Characteristics Of Green Spaces Research Articles

Refined Land Use Classification for Urban Core Area from Remote Sensing Imagery by the EfficientNetV2 Model

In the context of accelerated urbanization, assessing the quality of the existing built environment plays a crucial role in urban renewal. In the existing research and use of deep learning models, most categories are urban construction areas, forest land, farmland, and other categories. These categories are not conducive to a more accurate analysis of the spatial distribution characteristics of urban green space, parking space, blue space, and square. A small sample of refined land use classification data for urban built-up areas was produced using remote sensing images. The large-scale remote sensing images were classified using deep learning models, with the objective of inferring the fine land category of each tile image. In this study, satellite remote sensing images of four cities, Handan, Shijiazhuang, Xingtai, and Tangshan, were acquired by Google Class 19 RGB three-channel satellite remote sensing images to establish a data set containing fourteen urban land use classifications. The convolutional neural network model EfficientNetV2 is used to train and validate the network framework that performs well on computer vision tasks and enables intelligent image classification of urban remote sensing images. The model classification effect is compared and analyzed through accuracy, precision, recall, and F1-score. The results show that the EfficientNetV2 model has a classification recognition accuracy of 84.56% on the constructed data set. The testing set accuracy increases sequentially after transfer learning. This paper verifies that the proposed research framework has good practicality and that the results of the land use classification are conducive to the fine-grained quantitative analysis of built-up environmental quality.

Utilizing regression model to characterize the impact of urban green space features on the subjective well-being of older adults

Based on the background of rapid global ageing, research exploring urban green spaces and the subjective well-being of urban residents has become one of the key research interests. However, the evidence for assessing the real benefits of urban green spaces on the subjective well-being of older adults by comprehensively examining the features of urban green spaces is limited. We surveyed older urban green space users (n = 536) aged 60 years and older in Nanjing, China, and evaluated the impacts of spatial, green, and grey features of urban green spaces on older people's overall satisfaction with urban green spaces and subjective well-being. The results of the multiple linear regression model showed a strong association between the three types of urban green space features, overall satisfaction with urban green space, and subjective well-being of older adults. The study results indicated that the grey feature of urban green spaces emerged as the most influential factor (p = 0.004) among the three features of urban green spaces in relation to their effect on the subjective well-being of older adults. This implies that it is essential to focus on the importance of grey s of urban green spaces in the planning and design of urban green spaces for older adults' use of different urban green spaces and their sense of well-being. These results contribute to the development of healthy ageing policies and age-friendly urban green space management strategies in rapid ageing countries around the world.

Urban green spaces, self-rated air pollution and health: A sensitivity analysis of green space characteristics and proximity in four European cities

Exploring the influence of green space characteristics and proximity on health via air pollution mitigation, our study analysed data from 1,365 participants across Porto, Nantes, Sofia, and Høje-Taastrup. Utilizing OpenStreetMap and the AID-PRIGSHARE tool, we generated nine green space indicators around residential addresses at 15 distances, ranging from 100m to 1500m. We performed a mediation analysis for these 135 green space variables and revealed significant associations between self-rated air pollution and self-rated health for specific green space characteristics. In our study, indirect positive effects on health via air pollution were mainly associated with green corridors in intermediate Euclidean distances (800-1,000m) and the amount of accessible green spaces in larger network distances (1,400–1,500m). Our results suggest that the amount of connected green spaces measured in intermediate surroundings seems to be a prime green space characteristic that could drive the air pollution mitigation pathway to health.

Supralocal Role of Medium to Large Scale Urban Parks, in Greater Athens Region in Greece. Issues of Meso Car Dependence During the Covid-19 Pandemic

This paper investigates the supralocal role of medium to large-scale urban parks in Attica, Greece, during the COVID-19 pandemic. The research includes literature review and fieldwork. A statistical sample of 783 individuals was designed to investigate the community’s outlook towards urban parks in Attica. The study establishes a theoretical framework based on the available literature that focuses on factors influencing individuals' willingness to commute so as to reach urban parks. These factors may include household characteristics, green space characteristics, and local accessibility. The paper also examines engaging factors for distant park use, such as relaxation, physical exercise, and social contact. The study adds to existing knowledge on the role of medium to large scale urban parks in Attica, by providing findings on visitation patterns during the pandemic. One of the study’s most important finding is the car dependent travel mode which should be taken into consideration so as to promote sustainable urban mobility.

