Do Spatial Boundaries Matter for Exploring the Impact of Community Green Spaces on Health?

The Vitality of Public Space and the Effects of Environmental Factors in Chinese Suburban Rural Communities Based on Tourists and Residents

The vitality of public space in rural communities is an important symbol of rural revitalization, especially in suburban rural communities. Previous studies focused on rural industries and ignored the effects of the spatial environment on different groups of people. Hence, this study takes the public space of suburban rural communities as an example and uses Global Positioning System (GPS) and cognitive mapping data to establish a new vibrancy assessment system for tourists and residents, respectively. The effects of the public space environment and space vitality in suburban rural communities are revealed through ordinary least squares (OLS) and geographically weighted regression (GWR) models. The results suggested that: (I) There were pronounced seasonal changes and spatial distribution differences in the space vitality of tourists, while residents were concentrated in fixed public spaces. (II) For tourists, the public space vitality in rural communities was affected by seven factors, including accessibility, seats, green looking ratio, recreational facilities, water area, plant species richness, and plant color composition. Green looking ratio and water area had a negative impact. For residents, the public space vitality in rural communities was affected by five factors, including shelter facilities, seats, accessibility, space type, fitness facilities. Only fitness facilities had a negative effect. Our research proposed a feasible and effective method to assess the vitality of rural public space in rural communities, and the finding from this study provides significant implications for the development and planning of suburban rural communities oriented by vitality.

COVID-19, green space exposure, and mask mandates

INTRODUCTION

Mask-wearing and social distancing are critical prevention measures that have been implemented to stem the spread of COVID-19. The degree to which these measures are adhered to in the US, however, may be influenced by access to outdoor resources such as green space, as well as mask mandates that may vary by state.

PURPOSE

To examine the association between the presence or absence of statewide mask mandates and green space exposure with COVID-19 cumulative incidence in the US.

METHODS

In October 2020, COVID-19 case data for each US county was downloaded from USA Facts, in addition to statewide mask mandates from a database maintained by the American Association of Retired Persons. The Normalized Difference Vegetation Index from the US Geological Survey (USGS), was used as a measure of greenspace, while the 2016 National Land Cover Database was used to assess tree canopy exposure as an alternative measure of greenspace. We performed generalized linear regression to evaluate associations with COVID-19 incidence, adjusting for potential confounders such as other environmental factors (i.e., air pollution and climate) and socio-economic factors derived from the CDC social vulnerability index. In addition, we also performed spatial regression analyses to account for spatial autocorrelation across counties.

RESULTS

Counties with mandatory mask-wearing policies had a lower cumulative incidence of COVID-19 (B = -0.299, SE = 0.038). Among environmental factors, precipitation (B = 0.005, SE = 0.001) and PM 2.5 (B = 0.072, SE = 0.012) were associated with a higher incidence of COVID-19, while tree canopy (B = -0.501, SE = 0.129) was associated with a lower risk of COVID-19. COVID-19 incidence was higher in counties with socially vulnerable populations regarding socioeconomic status, minority status, and housing and transportation.

CONCLUSION

Mandatory mask regulation, exposure to green space, and reduced exposure to air pollution may reduce COVID-19 incidence in the US. Additional public health policies should consider ways to mitigate environmental conditions that may contribute to the risk of COVID-19, especially for vulnerable populations.

Is green space associated with reduced crime? A national-scale study from the Global South

Assumptions about the link between green space and crime mitigation are informed by literature that overwhelmingly originates in the Global North. Little is known about the association between green spaces and crime in the Global South. We utilized 10 years of precinct-level crime statistics (n = 1152) over South Africa, a global crime hotspot, to test the hypothesis that green space is associated with reduced crime rates. We found that, after controlling for a number of socio-demographic confounders (unemployment, income, age, education, land use and population density), for every 1% increase in total green space there is a 1.2% (0.7 to 1.7%; 95% confidence interval) decrease in violent crime, and 1.3% (0.8 to 1.8%) decrease in property crime, with no effect on sexual crimes. However, the direction of the association changed for property crimes when exploring the effect of green space characteristics including tree cover and park accessibility. Property crimes increase by 0.4% (0.1 to 0.7%) with a percentage increase in tree cover, and by 0.9% (0.5 to 1.3%) with every kilometer increase in proximity to a public park. Further research, including experimental studies, is needed to better isolate causal mechanisms behind crime-green space associations, especially considering that green space may map to race and income inequality and that there may be more crime reporting in affluent areas. Nevertheless, our results provide a complementary contribution to the evidence from the Global North, highlighting the need for more nuanced definitions of green space and its characteristics when considering links to crime. When viewed in light of the broader suite of ecosystem services provided by green space, our results support urban greening as a major strategy towards achieving just and sustainable cities and towns.

