Emotional Responses to the Visual Patterns of Urban Streets: Evidence from Physiological and Subjective Indicators

From space to street: A systematic review of the associations between visible greenery and bluespace in street view imagery and mental health

A large body of literature shows that living near greenery supports healthy lifestyles and improves mental health. Much of this research has used greenery measured from a bird's eye perspective. Street view images (SVI) are an important alternative data source that could assess visible greenery experienced by residents in daily life. The current review is the first to systematically critique and synthesize the evidence relating to greenery and bluespace in SVI and its associations with mental health outcomes. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to conduct this review. First, we identified relevant articles published as of April 2023 in PubMed, Web of Science, Scopus, and CINAHL. Articles meeting inclusion criteria were narratively synthesized. Quality assessments were conducted with the Newcastle-Ottawa Scale (NOS). Based on our search, we identified 35 articles on greenery and bluespace measured with SVI and mental health outcomes. Two-thirds of the included papers found positive associations between greenery in SVI and mental health. The average score for risk of bias was good. Association between visible greenery in SVI and all 10 of the mental health outcomes studied were low or very low quality of evidence and showed limited or inadequate strength of evidence. SVI is likely to be an increasingly used and a validated instrument for estimating health-promoting exposure to greenery. Future research would benefit from the standardization of SVI datasets and computational processes, and studies conducted outside of China and high-income countries. Such advancements would improve the generalizability and robustness of associations between visible greenery and mental health outcomes.

Physiological and affective responses to green space virtual reality among pregnant women

BACKGROUND

Benefits of green spaces on stress reduction have been shown in previous studies. Most existing studies to date have focused on the general population. However, there is a lack of understanding of physiological mechanisms underlying the beneficial effects of green space among special populations, such as pregnant women.

OBJECTIVES

To examine physiological and affective responses to green space on stress recovery among pregnant women, using simulated green space exposure through virtual reality (VR).

METHODS

We recruited 63 pregnant women between 8 and 14 weeks' gestational age for a laboratory experiment. Participants were randomly assigned to view one of three, 5-min, VR videos of an urban scene with different green space levels (i.e., non-green, moderate, and high) after a laboratory stressor, the Trier Social Stress Test. Physiological stress responses were measured via changes in blood pressure, heart rate, skin conductance level, salivary alpha-amylase, and salivary cortisol. Affective response was measured using the Positive and Negative Affect Scale.

RESULTS

We found that visual exposure to a green space environment in VR was associated with both physiological and affective stress reduction among pregnant women, including lower systolic blood pressure [-4.6 mmHg, 95% confidence interval (CI): -8.8, -0.4], reduced salivary alpha-amylase concentration (-1.2 ng/ml, 95% CI: -2.2, -0.2), improved overall positive affect (score: 6.6, 95% CI: 0.3, 13.0) and decreased negative affect of anxiety (score: -2.6, 95% CI: -5.19, -0.04) compared to non-green space environment. Exposure to high green space environment in park-like setting had the strongest impacts on stress recovery.

CONCLUSION

This study demonstrated that virtual green space exposure could effectively ease stress and improve mental health and well-being during pregnancy. Even a short immersion in VR-based green space environment may bring health benefits, which has significant implications for pregnant women when access to an actual nature may not be possible.

Restorative perception of urban streets: Interpretation using deep learning and MGWR models

Restorative environments help people recover from mental fatigue and negative emotional and physical reactions to stress. Excellent restorative environments in urban streets help people focus and improve their daily behavioral performance, allowing them to regain efficient information processing skills and cognitive levels. High-density urban spaces create obstacles in resident interactions with the natural environment. For urban residents, the restorative function of the urban space is more important than that of the natural environment in the suburbs. An urban street is a spatial carrier used by residents on a daily basis; thus, the urban street has considerable practical value in terms of improving the urban environment to have effective restorative function. Thus, in this study, we explored a method to determine the perceived restorability of urban streets using street view data, deep learning models, and the Ordinary Least Squares (OLS), the multiscale geographically weighted regression (MGWR) model. We performed an empirical study in the Nanshan District of Shenzhen, China. Nanshan District is a typical high-density city area in China with a large population and limited urban resources. Using the street view images of the study area, a deep learning scoring model was developed, the SegNet algorithm was introduced to segment and classify the visual street elements, and a random forest algorithm based on the restorative factor scale was employed to evaluate the restorative perception of urban streets. In this study, spatial heterogeneity could be observed in the restorative perception data, and the MGWR models yielded higher R ² interpretation strength in terms of processing the urban street restorative data compared to the ordinary least squares and geographically weighted regression (GWR) models. The MGWR model is a regression model that uses different bandwidths for different visual street elements, thereby allowing additional detailed observation of the extent and relevance of the impact of different elements on restorative perception. Our research also supports the exploration of the size of areas where heterogeneity exists in space for each visual street element. We believe that our results can help develop informed design guidelines to enhance street restorative and help professionals develop targeted design improvement concepts based on the restorative nature of the urban street.

Application of Neuroscience Tools in Building Construction – An Interdisciplinary Analysis

Interdisciplinary integration is a new driving force in development of science and technology. Neuroscience, a powerful tool for studying human physiology and psychology that is greatly interconnected with the field of building construction, has attracted numerous research attention. In this paper, we systematically review the interdisciplinary applications of neuroscience tools using bibliometric methods. We report that the built environment, construction safety, architectural design, and occupational health are the main areas of research attention, while thermal comfort, air quality, hazard recognition, safety training, aesthetic design, and biophilic design, among others, comprise the most frequently studied topics with regards to application of neuroscience tools. Currently, eye tracking and the electroencephalogram are the most commonly used tools in the field of building construction, while functional near-infrared spectroscopy, functional magnetic resonance imaging and trigeminal nerve stimulation are still at their initial stage of application.

Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning

This paper integrates classical design theory, multisource urban data, and deep learning to explore an accurate analytical framework in a new data environment, providing a scientific analysis path for the "where" and "how" of greenways in a high-density built environment. The analysis is based on street view data and location service data. Through the integration of multiple data sources such as street scape data, location service data, point-of-interest data, structured web data, and refined built environment data, a systematic measurement of the key elements of density, diversity, design, accessibility to destinations, and distance to transport facilities as defined in the Five Elements of High Quality Built Environment (5D) theory is achieved. The assessment of alignment potential was carried out. The key factors influencing the aesthetics of the street were identified. Based on an extensive landscape perception-based survey, it was found that although different respondents had different views and preferences for the same street scape, their preferences were overwhelmingly influenced by the visual quality of the street scape aesthetics itself, with higher aesthetic quality of the landscape.