Urban Environments & Human Health Lab (former name: VR Lab of UEHH)

About the lab

The mission of the Virtual Reality Lab of Urban Environments & Human Health (UEHH) is to understand how and to what extent urban environments, especially urban green spaces, influence human health and wellbeing. Researchers in the lab are concerned with understanding and measure the influence through multiple approaches including psychological, physiological, and hormonal measures of human health.

Featured projects (1)

Examine the impacts of electronic screens on people's behavioral and mental status in urban environments.

Featured research (11)

Background: The striking racial disparity in SARS-CoV-2 infection rates in the United States has been frequently reported. It is unclear to what extent the racial disparity in environmental exposure is related to the racial disparity in SARS-CoV-2 infection rates. We adopted 1416 counties in the contiguous United States as research sites to explore the differences in population exposure to green spaces and urbanized areas across racial population groups and the associated health outcomes. Methods: To ensure that the selected counties were representative, we applied a random selection strategy based on geographic spatial distribution and obtained robust results. First, we used 30-m-spatial resolution land cover and racial mappings to quantify the racial disparity between black and white people in exposure to green spaces and other environments in each county. Then, we investigated the association between the racial disparity in those environmental exposures and racial disparity in SARS-CoV-2 infection rates. We conducted a multivariate sensitivity analysis to make our findings more robust. Findings: We found, compared to white people, black people had significantly higher infection rates, less exposure to green space, and more exposure to urbanized areas with three levels of development intensity. Further, we found that greater exposure to green spaces and urban areas with low or medium development intensity was significantly associated with lower racial disparity in infection rates (R2 = 0∙370, p < 0∙001). Among all environmental exposures, exposure to forests outside parks yielded the strongest association with lower racial disparity in infection rates. Interpretation: Our findings suggest that providing access to nearby urban forests and adopting low or moderate urban development can reduce racial disparity in public health outcomes.
Due to intense urbanization in the last decade, high-density city has become a major type of human habitat globally. In those cities, oppressiveness has been recognized as a dominating environmental perception. Stress Reduction Theory is a leading theory that explains the relationship between environmental exposure and mental stress. However, the theory missed that perceived oppressiveness may substantially explain impacts of environmental exposure on mental stress in the context of high-density city. This study aimed to address that significant theoretical deficiency. A new pathways model was proposed to investigate whether and to what extent environmental exposure impacts mental stress through perceived oppressiveness. To test this pathways model, we conducted an online photo-based experiment with Hong Kong city residents. Firstly, we used a grid method to randomly choose 90 street spots in the city area. We created one GIF image by integrating nine Google Street photos to cover the full 360° viewshed for each spot. The percentage of all streetscape elements for each GIF image was measured. Then, 1396 participants were randomly assigned to view three of 90 GIF images. After viewing each image, participants reported perceived oppressiveness, perceived environmental quality, and acute mental stress responses. Lastly, participants reported their socioeconomic, demographic, and other background information. We identified three pathways linking streetscapes to mental stress response. After controlling for covariates, perceived oppressiveness was the major mediator to link streetscapes and mental stress, explaining 50.2% of relationship. Tree canopy and sky had the greatest association with lower level of stress through perceived oppressiveness, while vehicles and billboards had the greatest association with higher level of stress through perceived oppressiveness. This new pathways model confirms the major role of perceived oppressiveness in interpreting the impact of urban streetscapes on mental stress in high-density cities. The results suggest an update of Stress Reduction Theory is feasible and necessary. Keywords Stress reduction theory;High-density city;Mental stress;Oppressiveness;Streetscape;Pathways model
Knowledge workers drive social and economic development in contemporary cities but often exhibit poor psychological and physical health because of sedentary work, long-term and intense mental labor, and high-level occupational competition. Thus, providing high-quality restorative green spaces in knowledge workers’ proximity to promote their health and well-being has become an important and pressing need. Although the multiple health benefits of proximity to green spaces have been highlighted, the existing planning and design practices are not well supported by scientific theories and evidence. This study interprets the health benefits of proximity to green spaces in work environments considering four theoretical mechanisms: stress reduction, attention restoration and landscape preference, physical activity promotion, and sensory enrichment through an integrative literature review. Next, the paper identifies the key environmental characteristics of green spaces that can enhance the health and well-being of knowledge workers. In addition, it develops a set of criteria for evaluating the restorative capacity of existing sites and a set of guidelines to design restorative nearby green spaces, and proposes a simple paradigm to connect interdisciplinary research and practice.
The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest–infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100m to 4km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200m for forest outside park and within 600m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.
The COVID-19 pandemic has caused a huge loss of human life globally. However, few studies investigated the link between exposure to green space and risk of COVID-19 mortality rate, while also distinguishing the effects of various types of green space, considering the spatial distribution of human population and green space, and identifying the optimal buffer distances of nearby green space. It is critical and pressing to fill this significant knowledge gap to protect and promote billions of people's health and life across the world. This study adopts a negative binomial generalized linear mixed-effects model to examine the association between the ratios of various types of green space, population-weighted exposure to those various types of green space, and COVID-19 mortality rates across 3025 counties in the USA, adjusted for sociodemographic, pre-existing chronic disease, policy and regulation, behavioral, and environmental factors. The findings show that greater exposure to forest was associated with lower COVID-19 mortality rates, while developed open space had mixed associations with COVID-19 mortality rates. Forest outside park had the largest effect size across all buffer distances, followed by forest inside park. The optimal exposure buffer distance was 1 km for forest outside park, with per one-unit of increase in exposure associated with a 9.9 % decrease in COVID-19 mortality rates (95 % confidence interval (CI): 6.9 %–12.8 %). The optimal exposure buffer distance of forest inside park was 400 m, with per one-unit of increase in exposure associated with a 4.7 % decrease in mortality rates (95 % CI: 2.4 %–6.9 %). The results suggest that greater exposure to green spaces, especially to nearby forests, may mitigate the risk of COVID-19 mortality. Although findings of an ecological study cannot be directly used to guide medical interventions, this study may pave a critical new way for future research and practice across multiple disciplines.

Lab head

Bin Jiang
  • Department of Architecture
About Bin Jiang
  • Bin JIANG is an Associate Professor in Landscape Architecture and the founding director of the Built Environments and Human Health Lab (UEHH) at the University of Hong Kong. BJ holds a Ph.D. degree from the University of Illinois at Urbana-Champaign. His research focuses on investigating impacts of built environment on public health.

Members (8)

Chris Webster
  • The University of Hong Kong
Wenyan Xu
  • The University of Hong Kong
Xueming Liu
  • The University of Hong Kong
Yuwen Yang
  • The University of Hong Kong
Lin Qiao
  • Zhejiang University
Lan Luo
  • The University of Hong Kong
Huan Lu
  • The University of Hong Kong
Li Jiali
  • The University of Hong Kong

Alumni (2)

Huaqing Wang
  • Utah State University
Jielin Chen
  • National University of Singapore