Comparing the Normalized Difference Vegetation Index with the Google Street View Measure of Vegetation to Assess Associations between Greenness, Walkability, Recreational Physical Activity, and Health in Ottawa, Canada

Associations of neighborhood greenspace, and active living environments with autism spectrum disorders: A matched case-control study in Ontario, Canada

BACKGROUND

Increasing evidence links early life residential exposure to natural urban environmental attributes and positive health outcomes in children. However, few studies have focused on their protective effects on the risk of autism spectrum disorder (ASD). The aim of this study was to investigate the associations of neighborhood greenspace, and active living environments during pregnancy with ASD in young children (≤6 years).

METHODS

We conducted a population-based matched case-control study of singleton term births in Ontario, Canada for 2012-2016. The ASD and environmental data was generated using the Ontario Autism Spectrum Profile, the Better Outcomes Registry & Network Ontario, and Canadian Urban Environmental Health Research Consortium. We employed conditional logistic regressions to estimate the odds ratio (OR) between ASD and environmental factors characterizing selected greenspace metrics and neighborhoods conducive to active living (i.e., green view index (GVI), normalized difference vegetation index (NDVI), tree canopy, park proximity and active living environments index (ALE)).

RESULTS

We linked 8643 mother-child pairs, including 1554 cases (18%). NDVI (OR 1.034, 0.944-1.024, per Inter Quartile Range [IQR] = 0.08), GVI (OR 1.025, 95% CI 0.953-1.087, per IQR = 9.45%), tree canopy (OR 0.992, 95% CI 0.903-1.089, per IQR = 6.24%) and the different categories of ALE were not associated with ASD in adjusted models for air pollution. In contrast, living closer to a park was protective (OR 0.888, 0.833-0.948, per 0.06 increase in park proximity index), when adjusted for air pollution.

CONCLUSIONS

This study reported mixed findings showing both null and beneficial effects of green spaces and active living environments on ASD. Further investigations are warranted to elucidate the role of exposure to greenspaces and active living environments on the development of ASD.

Health and wellbeing benefits of urban forests in winter: a narrative review

Urban trees and green spaces, hereafter, urban forests, are known to contribute to human health and wellbeing. However, research has predominantly focused on warm seasons. To understand whether these benefits extend to winter months, when vegetation is dormant, we conducted a narrative review of the health outcomes associated with urban forests in winter in cities with cold climates. We synthesized findings from 21 studies originating from Asia, Europe and North America. The most studied health outcomes were mental health, physical activity and physiological relaxation, all showing a positive relationship with urban forest exposure. These finding appear similar to those observed in warmer seasons. However, more studies are needed, on a diversity of health outcomes, to draw more robust conclusions in this emerging research field. Future research on urban forests should therefore consider winter and the effect of seasonality to improve health and wellbeing of urban dwellers in all seasons.

Longitudinal study of changes in greenness exposure, physical activity and sedentary behavior in the ORISCAV-LUX cohort study

BACKGROUND

Greenness exposure has been associated with many health benefits, for example through the pathway of providing opportunities for physical activity (PA). Beside the limited body of longitudinal research, most studies overlook to what extent different types of greenness exposures may be associated with varying levels of PA and sedentary behavior (SB). In this study, we investigated associations of greenness characterized by density, diversity and vegetation type with self-reported PA and SB over a 9-year period, using data from the ORISCAV-LUX study (2007-2017, n = 628).

METHODS

The International Physical Activity Questionnaire (IPAQ) short form was used to collect PA and SB outcomes. PA was expressed as MET-minutes/week and log-transformed, and SB was expressed as sitting time in minutes/day. Geographic Information Systems (ArcGIS Pro, ArcMap) were used to collect the following exposure variables: Tree Cover Density (TCD), Soil-adjusted Vegetation Index (SAVI), and Green Land Use Mix (GLUM). The exposure variables were derived from publicly available sources using remote sensing and cartographic resources. Greenness exposure was calculated within 1000m street network buffers around participants' exact residential address.

RESULTS

Using Random Effects Within-Between (REWB) models, we found evidence of negative within-individual associations of TCD with PA (β = - 2.60, 95% CI - 4.75; - 0.44), and negative between-individual associations of GLUM and PA (β = - 2.02, 95% CI - 3.73; - 0.32). There was no evidence for significant associations between greenness exposure and SB. Significant interaction effects by sex were present for the associations between TCD and both PA and SB. Neighborhood socioeconomic status (NSES) did not modify the effect of greenness exposure on PA and SB in the 1000 m buffer.

DISCUSSION

Our results showed that the relationship between greenness exposure and PA depended on the type of greenness measure used, which stresses the need for the use of more diverse and complementary greenness measures in future research. Tree vegetation and greenness diversity, and changes therein, appeared to relate to PA, with distinct effects among men and women. Replication studies are needed to confirm the relevance of using different greenness measures to understand its' different associations with PA and SB.

Residential surrounding greenness and the incidence of childhood asthma: Findings from a population-based cohort in Ontario, Canada

Several epidemiological studies have investigated the possible role that living in areas with greater amounts of greenspace has on the incidence of childhood asthma. These findings have been inconsistent, and few studies explored the relevance of timing of exposure. We investigated the role of residential surrounding greenness on the risk of incident asthma using a population-based retrospective cohort study. We included 982,131 singleton births in Ontario, Canada between 2006 and 2013. Two measures of greenness, the Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI), were assigned to the residential histories of these infants from pregnancy through to 12 years of age. Longitudinally-based diagnoses of asthma were determined by using provincial administrative health data. The extended Cox hazards model was used to characterize associations between greenness measures and asthma (up to age 12 years) while adjusting for several risk factors. In a fully adjusted model, that included a term for traffic-related air pollution (NO2), we found no association between an interquartile range increase (0.08) of the NDVI during childhood and asthma incidence (HR = 0.99; 95 % CI = 0.99-1.01). In contrast, we found that an 0.08 increase in NDVI during childhood reduced the risk of asthma in children 7-12 years of age by 14 % (HR = 0.86, 95 % CI:0.79-0.95). Seasonal differences in the association between greenness and asthma were noted. Our findings suggest that residential proximity to greenness reduces the risk of asthma in children aged 7-12.

Does urban greenness reduce loneliness and social isolation among Canadians? A cross-sectional study of middle-aged and older adults of the Canadian Longitudinal Study on Aging (CLSA)

OBJECTIVES

Urban greenness has been shown to confer many health benefits including reduced risks of chronic disease, depression, anxiety, and, in a limited number of studies, loneliness. In this first Canadian study on this topic, we investigated associations between residential surrounding greenness and loneliness and social isolation among older adults.

METHODS

This cross-sectional analysis of the Canadian Longitudinal Study on Aging included 26,811 urban participants between 45 and 86 years of age. The Normalized Difference Vegetation Index (NDVI), a measure of greenness, was assigned to participants' residential addresses using a buffer distance of 500 m. We evaluated associations between the NDVI and (i) self-reported loneliness using the Center for Epidemiological Studies Depression Scale, (ii) whether participants reported "feeling lonely living in the local area", and (iii) social isolation. Logistic regression models were used to characterize associations between greenness and loneliness/social isolation while adjusting for individual socio-economic and health behaviours.

RESULTS

Overall, 10.8% of participants perceived being lonely, while 6.5% reported "feeling lonely in their local area". Furthermore, 16.2% of participants were characterized as being socially isolated. In adjusted models, we observed no statistically significant difference (odds ratio (OR) = 0.99; 95% confidence interval (CI) 0.93-1.04) in self-reported loneliness in relation to an interquartile range (IQR) increase of NDVI (0.06). However, for the same change in greenness, there was a 15% (OR = 0.85; 95% CI 0.72-0.99) reduced risk for participants who strongly agreed with "feeling lonely living in the local area". For social isolation, for an IQR increase in the NDVI, we observed a 7% (OR = 0.93; 95% CI 0.88-0.97) reduction in prevalence.

CONCLUSION

Our findings suggest that urban greenness plays a role in reducing loneliness and social isolation among Canadian urbanites.

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research.

Beneficial associations between outdoor visible greenness at the workplace and metabolic syndrome in Chinese adults

BACKGROUND

Greenness surrounding residential places has been found to significantly reduce the risk of diseases such as hypertension, obesity, and metabolic syndrome (MetS). However, it is unclear whether visible greenness exposure at the workplace has any impact on the risk of MetS.

