Abstract summary 

Previous research on the relationship between greenspace exposure and mental health has largely taken a residence-based approach to exposure assessment, ignoring the dynamic nature of people's daily movements. Moreover, most studies evaluated greenspace from an overhead perspective, whereas an eye-level perspective could potentially offer a more comprehensive understanding of individuals' encounters with greenspaces. Based on our survey in two communities in Hong Kong (Sham Shui Po and Tin Shui Wai), we captured people's eye-level greenspace exposure based on their travel routes and visited places using GPS trajectories, streetscape images, and deep learning methods. We then compared the results with those obtained with an overhead greenness exposure measure (the normalized difference vegetation index [NDVI]). The results indicate that these two greenspace measurements are not associated with each other, implying that they encompass distinct facets of greenspace, which may have different effects on mental health. Further, we examined the associations between various greenspace exposure measures and mental health using GPS trajectories and ecological momentary assessment data. The results reveal a negative association between eye-level greenspace exposure and momentary stress, while no similar association was observed when using the top-down NDVI as an indicator of greenspace exposure. Moreover, compared to the total volume of greenspace exposure, the distance-weighted average of greenspace exposure based on dynamic mobility contexts has a stronger association with individual overall mental health. Lastly, the relationship between greenspace exposure and mental health varies between the two communities with different socio-economic attributes. The study indicates that policymakers should focus not only on residential neighborhoods and overhead greenspace but also consider the dynamic environments and socio-economic contexts that people are embedded in.

Authors & Co-authors:  Yu Kwan

Study Outcome 

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