Nature-based climate shelters? Exploring urban green spaces as cooling solutions for older adults in a warming city

As cities grapple with the escalating challenges of urban heat and its impacts on vulnerable populations, particularly older adults, green spaces are increasingly promoted as effective urban cooling solutions. However, despite the extensive literature on people’s access to and preferences for urban green spaces, little is known about the perception and use of these spaces as nature-based climate shelters on hot days, especially by older adults. This study focuses on Barcelona, a Mediterranean city facing rising temperatures, to explore older adults' patterns of use and preferences for urban green spaces on hot days. The research aims to: 1) analyze heat coping behaviors, emphasizing visits to urban green spaces; 2) identify crucial characteristics of green spaces for older adults; and 3) assess variations in behaviors and preferences based on socio-demographic factors. The study leverages survey data from 291 older adult residents, combining face-to-face and online formats. Results indicate that 54 % of older adults use urban green spaces for cooling on hot days, with preferences for morning or evening visits. Factors influencing non-visitation include perceived lack of thermal comfort, noisiness or crowdedness, and proximity issues. Alternative heat coping strategies include staying at home, traveling to cooler areas outside the city, visiting blue spaces, or air-conditioned indoor facilities. Preferred green space characteristics include abundant shade, leafy vegetation, accessibility factors (e.g., walkability), urban furniture (e.g., benches), and water features. Socio-demographic differences reveal higher green space use among younger age groups and residents in certain districts. Mobility limitations and lower education levels influence preferences, with mobility-challenged individuals prioritizing accessibility aspects. Lower-educated respondents are more likely to report barriers to accessing green spaces. These findings highlight the need for tailored urban planning strategies, considering sociodemographic variations, to mitigate heat-related health risks for older adults. By prioritizing green space accessibility, enhancing its quality, promoting its cooling benefits, addressing cooling inequalities and integrating climate considerations in urban green planning, cities facing increasingly pressing heat challenges can create climate-resilient and inclusive green environments that prioritize the well-being of their aging populations.

Urban green space preferences for various health-related psychological benefits of adolescent pupils, university students and adults

Previous research has shown that biophysical and social characteristics of urban green spaces (UGS) and individual factors may play a role in preferences for restorative environments. However, little is known about the trade-offs between (bio)physical and social characteristics of UGS on different health-related psychological benefits between different population groups. This exploratory study used an image-based discrete choice experiment, in which 298 adolescent pupils, 159 university students, and 142 adults were asked which urban green spaces they would prefer for general recovery, stress reduction, and concentration improvement. Each of the 128 green space scenarios depicted different attributes of varying levels of recreational infrastructure, numbers of trail users, distances between forest vegetation and trail, traffic noise, background scenery, both in winter and in summer. The results indicate that visitor numbers and traffic noise strongly influenced green space preferences for all study groups, but groups differed in terms of their preferences and trade-offs for the psychological benefits. The biophysical environment was less important for adolescent pupils, while recreational infrastructure was less important for the adults. There were group differences in stated preferences for general recovery, stress reduction, and concentration enhancement. Implications for urban green space planning are presented.

GreenR: An open-source framework for quantifying urban greenness

How do we quantify the levels of greenness within urban street networks? Numerous attempts to quantify this factor have been made through survey methodologies, remote sensing data, and street view imagery. The results are promising, but are often limited by scalability constraints, including limited data availability, high requirements of computational power, and validation challenges. This study introduces a comprehensive framework for urban greenness assessment, leveraging open-source data to overcome these limitations. Central to this framework is the development of the greenR R package and an accompanying Shiny app, designed to compute, visualize, and analyze a novel proximity-based green index for individual street segments. Beyond this, the framework innovatively extends its analytical capability by integrating accessibility analysis, Green View Index quantification as well as introducing the Green Space Similarity Index, a novel metric that evaluates and compares the characteristics of urban green spaces across different regions. This extension enriches the proposed framework, providing not just a measure of proximity to green spaces, but also insights into their spatial connectivity and distribution. The efficacy of greenR is demonstrated by studying urban greenness patterns across several cities, highlighting the potential impact of such an open-source framework for citizens, urban planners, and policy-makers. This study not only advances our methodological approach to quantifying urban greenness but also provides practical tools and metrics that can inform sustainable urban planning and policy decisions.