Rapid Mapping and Annual Dynamic Evaluation of Quality of Urban Green Spaces on Google Earth Engine

In order to achieve the United Nations 2030 Sustainable Development Goals (SDGs) related to green spaces, monitoring dynamic urban green spaces (UGSs) in cities around the world is crucial. Continuous dynamic UGS mapping is challenged by large computation, time consumption, and energy consumption requirements. Therefore, a fast and automated workflow is needed to produce a high-precision UGS map. In this study, we proposed an automatic workflow to produce up-to-date UGS maps using Otsu’s algorithm, a Random Forest (RF) classifier, and the migrating training samples method in the Google Earth Engine (GEE) platform. We took the central urban area of Beijing, China, as the study area to validate this method, and we rapidly obtained an annual UGS map of the central urban area of Beijing from 2016 to 2020. The accuracy assessment results showed that the average overall accuracy (OA) and kappa coefficient (KC) were 96.47% and 94.25%, respectively. Additionally, we used six indicators to measure quality and temporal changes in the UGS spatial distribution between 2016 and 2020. In particular, we evaluated the quality of UGS using the urban greenness index (UGI) and Shannon’s diversity index (SHDI) at the pixel level. The experimental results indicate the following: (1) The UGSs in the center of Beijing increased by 48.62 km2 from 2016 to 2020, and the increase was mainly focused in Chaoyang, Fengtai, and Shijingshan Districts. (2) The average proportion of relatively high and above levels (UGI > 0.5) in six districts increased by 2.71% in the study area from 2016 to 2020, and this proportion peaked at 36.04% in 2018. However, our result revealed that the increase was non-linear during this assessment period. (3) Although there was no significant increase or decrease in SHDI values in the study area, the distribution of the SHDI displayed a noticeable fluctuation in the northwest, southwest, and northeast regions of the study area between 2016 and 2020. Furthermore, we discussed and analyzed the influence of population on the spatial distribution of UGSs. We found that three of the five cold spots were located in the east and southeast of Haidian District. Therefore, the proposed workflow could provide rapid mapping and dynamic evaluation of the quality of UGS.

Multi-source sensor based urban habitat and resident health sensing: A case study of Wuhan, China

The COVID-19 pandemic undoubtedly has a great impact on the world economy, especially the urban economy. It is urgent to study the environmental pathogenic factors and transmission route of it. We want to discuss the relationship between the urban living environment and the number of confirmed cases at the community scale, and examine the driving forces of community infection (e.g., environment, ecology, convenience, livability, and population density). Besides, we hope that our research will help make our cities more inclusive, safe, resilient, and sustainable. 650 communities with confirmed COVID-19 cases in Wuhan were selected as the research objects. We utilize deep learning semantic segmentation technology to calculate the Visible Green Index (VGI) and Sky View Factor (SVF) of street view and use Partial Least Squares Structural Equation Modeling (PLS-SEM) to study the driving forces of pandemic situation. Temperature and humidity information recorded by sensors was also used for urban sensing. We find that the more SVF has a certain inhibitory effect on the virus transmission, but contrary to our intuitive perception, higher VGI has a certain promotion effect. Also, the structural equation model constructed in this paper can explain the variance of 28.9% of the number of confirmed cases, and results (path coef.) demonstrate that residential density of community (0.517) is a major influencing factor for pandemic cases, whereas convenience of community living (0.234) strongly influence it. Communities with good suitability of community human settlement (e.g., construction time, price) are safer in the face of pandemic events. Does the influence of SVF and VGI on the results of the pandemic situation mean that sunlight can effectively block the spread of the virus? This spatial heterogeneity in different communities is helpful for us to explore the environmental transmission route of COVID-19.