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The health benefits of urban green spaces have been found to vary for different populations, such as people of different socio-demographics or different cultures. Among these populations, one remains understudied although its numbers are growing: Chinese international students. Indeed, more and more Chinese students choose to go abroad for higher education but face specific challenges, often resulting in them experiencing high levels of stress and poor health. This study explores the link between Chinese international students’ use of local green spaces and the effect on their perceived stress and health. An online survey was created to collect data from 186 Chinese international students studying in Edinburgh, Scotland (UK). The data covered the participants’ reported health status and their perceived stress levels, the strategies they use when coping with stress, their awareness of the benefits of visiting green spaces as well as their self-reported use of and access to local green spaces. The findings show a significant correlation between access to and use of urban green spaces and the self-reported wellbeing indicators. Some of the barriers experienced by participants in visiting green spaces were also explored. By investigating the specific behaviors of this emerging and vulnerable population, this study expands the corpus of existing evidence for the role played by urban green spaces in supporting wellbeing. Some recommendations to support the health of this community using urban green spaces can therefore be proposed.

Cities are complex systems and their physical forms are the manifestation of cultural, social and economic processes shaped by the geometry of natural and man-made elements. Digital Surface Models (DSM) using LiDAR provide an efficient volumetric transformation of urban fabric including all built and natural elements which allows the study of urban complexity through the lens of fractal dimension (D). Founded on the “box-counting” method, we reveal a voxelization technique developed in GIS (Geographic Information System) to estimate D values of ten DSM samples across central Melbourne. Estimated D values of surface models (between 2 and 3) provide a measure to interpret the structural complexity of different urban characters defined by the pattern of developments and densities. The correlations between D values with other DSM properties such as elevation, volume, solar radiation and surface roughness, showed a strong relationship between DSM volume and mean elevation. Lower strength correlations were recorded with solar radiation and surface roughness. The proposed method provides opportunities for fractal research to study pressing issues in complex urban environments such as declining physical fitness, mental health and urban biodiversity.