Associations of street-view greenspace with Parkinson’s disease hospitalizations in an open cohort of elderly US Medicare beneficiaries

IntroductionProtective associations of greenspace with Parkinson’s disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). MethodsWe created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007–2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. ResultsThere were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. ConclusionIncreasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Warsaw small and medium-sized public urban green spaces as a response to short-time leisure needs of local residents and passers-by

ABSTRACT The study investigates visitors’ activity patterns in small and medium-sized public urban green spaces (SAMPUGS) and the relationship between the green space features and their use through the field observation of 36 case areas in Warsaw districts – Śródmieście and Ursynów. They were analysed using the perceived sensory dimensions (PSDs) and the Environmental Assessment of Public Recreation Spaces (EAPRS) tool. The green spaces in Ursynów are greener and richer in species than those in Śródmieście, but they lack historical features. The most popular activities are: sitting on a bench, strolling, dog walking, chatting and eating/drinking. Sitting on a bench is encouraged by the presence of historical feature and view outside green space but not by the ‘green ground cover’ or high green coverage ratio. Strolling is promoted in the green spaces of the bigger size and with the developed path network, but also with presence of flowerbeds and those rich in species. Chatting is promoted by the presence of historical features but not by the presence of ‘green ground cover’ and richness in species. Eating/drinking is encouraged by the presence of many places to sit, higher PSD ‘culture’ score, especially in Śródmieście and higher PSD ‘space’ score, especially in Ursynów.

Spatial Gradient Differences in the Cooling Island Effect and Influencing Factors of Urban Park Green Spaces in Beijing

Urban park green spaces exhibit significant cool island effects, which can effectively mitigate the urban heat environment. Clarifying the characteristics and differences in the cool island effects of urban parks across different spatial gradients within cities is instrumental in identifying potential issues and optimizing the structure and resource allocation of park green spaces in a scientifically rational manner. This study focuses on parks within the central urban area of Beijing, utilizing remote sensing image interpretation and land surface temperature (LST) inversion to extract relevant characteristics of park green spaces and the park cool island intensity (PCI) index. Various mathematical and statistical methods including correlation analysis, regression analysis, and cluster analysis are employed to conduct comparative studies across three gradients: within the 3rd ring road, between the 3rd and 5th ring roads, and outside the 5th ring road. The analysis reveals that both park green space characteristics and urban heat island effects exhibit spatial gradient differences, collectively influencing the cool island effects of urban park green spaces. PCI gradually decreases across the three spatial gradients. Cluster analysis identifies four distinct types of parks with different cool island effect characteristics, highlighting the need for optimization and improvement in over half of the parks. Various indicators of park green space characteristics show different correlations with PCI, with variations in correlation strength and thresholds across gradients. The fitting effects of regression equations for each characteristic indicator and PCI gradually worsen from within the 3rd ring road to outside the 5th ring road, with different factors playing important roles across gradients. This study enhances our understanding of the cool island effects of urban park green spaces and facilitates the proposition of differentiated optimization management strategies for urban park green space planning and system construction in different regions.

Associations between green space availability and youth’s physical activity in urban and rural areas across Germany

Green space may be an important physical activity facilitator for children and adolescents. However, to date, most studies focused on urban green space, and few studies investigated associations between green space and physical activity across urban and rural areas, which was the goal of this study. Data were obtained from a German cohort study, including 1,211 youth aged 6–17 years. Residential green space was assessed within a 1000 m street-network buffer using land cover and land use data and divided into quartiles, urbanicity using the European Urbanization Degree, and moderate-to-vigorous physical activity (MVPA) using accelerometers. Associations were investigated via linear regression analysis stratified by urbanicity degree, controlling for relevant confounders. We found that in rural areas, compared to youth in the bottom green quartile, those within the middle (2nd) and upper (3rd) green quartiles engaged in less MVPA. This relationship was not observed for youth in towns and cities. Interactions occurred between green space and socio-demographic characteristics for children and adolescents in cities: Compared to the bottom green quartile, boys or younger children within the middle green quartile engaged in more MVPA. However, socially disadvantaged children and adolescents in cities engaged in less MVPA in the upper compared to the bottom green quartile. Our results show that associations between green space and MVPA do not only differ between urban and rural areas but also among youth sub-populations. These findings are important to consider for planning policies to create inclusive active living environments across urban and rural areas.