METHODS

Visible greenness exposure was assessed using a Green View Index (GVI) based on street view images through a convolutional neural network model. We utilized logistic regression to examine the cross-sectional association between GVI and MetS as well as its components among 51,552 adults aged 18-60 in the city of Hangzhou, China, from January 2018 to December 2021. Stratified analyses were conducted by age and sex groups. Furthermore, a scenario analysis was conducted to investigate the risks of having MetS among adults in different GVI scenarios.

RESULTS

The mean age of the participants was 40.1, and 38.5% were women. We found a statistically significant association between GVI and having MetS. Compared to the lowest quartile of GVI, participants in the highest quartile of GVI had a 17% (95% CI: 11-23%) lower odds of having MetS. The protective association was stronger in the males, but we did not observe such differences in different age groups. Furthermore, we found inverse associations between GVI and the odds of hypertension, low high-density lipoprotein cholesterol, obesity, and high levels of FPG.

CONCLUSIONS

Higher exposure to outdoor visible greenness in the workplace environment might have a protective effect against MetS.

Comprehensive evaluation of community human settlement resilience and spatial characteristics based on the supply-demand mismatch between health activities and environment: a case study of downtown Shanghai, China

INTRODUCTION

Under globalization, human settlement has become a major risk factor affecting life. The relationship between humans and the environment is crucial for improving community resilience and coping with globalization. This study focuses on the key contradictions of community development under globalization, exploring community resilience by analyzing the mismatch between residents' health activities and the environment.

METHODS

Using data from Shanghai downtown, including land use, Sports app, geospatial and urban statistics, this paper constructs a comprehensive community resilience index (CRI) model based on the DPSIR model. This model enables quantitative analysis of the spatial and temporal distribution of Community Human Settlement Resilience (CR). Additionally, the paper uses geodetector and Origin software to analyze the coupling relationship between drivers and human settlement resilience.

RESULTS

i) The scores of CR showed a "slide-shaped" fluctuation difference situation; ii) The spatial pattern of CR showed a "pole-core agglomeration and radiation" type and a "ring-like agglomeration and radiation" type. iii) Distance to bus stops, average annual temperature, CO2 emissions, building density and number of jogging trajectories are the dominant factors affecting the resilience level of community human settlement.

CONCLUSION

This paper contributes to the compilation of human settlement evaluation systems globally, offering insights into healthy community and city assessments worldwide. The findings can guide the creation of similar evaluation systems and provide valuable references for building healthy communities worldwide.

Effects of greenery at different heights in neighbourhood streetscapes on leisure walking: a cross-sectional study using machine learning of streetscape images in Sendai City, Japan

BACKGROUND

It has been pointed out that eye-level greenery streetscape promotes leisure walking which is known to be a health -positive physical activity. Most previous studies have focused on the total amount of greenery in the eye-level streetscape to investigate its association with walking behaviour. While it is acknowledged that taller trees contribute to greener environments, providing enhanced physical and psychological comfort compared to lawns and shrubs, the examination of streetscape metrics specifically focused on greenery height remains largely unexplored. Therefore, this study examined the relationship between objective indicators of street greenery categorized by height from a pedestrian viewpoint and leisure walking time.

METHODS

We created streetscape indices of street greenery using Google Street View Images at 50-m intervals in an urban area in Sendai City, Japan. The indices were classified into four ranges according to the latitude of the virtual hemisphere centred on the viewer. We then investigated their relationship to self-reported leisure walking.

RESULTS

Positive associations were identified between the street greenery in higher positions and leisure walking time, while there was no significant association between the greenery in lower positions.

CONCLUSION

The findings indicated that streets with rich greenery in high positions may promote residents' leisure walking, indicating that greenery in higher positions contributes to thermally comfortable and aesthetic streetscapes, thus promoting leisure walking. Increasing the amount of greenery in higher positions may encourage residents to increase the time spent leisure walking.

The influence path of community green exposure index on activity behavior under multi-dimensional spatial perception

The purpose of this research is to reveal the internal relationship among community green space, space perception, and activity behavior response to supplement the lack of research results on the binary relationship between green space and behavior. Nine residential community green spaces and 398 residents were selected as the research objects. Thematic clustering and factor identification were used to determine the spatial dimensions of community green space that residents were concerned about. The analysis of the green exposure index, spatial perception evaluation, and activity behavior survey were combined to determine the influence of the green exposure index on spatial perception and activity behavior and its internal correlation path. According to research data, the community green view index (GVI) and normalized difference vegetation index (NDVI) negatively affected the perception factor, while the perception factor positively affected the activity frequency. The SEM model shows that the green exposure index stimulated activity behavior through the intermediate effect of the internal perception path of perceived landscape quality, perceived workability, and perceived accessibility. Spatial perception as the basis of the instantaneous emotional reaction process may affect people's choices for activities but be unable to extend the duration of the activities. The internal association among community green space, spatial perception, and physical activity behavior develops on the basis of spatial patterns at certain scales. This study provides a theoretical basis for understanding the spatial experience and residents' behavioral needs, evaluating the quality of urban green space scientifically, and promoting the optimization of community green space structure.

Towards sustainable and net-zero cities: A review of environmental modelling and monitoring tools for optimizing emissions reduction strategies for improved air quality in urban areas

Climate change is a defining challenge for today's society and its consequences pose a great threat to humanity. Cities are major contributors to climate change, accounting for over 70% of global greenhouse gas emissions. With urbanization occurring at a rapid rate worldwide, cities will play a key role in mitigating emissions and addressing climate change. Greenhouse gas emissions are strongly interlinked with air quality as they share emission sources. Consequently, there is a great opportunity to develop policies which maximize the co-benefits of emissions reductions on air quality and health. As such, a narrative meta-review is conducted to highlight state-of-the-art monitoring and modelling tools which can inform and monitor progress towards greenhouse gas emission and air pollution reduction targets. Urban greenspace will play an important role in the transition to net-zero as it promotes sustainable and active transport modes. Therefore, we explore advancements in urban greenspace quantification methods which can aid strategic developments. There is great potential to harness technological advancements to better understand the impact of greenhouse gas reduction strategies on air quality and subsequently inform the optimal design of these strategies going forward. An integrated approach to greenhouse gas emission and air pollution reduction will create sustainable, net-zero and healthy future cities.

Greenness and equity: Complex connections between intra-neighborhood contexts and residential tree planting implementation

Associations between neighborhood greenness and socioeconomic status (SES) are established, yet intra-neighborhood context and SES-related barriers to tree planting remain unclear. Large-scale tree planting implementation efforts are increasingly common and can improve human health, strengthen climate adaptation, and ameliorate environmental inequities. Yet, these efforts may be ineffective without in-depth understanding of local SES inequities and barriers to residential planting. We recruited 636 residents within and surrounding the Oakdale Neighborhood of Louisville, Kentucky, USA, and evaluated associations of individual and neighborhood-level sociodemographic indicators with greenness levels at multiple scales. We offered no-cost residential tree planting and maintenance to residents within a subsection of the neighborhood and examined associations of these sociodemographic indicators plus baseline greenness levels with tree planting adoption among 215 eligible participants. We observed positive associations of income with Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) within all radii around homes, and within yards of residents, that varied in strength. There were stronger associations of income with NDVI in front yards but LAI in back yards. Among Participants of Color, associations between income and NDVI were stronger than with Whites and exhibited no association with LAI. Tree planting uptake was not associated with income, education, race, nor employment status, but was positively associated with lot size, home value, lower population density, and area greenness. Our findings reveal significant complexity of intra-neighborhood associations between SES and greenness that could help shape future research and equitable greening implementation. Results show that previously documented links between SES and greenspace at large scales extend to residents' yards, highlighting opportunities to redress greenness inequities on private property. Our analysis found that uptake of no-cost residential planting and maintenance was nearly equal across SES groups but did not redress greenness inequity. To inform equitable greening, further research is needed to evaluate culture, norms, perceptions, and values affecting tree planting acceptance among low-SES residents.

