantial adverse factor. Natural elements, such as vegetation and grass, exhibit nonlinear effects on safety perception, with an optimal threshold range identified. The research further elucidates the intricate synergies and constraints among visual elements, underscoring that the highest perceived safety arises from the synergistic combination of positive factors. This study deepens the understanding of environmental perception among the elderly and offers a data-driven framework and practical guidelines for urban planners and designers. It holds significant theoretical and practical implications for advancing the refined and human-centered renewal of urban public spaces.