How do urban green spaces influence heat-related mortality in elderly? A realist synthesis

BackgroundAn important consequence of climate change for urban health is heat-related mortality. Vulnerable groups, especially elderly, will be the most affected. A solution put forward in many reports and policy documents is the introduction or expansion of urban green spaces. While they have a proven effect in decreasing the ambient temperature and reducing heat related mortality, the causal pathways are far from clear. Moreover, results vary for different contexts, population types and characteristics of green spaces as they are ‘complex systems thrusted into complex systems’. To our knowledge, there is no systematic synthesis of the literature that examines the mechanisms by which and the circumstances under which green spaces work to decrease heat-related mortality for elderly.MethodsWe performed a realist synthesis– a theory-driven review method– to develop a complexity- and context-sensitive program theory. As a first step, a causal loop diagram was constructed which describes the possible pathways through which urban green spaces influence heat-related mortality in elderly. In a second step, one of the pathways - how they may lead to a reduction of heat-related mortality by increasing social capital - was further explored for underlying mechanisms, the context in which they work and the differentiated patterns of outcomes they generate. Literature was searched for evidence supporting or contradicting the initial programme theory, resulting in a refined theory.ResultsResults show how urban green space can impact on heat-related mortality in elderly by its influence on their exposure to outdoor and indoor heat, by improving their resilience as well as by affecting their access to treatment. Urban green spaces and their interactions with social capital affect the access to health information, social support, and the capacity for effective lobbying. Several mechanisms help to explain these observed demi-regularities, among others perceived behavioural control, perceived usefulness, receptiveness, ontological security, and self-interest. If and how they are triggered depends on the characteristics of the urban green space, the population, and other contextual factors.ConclusionLooking into the impact of urban green spaces on heat-related mortality in elderly, researchers and policy makers should take interest in the role of social capital.

Evolutionary Characteristics and Driving Forces of Green Space in Guangzhou from a Zoning Perspective

Urban green spaces provide very important environmental and social services. Their dynamic changes are driven by a combination of natural and socioeconomic factors. However, the coupling mechanism of these factors has not been systematically studied. In this study, we examined dynamic characteristics of green space in Guangzhou from different zoning perspectives and explored the regional heterogeneity of the individual and interactive effects of driving factors using the geographic detector. The results showed that (1) from 2000 to 2020, the annual change rate of green space area in the central area was more apparent than that in the suburban areas. The fragmentation of green space in the near suburbs had increased. (2) Changes in green space were influenced by the combination of topography, climate, and socioeconomic development. From 2010 to 2020, the expansion of built-up land and GDP growth gradually had a substantial effect on the change in green space in the central area and the near suburbs. (3) The q-values of the interaction detections of the geodetector showed that the explanatory power of most factor interactions exceeded that of individual factors. Green space in the central area was primarily influenced by the interaction of economic and built-up land expansion. In contrast, green space in the near suburbs was mainly influenced by the interaction of urban expansion and topography and climate. Green space in the far suburbs was mainly affected by climate factors and human activity intensity. The results and methods of this study can provide decision support for the zoning planning of urban green space system in other cities or regions.