Associations of residential greenness with unhealthy consumption behaviors: Evidence from high-density Hong Kong using street-view and conventional exposure metrics

AIM

Residential greenness was theoretically associated with health-related consumption behaviors concerning the socio-ecological model and restoration environment theory, but empirical studies were limited, especially in high-density cities. We examined the associations of residential greenness with unhealthy consumption behaviors (infrequent breakfast consumption, infrequent fruit consumption, infrequent vegetable consumption, alcohol drinking, binge drinking, cigarette smoking, moderate-to-heavy smoking, and heavy smoking) using street-view and conventional greenness metrics in high-density Hong Kong.

METHODS

This cross-sectional study employed survey data from 1,977 adults and residence-based objective environmental data in Hong Kong. Street-view greenness (SVG) was extracted from Google Street View images using an object-based image classification algorithm. Two conventional greenness metrics were used, including normalized difference vegetation index (NDVI) derived from Landsat 8 remote-sensing images and park density derived from a geographic information system database. In the main analyses, logistic regression analyses together with interaction and stratified models were performed with environmental metrics measured within a 1000-m buffer of residence.

RESULTS

A standard deviation higher SVG and NDVI were significantly associated with fewer odds of infrequent breakfast consumption (OR = 0.81, 95% CI 0.71-0.94 for SVG; OR = 0.83, 95% CI 0.73-0.95 for NDVI), infrequent fruit consumption (OR = 0.85, 95% CI 0.77-0.94 for SVG; OR = 0.85, 95% CI 0.77-0.94 for NDVI), and infrequent vegetable consumption (OR = 0.78, 95% CI 0.66-0.92 for SVG; OR = 0.81, 95% CI 0.69-0.94 for NDVI). The higher SVG was significantly associated with less binge drinking and the higher SVG at a 400-m buffer and a 600-m buffer were significantly associated with less heavy smoking. Park density was not significantly associated with any unhealthy consumption behaviors. Some of the above significant associations were moderated by moderate physical activity, mental and physical health, age, monthly income, and marital status.

CONCLUSIONS

This study highlights the potential beneficial impact of residential greenness, especially in terms of street greenery, on healthier eating habits, less binge drinking, and less heavy smoking.

The effect of greenness on allergic rhinitis outcomes in children and adolescents: A systematic review and meta-analysis

BACKGROUND

The relationship between greenness and health emerges as new public health concern. More published studies from multiple areas have explored the relationship between greenness and allergic rhinitis (AR) in children and adolescents. This study aims to determine the association between greenness and allergic rhinitis by systematic review and meta-analysis, in order to provide a more comprehensive assessment of the impact of greenness on AR in children and adolescents.

METHODS

The relative literature was systematically searched in PubMed, Embase, and Web of science lastly on September 25, 2022. Terms related to greenness and allergic rhinitis were used for searching. Summary effect estimates of greenness on AR in children and adolescents were calculated for per 10 % increase of greenness exposure with different buffer sizes by random-effects model.

RESULTS

A total of 579 studies were screened, and fourteen studies from Europe, Asia and North America were finally included. Most greenness exposure were measured by normalized difference vegetation index (NDVI). Enhanced vegetation index, outdoor-green environmental score and existed to measuring different greenness types. Greenness surrounding residences and schools were assessed. The overall effect of greenness on primary outcome was 1.00 (95%CI = 0.99-1.00). Most effect estimates of greenness were included in the NDVI-500 m group, and the pooled OR was 0.99 (95%CI = 0.97-1.01). No significant pooled estimates were found in analyses with study locations.

CONCLUSION

This study indicates no significant association between greenness exposure and AR in children and adolescents. Various exposure measures and conversion of data may affect the results of this meta-analysis. More precise assessment of personal greenness exposure in well-designed prospective studies are vital for drawing a definite association in future. Furthermore, greenness exposure surrounding schools should be paid considerable attention for its effect on AR in school-aged children and adolescents.

How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning

BACKGROUND

Natural environment might encourage physical exercise, hence enhancing human health and wellbeing. Social media offers an extensive repository of spatiotemporal data, containing details on the feelings and behaviors of individuals. However, investigations on physical activity and public sentiment in the natural environment of the downtown neighborhood are lacking in the existing literature.

METHODS

To extract environmental and behavioral information from social media data and other multi-source data, natural language processing, semantic segmentation, instance segmentation, and fully convolutional neural networks are employed. The research examines how neighborhood blue-green spaces and other health-promoting facilities affect physical activity and public sentiment.

RESULTS

The results reveal that blue space visibility, activity facilities, street furniture, and safety all have a favorable influence on physical activity with a social gradient. Amenities, perceived street safety and beauty positively correlated to public sentiment. The findings from social media about the environment and physical activity are consistent with traditional surveys from the same time period with a 0.588 kappa value.

CONCLUSION

According to our findings, social media data might be utilized to learn more about how urban environments influence people's physical activity patterns. Also, the health-promoting effects of blue space require more investigation.

Predicting walking-to-work using street-level imagery and deep learning in seven Canadian cities

New 'big data' streams such as street-level imagery are offering unprecedented possibilities for developing health-relevant data on the urban environment. Urban environmental features derived from street-level imagery have been used to assess pedestrian-friendly neighbourhood design and to predict active commuting, but few such studies have been conducted in Canada. Using 1.15 million Google Street View (GSV) images in seven Canadian cities, we applied image segmentation and object detection computer vision methods to extract data on persons, bicycles, buildings, sidewalks, open sky (without trees or buildings), and vegetation at postal codes. The associations between urban features and walk-to-work rates obtained from the Canadian Census were assessed. We also assessed how GSV-derived urban features perform in predicting walk-to-work rates relative to more widely used walkability measures. Results showed that features derived from street-level images are better able to predict the percent of people walking to work as their primary mode of transportation compared to data derived from traditional walkability metrics. Given the increasing coverage of street-level imagery around the world, there is considerable potential for machine learning and computer vision to help researchers study patterns of active transportation and other health-related behaviours and exposures.

Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study

BACKGROUND

Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents.

OBJECTIVE

This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada.

METHODS

We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level.

RESULTS

The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (-7.009; 95% CI -13.130 to -0.980) and for both males (-4.544; 95% CI -8.224 to -0.895) and females (-3.513; 95% CI -6.289 to -0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature.

CONCLUSIONS

As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being.

A Framework of Community Pedestrian Network Design Based on Urban Network Analysis

Community is the foundation of modern cities, where urban residents spend most of their lifetime. Effective and healthy community design plays a vital role in improving residents’ living quality. Pedestrian network is an indispensable element in the community. Successful pedestrian network design can help the residents be healthy both physically and mentally, build the awareness of “Go Green” for the society, and finally contribute to low-carbon and green cities. This paper proposes a community pedestrian network design method based on Urban Network Analysis with the help of the Rhino software. A case study of a typical community in Guangzhou, China was implemented, specifying the steps of the proposed method. The findings presented include the features of the citizens and the accessibilities of the neighbors that are obtained from the community pedestrian network simulation. The limitation and scalability of this method was discussed. The proposed method can be essential to designing healthy and sustainable communities.

The Association between Greenness and Urbanization Level with Weight Status among Adolescents: New Evidence from the HBSC 2018 Italian Survey

Recent studies have examined how the environment can influence obesity in young people. The research findings are conflicting: in some studies, green spaces have shown a protective association with obesity and urbanization has turned out to worsen this condition, while other studies contradicted these results. The aim of the study was to examine the relationships between greenness, urbanization, and weight status among Italian adolescents. Student data (11-13 years old) on weight and height, physical activity (PA), and demographic characteristics were extracted from the 2018 Health Behaviour in School-aged Children (HBSC) survey in Piedmont, Northwest of Italy. Data on Normalized Difference Vegetation Index (NDVI) and urbanization were obtained from satellite images and the National Institute of Statistics (ISTAT). A multilevel regression model was used to assess the association between NDVI, urbanization, and obesity, controlling for PA. Students living in greener areas reported a lower likelihood of being obese [OR = 0.11, 95% CI 0.02-0.56, p = 0.007], while students living in areas with a higher level of urbanization showed a significantly increased risk of obesity [OR = 2.3, 95% CI:1.14-4.6, p = 0.02]. Living surrounded by higher amounts of greenness and lower levels of urbanization may positively influence health status through lower risk of obesity among youth.

Association between residential green cover and direct healthcare costs in Northern California: An individual level analysis of 5 million persons

BACKGROUND AND OBJECTIVE

Prior studies have shown higher green cover levels are associated with beneficial health outcomes. We sought to determine if residential green cover was also associated with direct healthcare costs.

