Behavior-Specific Nonlinear Associations Between Microscale Built-Environment Features and Public-Space Behaviors in Community Parks

Community parks support everyday leisure, exercise, and social interaction, but how multidimensional microscale spatial and visual features are differently associated with these behavior types continues to warrant further investigation. In particular, semantic composition, spatial form, and low-level visual features have rarely been integrated with video-based behavior recognition and nonlinear modeling at the node level. To address this gap, this study examined behavior-specific nonlinear associations between microscale built-environment features and leisure, exercise, and social behavior in community parks. Using 35 observation nodes in five community parks in Dalian, China, 70 paired video and image samples were collected on weekdays and weekends. Environmental indicators were extracted from static images, while behavioral indicators were derived from video-based behavior recognition and aggregated into behavior proportions. Generalized additive mixed models were used to identify nonlinear associations and key response ranges. Leisure behavior was associated with built vertical interfaces, moderate background depth, and controlled visual stimulation. Recreational equipment proportion showed opposite associations with exercise and social behavior. Low-level visual features captured behavioral differences not fully explained by semantic indicators. These findings provide context-specific quantitative evidence for behavior-oriented assessment and node-level design support in community public spaces.