Green Space Morphology and School Myopia in China

China has experienced both rapid urbanization and major increases in myopia prevalence. Previous studies suggest that green space exposure reduces the risk of myopia, but the association between myopia risk and specific geometry and distribution characteristics of green space has yet to be explored. These must be understood to craft effective interventions to reduce myopia. To evaluate the associations between myopia and specific green space morphology using novel quantitative data from high-resolution satellite imaging. This prospective cohort study included students grades 1 to 4 (aged 6 to 9 years) in Shenzhen, China. Baseline data were collected in 2016-2017, and students were followed up in 2018-2019. Data were analyzed from September 2020 to January 2022. Eight landscape metrics were calculated using land cover data from high-resolution Gaofen-2 satellite images to measure area, aggregation, and shape of green space. The 2-year cumulative change in myopia prevalence at each school and incidence of myopia at the student level after 2 years were calculated as main outcomes. The associations between landscape metrics and school myopia were assessed, controlling for geographical, demographic, and socioeconomic factors. Principal component analyses were performed to further assess the joint effect of landscape metrics at the school and individual level. A total of 138 735 students were assessed at baseline. Higher proportion, aggregation, and better connectivity of green space were correlated with slower increases in myopia prevalence. In the principal component regression, a 1-unit increase in the myopia-related green space morphology index (the first principal component) was negatively associated with a 1.7% (95% CI, -2.7 to -0.6) decrease in myopia prevalence change at the school level (P = .002). At the individual level, a 1-unit increase in myopia-related green space morphology index was associated with a 9.8% (95% CI, 4.1 to 15.1) reduction in the risk of incident myopia (P < .001), and the association remained after further adjustment for outdoor time, screen time, reading time, and parental myopia (adjusted odds ratio, 0.88; 95% CI, 0.80 to 0.97; P = .009). Structure of green space was associated with a decreased relative risk of myopia, which may provide guidance for construction and renovation of schools. Since risk estimates only indicate correlations rather than causation, further interventional studies are needed to assess the effect on school myopia of urban planning and environmental designs, especially size and aggregation metrics of green space, on school myopia.

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

The spatial pattern of regional green space is an important dimension to describe and quantitatively express the characteristics of regional green spaces outside the built-up area of a city. With the expansion of urban and rural construction land, regional green space has been continuously encroached upon. This leads to a decline in regional ecological well-being and the loss of biodiversity. Based on the remote sensing data of Shanghai city from 2000 to 2020, we quantitatively studied the spatial morphological change characteristics of regional green space outside the built-up area of Shanghai city. Firstly, with the help of the GEE platform, the optimal decoding accuracy classification method was selected through machine learning (random forest, support vector machine, classification regression tree); then, based on the biophysical component (BCI) and CA binarization, the built-up area ranges for up to five time nodes were obtained; finally, through GIS spatial data analysis and processing technology, the regional green space dynamic data of Shanghai for five time nodes were extracted. Based on the above data, an analysis index system was constructed to quantitatively analyze the spatial morphology characteristics of the regional green space outside the built-up area of Shanghai. The results show that (1) the area of regional green space outside the built-up area of Shanghai had a fluctuating growth pattern of “decreasing and then increasing”. The arable land and water areas in Shanghai decreased, and the woodland area increased steadily, while the wetland and grassland areas showed a trend of first decreasing and then increasing. (2) The regional green patch fragmentation shows a fluctuating development trend of increasing, decreasing, and increasing. (3) The change in the spatial center of gravity of the regional green space in Shanghai had a high degree of consistency with the overall green space change. The center of gravity of the grasslands in the regional green space moved substantially to the northwest, while the center of gravity of the other types remained basically unchanged. This study reveals the spatial morphology characteristics of regional green spaces and provides a research method to study the dynamic changes in regional ecological resources. The results of this study can provide a scientific basis for the identification, protection, and development of regional ecological resources.

Impacts of Building Environment and Urban Green Space Features on Urban Air Quality: Focusing on Interaction Effects and Nonlinearity

Air pollution is a rising environmental concern that has detrimental effects on human health and the environment. Building environment and urban green space features play a crucial role in the dispersion and accumulation of air pollutants. This study examines the impacts of building environment and urban green space on air pollution levels in the highly urbanized city of Hong Kong, focusing on their interaction effects and potential nonlinearity. For the analysis, this paper investigates how building density, building height, building types, urban green space size, and number of urban green space clusters, as well as their interplays, impact PM2.5 concentrations using high-resolution, satellite-based PM2.5 grids coupled with spatial analysis techniques. The findings reveal that a unit increase in the size of urban green space and the standard deviation of building height contribute to a 0.0004 and a 0.0154 reduction in PM levels, respectively. In contrast, air pollution levels are found to be positively associated with building density (0.1117), scatteredness of urban green space (0.0003), and share of commercial buildings (1.0158). Moreover, it has been found that building height presents a U-shape relationship with PM2.5 concentrations. Finally, the negative association between the size of urban green space and air pollution levels tends to be enlarged in districts with more low-rise buildings. This study conveys important building environment and urban green space planning implications.