METHODS

We linked residential Normalized Difference Vegetation Index (NDVI) satellite data for 5,189,303 members of Kaiser Permanente Northern California (KPNC) to direct individual healthcare costs for 2003-2015. Using generalized linear regression to adjust for confounding, we examined the association between direct healthcare costs and green cover within250, 500, and 1000 meters (m) of an individual's residence. Costs were determined from an internal cost accounting system that captures administrative and patient care costs for each clinical encounter. Sensitivity analyses included adjustments for comorbidity and an alternative measure of green cover, tree canopy.

RESULTS

We observed a significant inverse association between higher levels of residential green cover and lower direct healthcare costs. The relative rate of total cost for the highest compared to the lowest decile of NDVI was 0.92 (95% CI 0.90-0.93) for the 500 m buffer. The association was robust to adjustment from a broad array of confounders, found at each buffer size, and largely driven by hospitalization, and emergency department visits. Individuals in the top decile of residential green cover had adjusted healthcare costs of $374.04 (95% CI $307.31-$439.41) per person per year less than individuals living in the bottom or least green decile. Sensitivity analyses including tree canopy cover as the green space measure yielded similar findings. Analyses that included adjustment for comorbidity were consistent with the hypothesis that green cover reduces healthcare costs by improving health status.

CONCLUSION

Green cover was associated with lower direct healthcare costs, raising the possibility that residential greening can have a significant healthcare cost impact across the population.

Residential Exposure to Urban Trees and Medication Sales for Mood Disorders and Cardiovascular Disease in Brussels, Belgium: An Ecological Study

BACKGROUND

The available evidence for positive associations between urban trees and human health is mixed, partly because the assessment of exposure to trees is often imprecise because of, for instance, exclusion of trees in private areas and the lack of three-dimensional (3D) exposure indicators (e.g., crown volume).

OBJECTIVES

We aimed to quantify all trees and relevant 3D structural traits in Brussels (Belgium) and to investigate associations between the number of trees, tree traits, and sales of medication commonly prescribed for mood disorders and cardiovascular disease.

METHODS

We developed a workflow to automatically isolate all individual trees from airborne light detection and ranging (LiDAR) data collected in 2012. Trait data were subsequently extracted for 309,757 trees in 604 census tracts. We used the average annual age-standardized rate of medication sales in Brussels for the period 2006 to 2014, calculated from reimbursement information on medication prescribed to adults (19-64 years of age). The medication sales data were provided by sex at the census tract level. Generalized log-linear models were used to investigate associations between the number of trees, the crown volume, tree structural variation, and medication sales. Models were run separately for mood disorder and cardiovascular medication and for men and women. All models were adjusted for indicators of area-level socioeconomic status.

RESULTS

Single-factor models showed that higher stem densities and higher crown volumes are both associated with lower medication sales, but opposing associations emerged in multifactor models. Higher crown volume [an increase by one interquartile range (IQR) of 1.4×104 m³/ha] was associated with 34% lower mood disorder medication sales [women, β=-0.341 (95% CI: -0.379, -0.303); men, β=-0.340 (95% CI: -0.378, -0.303)] and with 21-25% lower cardiovascular medication sales [women, β=-0.214 (95% CI: -0.246, -0.182); men, β=-0.252 (95% CI: -0.285, -0.219)]. Conversely, a higher stem density (an increase by one IQR of 21.8 trees/ha) was associated with 28-32% higher mood disorder medication sales [women, β=0.322 (95% CI: 0.284, 0.361); men, β=0.281 (95% CI: 0.243, 0.319)] and with 20-24% higher cardiovascular medication sales [women, β=0.202 (95% CI: 0.169, 0.236); men, β=0.240 (95% CI: 0.206, 0.273)].

DISCUSSION

We found a trade-off between the number of trees and the crown volumes of those trees for human health benefits in an urban environment. Our results demonstrate that conserving large trees in urban environments may not only support conservation of biodiversity but also human health. https://doi.org/10.1289/EHP9924.

Analysis of Urban Built Environment Impacts on Outdoor Physical Activities-A Case Study in China

Outdoor physical activities can promote public health and they are largely influenced by the built environment in different urban settings. Understanding the association between outdoor physical activities and the built environment is important for promoting a high quality of life. Existing studies typically focus on one type of outdoor activity using interview-based small samples and are often lack of systematic understanding of the activities' intensity and frequency. In this study, we intend to gain deeper insight into how the built environment influences physical activities using the data extracted from individual's wearables and other open data sources for integrated analysis. Multi-linear regression with logarithm transformation is applied to perform the analysis using the data from Changsha, China. We found that built environment impacts on outdoor physical activities in Changsha are not always consistent with similar studies' results in other cities. The most effective measures to promote outdoor physical activities are the provision of good arterial and secondary road networks, community parks, among others in Changsha. The results shed light on future urban planning practices in terms of promoting public health.

Effect of Urban Green Space in the Hilly Environment on Physical Activity and Health Outcomes: Mediation Analysis on Multiple Greenery Measures

Background: Green spaces reduce the risk of multiple adverse health outcomes by encouraging physical activity. This study examined correlations between urban green space and residents’ health outcomes in hilly neighborhoods: if they are mediated by social cohesion, visual aesthetics, and safety. Methods: We used multiple green space indicators, including normalized difference vegetation index (NDVI) extracted from satellite imagery, green view index (GVI) obtained from street view data using deep learning methods, park availability, and perceived level of greenery. Hilly terrain was assessed by the standard deviation of the elevation to represent variations in slope. Resident health outcomes were quantified by their psychological and physiological health as well as physical activity. Communities were grouped by quartiles of slopes. Then a mediation model was applied, controlling for socio-demographic factors. Results: Residents who perceived higher quality greenery experienced stronger social cohesion, spent more time on physical activity and had better mental health outcomes. The objective greenery indicators were not always associated with physical activity and might have a negative influence with certain terrain. Conclusions: Perceived green space offers an alternative explanation of the effects on physical activity and mental health in hilly neighborhoods. In some circumstances, geographical environment features should be accounted for to determine the association of green space and resident health outcomes.

Rethinking walkability and developing a conceptual definition of active living environments to guide research and practice

BACKGROUND

Walkability is a popular term used to describe aspects of the built and social environment that have important population-level impacts on physical activity, energy balance, and health. Although the term is widely used by researchers, practitioners, and the general public, and multiple operational definitions and walkability measurement tools exist, there are is no agreed-upon conceptual definition of walkability.

METHOD

To address this gap, researchers from Memorial University of Newfoundland hosted "The Future of Walkability Measures Workshop" in association with researchers from the Canadian Urban Environmental Health Research Consortium (CANUE) in November 2017. During the workshop, trainees, researchers, and practitioners worked together in small groups to iteratively develop and reach consensus about a conceptual definition and name for walkability. The objective of this paper was to discuss and propose a conceptual definition of walkability and related concepts.

RESULTS

In discussions during the workshop, it became clear that the term walkability leads to a narrow conception of the environmental features associated with health as it inherently focuses on walking. As a result, we suggest that the term Active Living Environments, as has been previously proposed in the literature, are more appropriate. We define Active Living Environments (ALEs) as the emergent natural, built, and social properties of neighbourhoods that promote physical activity and health and allow for equitable access to health-enhancing resources.

CONCLUSIONS

We believe that this broader conceptualization allows for a more comprehensive understanding of how built, natural, and social environments can contribute to improved health for all members of the population.

Residential proximity to greenness and adverse birth outcomes in urban areas: Findings from a national Canadian population-based study

BACKGROUND

Over the last decade, several studies have reported that residential proximity to vegetation, or 'greenness', is associated with improved birth outcomes, including for term birth weight (TBW), preterm birth (PTB), and small for gestational age (SGA). However, there remain several uncertainties about these possible benefits including the role of air pollution, and the extent to they are influenced socioeconomic status.

METHODS

We addressed these gaps using a national population-based study of 2.2 million singleton live births in Canadian metropolitan areas between 1999 and 2008. Exposures to greenness, fine particulate matter (PM_2.5), and nitrogen dioxide (NO₂) were assigned to infants using the postal code of their mother's residence at the time of birth. The Normalized Difference Vegetation Index (NDVI) was used to characterize greenness, while estimates of ambient PM_2.5 and NO₂ were estimated using remote sensing, and a national land-use regression surface, respectively. Multivariable regression analysis was performed to describe associations between residential greenness and the birth outcomes. Stratified analyses explored whether these associations were modified by neighbourhood measures of socioeconomic status.

