How human-perceived urban green spaces contribute to mental well-being: a machine learning approach
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View abstract on PubMed
Summary
This summary is machine-generated.Urban green spaces
Area Of Science
- Urban green spaces (UGS) research
- Human perception in landscape ecology
- Mental well-being (MWB) studies
Background
- Urbanization increases focus on UGS for mental well-being (MWB).
- Macro-scale UGS studies lack micro-perceptual insights for fine-grained design.
- Traditional methods struggle with complex nonlinear relationships in UGS-MWB links.
Purpose Of The Study
- Identify critical micro-scale UGS perceptual features influencing MWB.
- Characterize nonlinear association trajectories and interactions of these features.
- Apply ecosystem service supply-demand theory to UGS-MWB relationships.
Main Methods
- Grounded in Landsenses Ecology and ecosystem service supply-demand theory.
- Employed explainable machine learning (Random Forest and SHAP) in Beijing Olympic Forest Park.
- Systematically identified micro-environmental factors affecting MWB.
Main Results
- Natural UGS attributes (58%) significantly impact MWB, surpassing demographic factors (1.1%).
- Key factors show positive, nonlinear associations with MWB, with accelerating/decelerating patterns.
- Main effects of UGS factors are stronger than pairwise interactions, though some synergies/trade-offs exist.
Conclusions
- Multi-sensory UGS perceptions are strongly linked to MWB.
- Findings guide UGS research prioritization and experimental design.
- Provides evidence for optimizing UGS to enhance MWB through better ecological resource allocation.
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