Exploring the influence of relative attractiveness in green spaces on urban movements: A potential to kinetic energy framework

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities.

Green Space and Apartment Prices: Exploring the Effects of the Green Space Ratio and Visual Greenery

Urban green spaces provide various social, economic, health, aesthetic, environmental, and ecological benefits. This study aimed to investigate the influence of green spaces on apartment prices, with a particular emphasis on visual greenery and the proportion of green spaces. Hedonic pricing models have often been used to assess the impact of green spaces on housing prices. Herein, 16 variables were considered as factors affecting housing prices and divided into housing, neighborhood, and green space characteristics. The findings indicate that the presence of green spaces enhanced the value of apartment complexes. Moreover, both visual greenery and the proportion of green spaces within apartment complexes influenced housing prices. Additional analysis demonstrated the impact of green space characteristics within Seoul apartment complexes on housing price changes from 2016 to 2022, finding that higher green space proportions and visual greenery led to approximately 20% higher price increases, and structural equation modeling revealed that the proportion of green spaces within apartment complexes, directly and indirectly, influenced housing prices through visual greenery. Overall, this study emphasized the importance of ensuring well-managed green spaces within and around apartment complexes.

The relation between proximity to and characteristics of green spaces to physical activity and health: A multi-dimensional sensitivity analysis in four European cities

IntroductionNon-communicable diseases are the global disease burden of our time, with physical inactivity identified as one major risk factor. Green spaces are associated with increased physical activity of nearby residents. But there are still gaps in understanding which proximity and what characteristics of green spaces can trigger physical activity. This study aims to unveil these differences with a rigorous sensitivity analysis. MethodsWe gathered data on self-reported health and physical activity from 1365 participants in selected neighbourhoods in Porto, Nantes, Sofia, and Høje-Taastrup. Spatial data were retrieved from OpenStreetMap. We followed the PRIGSHARE guidelines to control for bias. Around the residential addresses, we generated seven different green space indicators for 15 distances (100–1500 m) using the AID-PRIGSHARE tool. We then analysed each of these 105 green space indicators together with physical activity and health in 105 adjusted structural equation models. ResultsGreen space accessibility and green space uses indicators showed a pattern of significant positive associations to physical activity and indirect to health at distances of 1100 m or less, with a peak at 600 m for most indicators. Greenness in close proximity (100 m) had significant positive effects on physical activity and indirect effects on health. Surrounding greenness showed positive direct effects on health at 500–1100 m and so do green corridors in 800 m network distance. In contrast, a high quantity of green space uses, and surrounding greenness measured in a larger radius (1100–1500 m) showed a negative relationship with physical activity and indirect health effects. ConclusionsOur results provide insight into how green space characteristics can influence health at different scales, with important implications for urban planners on how to integrate accessible green spaces into urban structures and public health decision-makers on the ability of green spaces to combat physical inactivity.

A new framework for analysis of the spatial patterns of 15-minute neighbourhood green space to enhance carbon sequestration performance: A case study in Nanjing, China

As the most fundamental unit of urban development, the performance of green space carbon sequestration in neighborhoods is, therefore, a crucial factor in the pursuit of carbon neutrality. Consequently, studying the performance of carbon sequestration from the spatial structure of green spaces in conjunction with the recreational features of the neighborhood is urgent and necessary. Herein, for the first time, we combined four types of indicators, namely carbon sequestration, green space spatial structure, natural environment, and human activities, using three types of 15-minute neighborhoods in the Nanjing metropolitan area as an example. As observed, the area indicator percentage of landscape (PLAND) contributed directly to the carbon sequestration of green areas and also influenced the carbon sequestration by affecting the average normalized vegetation index (NDVI) level; the shape indicator Simpson's evenness index (SIEI) not only helped to reduce the average temperature in the neighborhood but also improved the average NDVI level, both of which can contribute to carbon sequestration; the aggregation indicators aggregation index (AI) had no direct effect on the carbon sequestration performance but contributed to it by lowering the temperature. Thus, the current study revealed the mechanism by which the spatial characteristics of residential green spaces influenced carbon sequestration and proposed a new direction for future sustainable urban development.