RESULTS

Mothers who lived in greener areas had a lower risk of low TBW, PTB, and SGA babies. These associations persisted after adjustment for ambient NO₂ and PM_2.5. Specifically, in fully adjusted models, an interquartile range (IQR = 0.16) increase in the NDVI within a residential buffer of 250 m yielded odds ratios of 0.93 (95% confidence interval (CI): 0.92, 0.94), 0.94 (95% CI: 0.92, 0.95), and 0.94 (95% CI: 0.93, 0.95) for the outcomes of PTB, low TBW, and SGA, respectively. Similarly, an IQR increase in greenness was associated with a 16.3 g (95% CI: 15.3, 17.4) increase in TBW. We found inverse associations between greenness and the occurrence of adverse birth outcomes regardless of the socioeconomic status of the neighbourhood.

INTERPRETATION

Our findings support the hypothesis that residential greenness contributes to healthier pregnancies, that these associations are independent from exposure to air pollution. , and that proximity to greenness benefits all mothers regardless of socioeconomic status.

Spatial resolution of Normalized Difference Vegetation Index and greenness exposure misclassification in an urban cohort

BACKGROUND

The Normalized Difference Vegetation Index (NDVI) is a measure of greenness widely used in environmental health research. High spatial resolution NDVI has become increasingly available; however, the implications of its use in exposure assessment are not well understood.

OBJECTIVE

To quantify the impact of NDVI spatial resolution on greenness exposure misclassification.

METHODS

Greenness exposure was assessed for 31,328 children in the Greater Boston Area in 2016 using NDVI from MODIS (250 m2), Landsat 8 (30 m2), Sentinel-2 (10 m2), and the National Agricultural Imagery Program (NAIP, 1 m2). We compared continuous and categorical greenness estimates for multiple buffer sizes under a reliability assessment framework. Exposure misclassification was evaluated using NAIP data as reference.

RESULTS

Greenness estimates were greater for coarser resolution NDVI, but exposure distributions were similar. Continuous estimates showed poor agreement and high consistency, while agreement in categorical estimates ranged from poor to strong. Exposure misclassification was higher with greater differences in resolution, smaller buffers, and greater number of exposure quantiles. The proportion of participants changing greenness quantiles was higher for MODIS (11-60%), followed by Landsat 8 (6-44%), and Sentinel-2 (5-33%).

SIGNIFICANCE

Greenness exposure assessment is sensitive to spatial resolution of NDVI, aggregation area, and number of exposure quantiles. Greenness exposure decisions should ponder relevant pathways for specific health outcomes and operational considerations.

Assessing and Qualifying Neighborhood Walkability for Older Adults: Construction and Initial Testing of a Multivariate Spatial Accessibility Model

Population aging and urban development pose major challenges for societies today. Joining the literature assessing urban accessibility, the present exploratory research developed a multivariate accessibility model based on four independent variables—related to formal and structural urban space—that influence walkability for older adults (pedestrian network; facilities and shops; public benches; and slopes and gradients). The model used ArcGIS software. For the accessibility calculations, we selected two suburban neighborhoods in the conurbation of Grenoble (France) and selected three types of older adults’ profiles to reflect the variety of aging: an older adult in good health, an older adult with a chronic disease, and an older adult with reduced mobility. The results suggest that the accessibility of a neighborhood depends not only on its physical and urban characteristics, but it is also influenced by the physical and health characteristics of its inhabitants. The originality of the model lies mainly in its ability to estimate the spatial accessibility of a territory by taking into account, firstly, objective data such as the physical characteristics and the built environment of the neighborhood through objectification variables that consider such original variables as the presence of benches or the slopes and gradients and, secondly, specific data such as the physical and/or health characteristics of the study population. The measurement of geospatial accessibility could be of great value for public health in urban contexts, which is why relevant tools and methodologies are needed to objectively examine and intervene in public spaces in order to make them age-friendly.

Leisure sedentary time and physical activity are higher in neighbourhoods with denser greenness and better built environments: An analysis of the Canadian Longitudinal Study on Aging

Associations of environmental variables with physical activity and sedentary time using data from the Canadian Longitudinal Study on Aging, and the Canadian Urban Environment Research Consortium (Canadian Active Living Environments (Can-ALE) dataset, and Normalized Difference Vegetation Index (NDVI, greenness) dataset) were assessed. The main outcome variables were physical activity and sedentary time as measured by a modified version of the Physical Activity for Elderly Scale. The sample consisted of adults aged 45 and older (n = 36,580, mean age 62.6±10.2, 51% female). Adjusted ordinal regression models consistently demonstrated that those residing in neighbourhoods in the highest Can-ALE category (most well-connected built environment) reported more physical activity and sedentary time. For example, males aged 75+ in the highest Can-ALE category had 1.9 times higher odds of reporting more physical activity (OR = 1.9, 95%CI = 1.1-3.4) and 1.8 higher odds of reporting more sedentary time (OR = 1.8, 95%CI = 1.0-3.4). Neighbourhoods with higher greenness scores were also associated with higher odds of reporting more physical activity and sedentary time. It appears that an environment characterized by higher Can-ALE and higher greenness may facilitate physical activity, but it also facilitates more leisure sedentary time in older adults; research using device measured total sedentary time, and consideration of the types of sedentary activities being performed is needed. Novelty: ●Middle-aged and older adults living in neighbourhoods with higher Can-ALE scores and more greenness report more physical activity and leisure sedentary time ●Greenness is important for physical activity and sedentary time in middle-aged adults.

Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index

Limitations of Normalized Difference Vegetation Index (NDVI) potentially contributed to the inconsistent findings of greenspace exposure and childhood asthma. The aim of this study was to use a novel greenness exposure assessment method, capable of overcoming the limitation of NDVI to determine the extent to which it was associated with asthma prevalence in Chinese children. During 2009-2013, a cross-sectional study of 59,754 children aged 2-17 years was conducted in northeast China. Tencent street view images surrounding participants' schools were segmented by a deep learning model, and streetscape greenness was extracted. The green view index (GVI) was used to assign exposure and higher value indicates more green coverage. Mixed-effects logistic regression models were used to calculate the adjusted odds of asthma per interquartile range (IQR) increase of GVI for trees and grass. Participants were further stratified to investigate whether particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) was a modifier. An IQR increase in GVI_800m for trees was associated with lower adjusted odds of doctor-diagnosed asthma (OR: 0.76; 95%CI: 0.72-0.80) and current asthma (OR: 0.82; 95%CI: 0.75-0.89). An IQR increase in GVI_800m for grass was associated with higher adjusted odds of doctor-diagnosed asthma (OR: 1.04; 95%CI: 1.00-1.08) and current asthma (OR: 1.08; 95%CI: 1.02-1.14). After stratification by PM_2.5 exposure level, the negative association between trees and asthma, and the positive association between grass and asthma were observed only in low PM_2.5 exposure levels (≤median: 56.23 μg/m³). Our results suggest that types of vegetation may play a role in the association between greenness exposure and childhood asthma. Exposure to trees may reduce the odds of childhood asthma, whereas exposure to grass may increase the odds. Additionally, PM_2.5 may modify the associations of trees and grass with childhood asthma.

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

BACKGROUND

Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.

OBJECTIVES

This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.

METHODS

SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.

RESULTS

The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.

CONCLUSION

Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

BACKGROUND

Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.

OBJECTIVES

This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.

METHODS

SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.

RESULTS

The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.

CONCLUSION

Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical Considerations

The release of Google Street View in 2007 inspired several new panoramic street-level imagery platforms including Apple Look Around, Bing StreetSide, Baidu Total View, Tencent Street View, Naver Street View, and Yandex Panorama. The ever-increasing global capture of cities in 360° provides considerable new opportunities for data-driven urban research. This paper provides the first comprehensive, state-of-the-art review on the use of street-level imagery for urban analysis in five research areas: built environment and land use; health and wellbeing; natural environment; urban modelling and demographic surveillance; and area quality and reputation. Panoramic street-level imagery provides advantages in comparison to remotely sensed imagery and conventional urban data sources, whether manual, automated, or machine learning data extraction techniques are applied. Key advantages include low-cost, rapid, high-resolution, and wide-scale data capture, enhanced safety through remote presence, and a unique pedestrian/vehicle point of view for analyzing cities at the scale and perspective in which they are experienced. However, several limitations are evident, including limited ability to capture attribute information, unreliability for temporal analyses, limited use for depth and distance analyses, and the role of corporations as image-data gatekeepers. Findings provide detailed insight for those interested in using panoramic street-level imagery for urban research.

