Exploring the complex associations between community public spaces and healthy aging: an explainable analysis using...

Area of Science:

• Gerontology and Public Health
• Urban Planning and Environmental Health

Background:

• Global aging necessitates understanding community public spaces (CPS) for healthy aging.
• Existing research often uses linear methods, neglecting non-linear and interactive effects of CPS on elderly well-being.

Purpose of the Study:

• To investigate the non-linear and interactive associations between CPS environmental features and multiple health dimensions in older adults.
• To identify key CPS features and their interactions influencing physical, mental, and social health.

Main Methods:

• Analysis of data from 2,508 older adults in Shanghai.
• Utilized an explainable machine learning approach (CatBoost and SHAP) to examine 13 CPS environmental features.
• Identified correlates and interaction patterns between CPS features and health outcomes.

Main Results:

• Positive associations found between destination accessibility, public transport, residential density, and walking infrastructure with all health dimensions.
• Traffic complexity and crime rate showed consistent negative associations with health.
• SHAP interaction analysis highlighted synergistic combinations of features, rather than isolated features, driving health benefits.

Conclusions:

• CPS features exhibit multidimensional and non-linear relationships with elderly health.
• Findings support evidence-based community planning and interventions to create health-supportive environments for aging populations.