Abstract

Urban green space is conducive to the physical and mental health of residents. However, exposure to green space is often highly unequal. This research addresses a major gap in the green space literature: how daily mobility patterns lead to differential exposure to green space among residents and how this varies by gender. The study uses travel data (7,800 trips) from 662 residents in Beijing (Haidan District), in combination with high-resolution street-view data and machine learning, to measure exposure to green space. The results show that there are significant disparities with respect to exposure to green space during daily travel, with men having advantages in terms of this exposure. This is, in part, due to the fact that their travel patterns varied more widely. Women had more exposure to green space, in terms of overall travel time, but it was more constrained to selected routes. Reasons for these disparities are complex, with age, occupation, household type and travel mode (e.g., car, bus, by foot) all important factors that intertwine with gender. In addition to revealing gender disparities associated with exposure to green space, this study provides a novel mixed method to capture daily travel patterns that can be used for a broad array of inquiries related to mobility in the built environment.

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