"Biophilic Cities": Quantifying the Impact of Google Street View-Derived Greenspace Exposures on Socioeconomic Factors and Self-Reported Health

According to the biophilia hypothesis, humans have evolved to prefer natural environments that are essential to their thriving. With urbanization occurring at an unprecedented rate globally, urban greenspace has gained increased attention due to its environmental, health, and socioeconomic benefits. To unlock its full potential, an increased understanding of greenspace metrics is urgently required. In this first-of-a-kind study, we quantified street-level greenspace using 751 644 Google Street View images and computer vision methods for 125 274 locations in Ireland's major cities. We quantified population-weighted exposure to greenspace and investigated the impact of greenspace on health and socioeconomic determinants. To investigate the association between greenspace and self-reported health, a negative binomial regression analysis was applied. While controlling for other factors, an interquartile range increase in street-level greenspace was associated with a 2.78% increase in self-reported "good or very good" health [95% confidence interval: 2.25-3.31]. Additionally, we observed that populations in upper quartiles of greenspace exposure had higher levels of income and education than those in lower quartiles. This study provides groundbreaking insights into how urban greenspace can be quantified in unprecedented resolution, accuracy, and scale while also having important implications for urban planning and environmental health research and policy.

Understanding the Differential Impact of Vegetation Measures on Modeling the Association between Vegetation and Psychotic and Non-Psychotic Disorders in Toronto, Canada

Considerable debate exists on whether exposure to vegetation cover is associated with better mental health outcomes. Past studies could not accurately capture people's exposure to surrounding vegetation and heavily relied on non-spatial models, where the spatial autocorrelation and latent covariates could not be adjusted. Therefore, a suite of five different vegetation measures was used to separately analyze the association between vegetation cover and the number of psychotic and non-psychotic disorder cases in the neighborhoods of Toronto, Canada. Three satellite-based and two area-based vegetation measures were used to analyze these associations using Poisson lognormal models under a Bayesian framework. Healthy vegetation cover was found to be negatively associated with both psychotic and non-psychotic disorders. Results suggest that the satellite-based indices, which can measure both the density and health of vegetation cover and are also adjusted for urban and environmental perturbations, could be better alternatives to simple ratio- and area-based measures for understanding the effect of vegetation on mental health. A strong dominance of spatially structured latent covariates was found in the models, highlighting the importance of adopting a spatial approach. This study can provide critical guidelines for selecting appropriate vegetation measures and developing spatial models for future population-based epidemiological research.

Near Real-Time Semantic View Analysis of 3D City Models in Web Browser

3D city models and their browser-based applications have become an increasingly applied tool in the cities. One of their applications is the analysis views and visibility, applicable to property valuation and evaluation of urban green infrastructure. We present a near real-time semantic view analysis relying on a 3D city model, implemented in a web browser. The analysis is tested in two alternative use cases: property valuation and evaluation of the urban green infrastructure. The results describe the elements visible from a given location, and can also be applied to object type specific analysis, such as green view index estimation, with the main benefit being the freedom of choosing the point-of-view obtained with the 3D model. Several promising development directions can be identified based on the current implementation and experiment results, including the integration of the semantic view analysis with virtual reality immersive visualization or 3D city model application development platforms.

Neighborhood Urban Design and Outdoor Later Life: An Objective Assessment of Out-of-Home Time and Physical Activity Among Older Adults in Barcelona

This study explores how older adults' time out-of-home and physical activity (PA) are associated with the provision of urban open spaces (green spaces, plazas, and boulevards) and microelements (street trees and benches) in their neighborhoods. The authors used data from 103 residents in Barcelona and matched it to official geospatial data. The authors adjusted a set of mixed-effects linear regressions, both for the entire sample and also stratified by age and gender. For the entire sample, the percentage of green spaces showed a positive association with neighborhood time out-of-home and PA, while participants' PA also showed a positive association with the presence of benches. Outdoor time among older women was not associated with any of the measured exposures. For men, the provision of green spaces and benches was positively associated with time out-of-home and PA. These results could inform the design of urban spaces that aim to encourage outdoor activity among older adults.

Are school-based measures of walkability and greenness associated with modes of commuting to school? Findings from a student survey in Ontario, Canada

OBJECTIVES

In Canada, students are increasingly reliant on motorized vehicles to commute to school, and few meet the recommended overall physical activity guidelines. Infrastructure and built environments around schools may promote active commuting to and from school, thereby increasing physical activity. To date, few Canadian studies have examined this research question.

METHODS

This study is a cross-sectional analysis of 11,006 students, aged 11-20, who participated in the 2016/2017 Ontario Student Drug Use and Health Survey. The remote sensing-derived Normalized Difference Vegetation Index (NDVI), at a buffer of 500 m from the schools' locations, was used to characterize greenness, while the 2016 Canadian Active Living Environments (Can-ALE) measure was used for walkability. Students were asked about their mode of regular commuting to school, and to provide information on several socio-demographic variables. Multivariable logistic regression models were used to quantify associations between active commuting and greenness and the Can-ALE. The resulting odds ratios, and their 95% confidence intervals, were adjusted for a series of risk factors that were collected from the survey.

RESULTS

Overall, 21% of students reported active commuting (biking or walking) to school, and this prevalence decreased with increasing age. Students whose schools had higher Can-ALE scores were more likely to be active commuters. Specifically, the adjusted odds ratio (OR) of being an active commuter for schools in the highest quartile of the Can-ALE was 2.11 (95% CI = 1.64, 2.72) when compared with those in the lowest. For children, aged 11-14 years, who attended schools in high dwelling density areas, a higher odds of active commuting was observed among those in the upper quartile of greenness relative to the lowest (OR = 1.41; 95% CI = 0.92, 2.15). In contrast, for lower dwelling density areas, greenness was inversely associated with active commuting across all ages.

CONCLUSION

Our findings suggest that students attending schools with higher Can-ALE scores are more likely to actively commute to school, and that positive impacts of greenness on active commuting are evident only in younger children in more densely populated areas. Future studies should collect more detailed data on residential measures of the built environment, safety, distance between home and school, and mixed modes of commuting behaviours.

Evaluating Street Greenery by Multiple Indicators Using Street-Level Imagery and Satellite Images: A Case Study in Nanjing, China

Street greenery plays an essential role in improving the street environment and residents’ health. The evaluation of street greenery is of great value to establish environmentally friendly streets. The evaluation indicators of present studies evaluating street greenery were relatively single, either the Green View Index (GVI) or Normalized Difference Vegetation Index (NDVI), which cannot describe the greenery condition in its entirety. The objective of this study is to assess the street greenery using multiple indicators, including GVI, NDVI, and Vegetation Structural Diversity (VSD). We combined street view images with a semantic segmentation method to extract the GVI and VSD and used satellite images to calculate the NDVI in the urban area of Nanjing, China. We found correlations and discrepancies of these indicators using statistical analyses in different urban districts, functional areas, and road levels. The results indicate that: (1) the GVI and NDVI are strongly correlated in open spaces, whereas weakly correlated in residential and industrial lands, (2) the areas with higher VSD are mainly located in the new city, whereas the VSD in the old city is lower, and a weak negative correlation exists between the GVI and VSD in the research area, and (3) the old city has a higher GVI level compared to the new city on the main road, whereas the new city has a higher GVI level than the old city on the branch road. Compared with the GVI, the trend of VSD in the old city and the new city is relatively consistent. Our findings suggest that considering multiple indicators of street greenery evaluation can provide a comprehensive reference for building more human-friendly and diversified street green belts.

