Predicting geriatric environmental safety perception assessment using LightGBM and SHAP framework

Abstract Global population aging highlights the need to understand how the elderly perceive safety in urban public spaces. This study used image semantic segmentation to identify key visual elements from panoramic images. A dataset was created by combining manual scoring with deep learning to explore how pocket park environments impact older adults’ safety perceptions. Analyzing 497 images from 29 pocket parks in Xiamen Island with LightGBM and SHAP tools, researchers identified visual elements that significantly affect seniors’ safety perceptions. The findings indicate: (1) Elderly environmental safety perceptions in the 29 surveyed parks on Xiamen Island were generally positive, yet safety scores varied markedly across parks. (2) Pedestrian area, car, wall, person, billboard, parterre, and vegetation were identified as the seven visual elements most impactful on elderly environmental safety perceptions. (3) Interactions among visual elements were observed, with vegetation exerting a notably regulatory effect on environmental safety perceptions, significantly enhancing the elderly’s perception of security. This study’s empirical analysis elucidates the influence of visual elements in pocket parks on elderly environmental safety perceptions, offering practical guidance for park planners to design more inclusive and secure green spaces for the elderly, with broad application potential.

Abstract licence: CC BY-NC-ND

34 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.