Unraveling nonlinear impacts of seasonal climate and built environments on exercise walking in high-density cities via a modified machine learning approach

Key Points

• To explore how seasonal climate factors and built environments impact exercise walking in urban areas.
• Analyzed seasonal climate variability and its effects on walking patterns.
• Assessed spatial differences in built environments and their implications for exercise.
• Employed a modified machine learning approach to model these relationships.
• Identified key strategies for enhancing pedestrian walking in high-density settings.
• Showed that improved pedestrian infrastructure can significantly increase year-round walking.
• Suggested specific design changes to optimize urban spaces for better health outcomes.