Objective scoring of streetscape walkability related to leisure walking: Statistical modeling approach with semantic segmentation of Google Street View images

Although the pedestrian-friendly qualities of streetscapes promote walking, quantitative understanding of streetscape functionality remains insufficient. This study proposed a novel automated method to assess streetscape walkability (SW) using semantic segmentation and statistical modeling on Google Street View images. Using compositions of segmented streetscape elements, such as buildings and street trees, a regression-style model was built to predict SW, scored using a human-based auditing method. Older female active leisure walkers living in Bunkyo Ward, Tokyo, are associated with SW scores estimated by the model (OR = 3.783; 95% CI = 1.459 to 10.409), but male walkers are not.

Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation

Urban greenery is considered an important factor in sustainable development and people’s quality of life in the city. To account for urban green vegetation, Green View Index (GVI), which captures the visibility of greenery at street level, has been used. However, as GVI is point-based estimation, when aggregated at an area-level by mean or median, it is sensitive to the location of sampled sites, overweighing the values of densely located sites. To make estimation at area-level more robust, this study aims to (1) propose an improved indicator of greenery visibility (standardized GVI; sGVI), and (2) quantify the relation between sGVI and other green metrics. Experiment on an hypothetical setting confirmed that bias from site location can be mitigated by sGVI. Furthermore, comparing sGVI and Normalized Difference Vegetation Index (NDVI) at the city block level in Yokohama city, Japan, we found that sGVI captures the presence of vegetation better in the city center, whereas NDVI is better at capturing vegetation in parks and forests, principally due to the different viewpoints (eye-level perception and top-down eyesight). These tools provide a foundation for accessing the effect of vegetation in urban landscapes in a more robust matter, enabling comparison on any arbitrary geographical scale.

Does Green Space Really Matter for Residents' Obesity? A New Perspective From Baidu Street View

Despite a growing literature on the topic, the association between neighborhood greenness and body weight is inconsistent. The objective of this research is to examine the association between neighborhood greenness and residents' obesity levels in a high population density area. We accounted for three greenness features: green access, green exposure, and view-based green index. We used the novel technique of deep convolutional neural network architecture to extract eye-level information from Baidu Street View images to capture the urban vertical greenness level. The research involved a survey with 9,524 respondents from 40 communities in Shanghai. Generally, we found all aspects of horizontal greenery, vertical greenery, and proximity of green levels to be impactful on body weight; however, only the view-based green index consistently had an adverse effect on weight and obesity.

Residential Greenness Alters Serum 25(OH)D Concentrations: A Longitudinal Cohort of Chinese Older Adults

Objectives

Vitamin D deficiency is prevalent among older adults. We aimed to study whether residential greenness could alter serum 25(OH)D concentrations as a possible mechanism of residential greenness's positive health effects.

Design

A longitudinal cohort study.

Setting and Participants

We included older adults aged ≥65 years from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) with follow-up between 2012 and 2014.

Methods

We measured residential greenness by calculating annual average Normalized Difference Vegetation Index (NDVI) in a 500 m radius by using satellite images around each participant's residential address. Serum 25-hydroxyvitamin D (25(OH)D) concentration was dichotomized into 2 categories: nondeficiency (≥50 nmol/L) and deficiency (<50 nmol/L). We used the generalized estimating equation to examine the relationship between annual average NDVI and serum 25(OH)D.

Results

We included 1336 participants in our analysis. The annual average NDVI was 0.49, and mean serum 25(OH)D was 43 nmol/L at baseline. Each 0.1-unit increase in annual average NDVI was associated with a 13% higher odds of vitamin D nondeficiency [95% confidence interval (CI): 1.01, 1.26]. The association was stronger among men [odds ratio (OR): 1.17, 95% CI: 1.02, 1.35] than women (OR: 1.08, 95% CI: 0.91, 1.29) and also stronger among those who were free of activities of daily living (ADL) disability at baseline (OR: 1.12, 95% CI: 1.00, 1.25). During the follow-up period, the participants who lived in greener areas were more likely to have an improved, rather than stable or deteriorated, vitamin D status (OR: 1.94, 95% CI: 1.51, 2.51).

Conclusions and Implications

Our study suggests that higher levels of residential greenness are associated with higher serum 25(OH)D concentrations, which has implications for prevention of vitamin D deficiency among older adults.

The Relationship Between Green Space and Prosocial Behaviour Among Children and Adolescents: A Systematic Review

The plausible role of nearby green space in influencing prosocial behaviour among children and adolescents has been studied recently. However, no review has been conducted of the evidence testing the association between green space and prosocial behaviour. This systematic review addresses this gap among children and adolescents. Within this review, we propose a conceptual framework describing potential pathways linking green space to prosocial behaviour, discuss the direction, magnitude, moderators, and mediators of the association, and develop a narrative synthesis of future study directions. Out of 63 extracted associations from 15 studies, 44 were in the positive or expected direction, of which 18 were reported to be statistically significant (p < 0.05). Overall, the current evidence shows that exposure to green space may potentially increase prosocial behaviour among children and adolescents, with some contingencies (e.g., child's sex and ethnic background). However, the volume and quality of this evidence is not yet sufficient to draw conclusions on causality. Further, heterogeneity in the indicators of green space exposure could lead to mixed findings. In addition, none of the included studies investigated potential mediators. Nevertheless, this review provides preliminary evidence and a basis for further investigation with rigorous study methodology capable of drawing causal inferences and testing potential effect modifiers, linking pathways, and relevant green space measures.

Residential greenness, air pollution and psychological well-being among urban residents in Guangzhou, China

China's rapid urbanization has led to an increasing level of exposure to air pollution and a decreasing level of exposure to vegetation among urban populations. Both trends may pose threats to psychological well-being. Previous studies on the interrelationships among greenness, air pollution and psychological well-being rely on exposure measures from remote sensing data, which may fail to accurately capture how people perceive vegetation on the ground. To address this research gap, this study aimed to explore relationships among neighbourhood greenness, air pollution exposure and psychological well-being, using survey data on 1029 adults residing in 35 neighbourhoods in Guangzhou, China. We used the Normalized Difference Vegetation Index (NDVI) and streetscape greenery (SVG) to assess greenery exposure at the neighbourhood level, and we distinguished between trees (SVG-tree) and grasses (SVG-grass) when generating streetscape greenery exposure metrics. We used two objective (PM_2.5 and NO₂ concentrations) measures and one subjective (perceived air pollution) measure to quantify air pollution exposure. We quantified psychological well-being using the World Health Organization Well-Being Index (WHO-5). Results from multilevel structural equation models (SEM) showed that, for parallel mediation models, while the association between SVG-grass and psychological well-being was completely mediated by perceived air pollution and NO₂, the relationship between SVG-tree and psychological well-being was completely mediated by ambient PM_2.5, NO₂ and perceived air pollution. None of three air pollution indicators mediated the association between psychological well-being and NDVI. For serial mediation models, measures of air pollution did not mediate the relationship between NDVI and psychological well-being. While the linkage between SVG-grass and psychological well-being scores was partially mediated by NO₂-perceived air pollution, SVG-tree was partially mediated by both ambient PM_2.5-perceived air pollution and NO₂-perceived air pollution. Our results suggest that street trees may be more related to lower air pollution levels and better mental health than grasses are.

Physical Activity in the Summer Heat: How Hot Weather Moderates the Relationship Between Built Environment Features and Outdoor Physical Activity of Adults

BACKGROUND

Research has not yet examined how hot weather moderates the relationship between the built environment and outdoor physical activity levels. The authors posited that hot days will increase the magnitude of the expected directional effect of built environment features on physical activity.

METHODS

This longitudinal study included 134 US adults from the Three city Heat and Electrical failure AdapTation study. Adults self-reported physical activity for multiple summer days (nstudy-days = 742) in 2016. Hot days were defined as ≥90th percentile of daily maximum heat index. Built environment features included density, safety, trees, hilliness, connectivity, access to parks, and access to shops + services. Separate growth curve models with interaction terms (ie, hot day × built environment feature) were run for daily minutes of outdoor physical activity (ie, any activity and recommended activity).

RESULTS

Neither hot days nor built environment features impacted outdoor physical activity significantly, and hot days did not moderate the relationship between built environment features and physical activity (P > .05).

CONCLUSIONS

With adults failing to modify behavior on hot days, cities may be placing adults at increased risk of exertional heat illness. The authors recommend incorporating the risk of exertional heat illness in health impact assessments and deploying heat management strategies.

Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review

BACKGROUND

There is an increasing volume of literature investigating the links between urban environments and human health, much of which involves spatial conceptualisations and research designs involving various aspects of geographical information science. Despite intensifying research interest, there has been little systematic investigation of pragmatic methodological concerns, such as how studies are realised in terms of the types of data that are gathered and the analytical techniques that are applied, both of which have the potential to impact results. The aim of this systematic review is, therefore, to understand how spatial scale, datasets, methods, and analytics are currently applied in studies investigating the relationship between green and blue spaces and human health in urban areas.

METHOD

We systematically reviewed 93 articles following PRISMA protocol, extracted information regarding different spatial dimensions, and synthesised them in relation to various health indicators.

RESULTS AND DISCUSSION

We found a preponderance of the use of neighbourhood-scale in these studies, and a majority of the studies utilised land-use and vegetation indices gleaned from moderate resolution satellite imagery. We also observed the frequent adoption of fixed spatial units for measuring exposure to green and blue spaces based on physical proximity, typically ranging between 30 and 5000 m. The conceptual frameworks of the studies (e.g., the focus on physical vs. mental health or the definition of exposure to green space) were found to have an influence on the strength of association between exposure and health outcomes. Additionally, the strength and significance of associations also varied by study design, something which has not been considered systematically.

CONCLUSION

On the basis of our findings, we propose a set of recommendations for standardised protocols and methods for the evaluation of the impact of green-blue spaces on health. Our analysis suggests that future studies should consider conducting analyses at finer spatial scales and employing multiple exposure assessment methods to achieve a comprehensive and comparable evaluation of the association between greenspace and health along multiple pathways.

Residential greenness and increased physical activity in patients after coronary artery bypass graft surgery

Aims

Physical activity is a fundamental component of rehabilitation following coronary artery bypass (CABG) surgery. Proximity to neighbourhood green spaces may encourage physical activity. We investigated the association between residential greenness and exercise-related physical activity post-CABG surgery.

Methods

Participants in a prospective cohort study of 846 patients (78% men) who underwent CABG surgery at seven cardiothoracic units during the time period 2004–2007 were interviewed regarding their physical activity habits one day before and one year after surgery. Exposure to residential neighbourhood greenness (within a 300 m buffer around their place of residence) was measured using the Normalized Difference Vegetative Index. Participation in exercise-related physical activity (yes/no), weekly duration of exercise-related physical activity and the change in exercise-related physical activity between baseline and follow-up were examined for associations with residential greenness, adjusting for socio-demographic factors, propensity score adjusted participation in cardiac rehabilitation and health-related covariates after multiple imputation for missing variables.

Results

Living in a higher quartile of residential greenness was associated with a 52% greater odds of being physically active (OR 1.52, 95% CI 1.22–1.90). This association persisted only (OR 1.75, 95% CI 1.35–2.27) among patients who did not participate in cardiac rehabilitation following surgery and was stronger in women (OR 2.38, 95% CI 1.40–4.07) than in men (OR 1.37, 95% CI 1.07–1.75). Participants who lived in greener areas were more likely to increase their post-surgical physical activity than those who lived in less green areas (OR 1.59, 95% CI 1.25–2.01).

Conclusions

Residential greenness appears to be beneficial in increasing exercise-related physical activity in cardiac patients, especially those not particpating in cardiac rehabilitation after CABG surgery.

Assessing the Impact of School-Based Greenness on Mental Health Among Adolescent Students in Ontario, Canada

Neighbourhood greenness has been frequently associated with improved mental health in adulthood, yet its impact among youth is less clear. Additionally, though youth spend large portions of time at school, no study has investigated associations between school-based measures of greenness and students’ mental health in Canada. We addressed this gap by linking participant responses from the 2016–2017 Ontario Student Drug Use and Health Survey to school-based features of the built environment. Our analyses included 6313 students, ages 11–20. Measures of greenness were the mean and max of the annual mean Normalized Difference Vegetation Index within 500 m and 1000 m from the centroid of the school postal code. Measures of mental health included: serious psychological distress (Kessler 6-item Psychological Distress Scale), self-rated mental health (using a five-point Likert scale), suicide ideation, and suicide attempt. In our study population, the prevalence of serious psychological distress and low self-rated mental health was 16.7% and 20.3%, respectively. Suicide ideation was reported by 13.5% of participants, while 3.7% reported a suicide attempt. Quantity of greenness was similar between schools in the lower and upper quartiles. In logistic regressions, we found no association between objective school-based greenness and mental health, as assessed by multiple measures, both before and after adjustment. Null findings held true after stratification by season, as well. Whether other characteristics of school greenness (such as type, quality, or access and use) are more impactful to students’ mental health should be a focus of future analyses.

Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures

BACKGROUND

Multiple mechanisms have been proposed to explain how greenery in the vicinity of people's homes enhances their mental health and wellbeing. Mediation studies, however, focus on a limited number of mechanisms and rely on remotely sensed greenery measures, which do not accurately capture how neighborhood greenery is perceived on the ground.

OBJECTIVE

To examine: 1) how streetscape and remote sensing-based greenery affect people's mental wellbeing; 2) whether and, if so, to what extent the associations are mediated by physical activity, stress, air quality and noise, and social cohesion; and 3) whether differences in the mediation across the streetscape greenery and NDVI exposure metrics occurred.

METHODS

We used a population sample of 1029 adult residents of the metropolis of Guangzhou, China, from 2016. Mental wellbeing was quantified by the World Health Organization Well-Being Index (WHO-5). Two objective greenery measures were extracted at the neighborhood level: 1) streetscape greenery from street view data via a convolutional neural network, and 2) the normalized difference vegetation index (NDVI) from Landsat 8 remote sensing images. Single and multiple mediation analyses with multilevel regressions were conducted.

RESULTS

Streetscape and NDVI greenery were weakly and positively, but not significantly, correlated. Our regression results revealed that streetscape greenery and NDVI were, individually and jointly, positively associated with mental wellbeing. Significant partial mediators for the streetscape greenery were physical activity, stress, air quality and noise, and social cohesion; together, they explained 62% of the association. For NDVI, only physical activity and social cohesion were significant partial mediators, accounting for 22% of the association.

CONCLUSIONS

Mental health and wellbeing and both streetscape and satellite-derived greenery seem to be both directly correlated and indirectly mediated. Our findings signify that both greenery measures capture different aspects of natural environments and may contribute to people's wellbeing by means of different mechanisms.

Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China

BACKGROUND

Residential green and blue spaces may be therapeutic for the mental health. However, solid evidence on the linkage between exposure to green and blue spaces and mental health among the elderly in non-Western countries is scarce and limited to exposure metrics based on remote sensing images (i.e., land cover and vegetation indices). Such overhead-view measures may fail to capture how people perceive the environment on the site.

OBJECTIVE

This study aimed to compare streetscape metrics derived from street view images with satellite-derived ones for the assessment of green and blue space; and to examine associations between exposure to green and blue spaces as well as geriatric depression in Beijing, China.

METHODS

Questionnaire data on 1190 participants aged 60 or above were analyzed cross-sectionally. Depressive symptoms were assessed through the shortened Geriatric Depression Scale (GDS-15). Streetscape green and blue spaces were extracted from Tencent Street View data by a fully convolutional neural network. Indicators derived from street view images were compared with a satellite-based normalized difference vegetation index (NDVI), a normalized difference water index (NDWI), and those derived from GlobeLand30 land cover data on a neighborhood level. Multilevel regressions with neighborhood-level random effects were fitted to assess correlations between GDS-15 scores and these green and blue spaces exposure metrics.

RESULTS

The average cumulative GDS-15 score was 3.4 (i.e., no depressive symptoms). Metrics of green and blue space derived from street view images were not correlated with satellite-based ones. While NDVI was highly correlated with GlobeLand30 green space, NDWI was moderately correlated with GlobeLand30 blue space. Multilevel regressions showed that both street view green and blue spaces were inversely associated with GDS-15 scores and achieved the highest model goodness-of-fit. No significant associations were found with NDVI, NDWI, and GlobeLand30 green and blue space. Our results passed robustness tests.

CONCLUSION

Our findings provide support that street view green and blue spaces are protective against depression for the elderly in China, yet longitudinal confirmation to infer causality is necessary. Street view and satellite-derived green and blue space measures represent different aspects of natural environments. Both street view data and deep learning are valuable tools for automated environmental exposure assessments for health-related studies.