Observed inequality in urban greenspace exposure in China

Examining the importance of neighborhood natural, and built environment factors in predicting older adults' mental well-being: An XGBoost-SHAP approach

BACKGROUND

Previous studies have shown that urban neighborhood environmental factors significantly influence the health outcomes of urban older adults. However, most cross-sectional studies exploring the health effects of these factors have failed to quantify the relative importance of each factor.

METHODS

We use XGBoost machine learning techniques and SHAPley Additive Interpretation (SHAP) to rank the importance of urban neighborhood environmental factors in shaping the mental health of urban older adults. To address self-selection bias in housing choice, we distinguish older adults living in private housing from those living in public as residents in private housing have more freedom to choose where to live.

RESULTS

The results show that both natural and built environmental factors in urban neighborhoods are important predictors of mental well-being scores. Five natural environmental factors (blue space, perceived greenery quantity, NDVI, street view greenness, aesthetic quality) and three built environmental factors (physical activity facilities quality, physical activity facilities quantity, neighborhood disorder) had considerable predictive power for mental well-being scores in two groups. Among them, blue space, perceived greenery quantity and street view greenness quantity became less important after controlling for self-selection bias, possibly because of the unequal distribution of quantity and quality, and the performance of neighborhood disorder, aesthetic quality and physical activity facilities quality was more sensitive in public housing.

CONCLUSIONS

These results highlight the nuanced and differential effects of neighborhood environmental exposures on mental well-being outcomes, depending on housing preferences. The results of this study can provide support for decision makers in urban planning, landscape design and environmental management in order to improve the mental well-being status of urban older adults.

Delineating Urbanicity and Rurality: Impact on Environmental Exposure Assessment

Environmental exposures and their health impacts can vary substantially between urban and rural areas. However, different methods for classifying these areas could lead to inconsistencies in environmental exposure and health studies, which are often overlooked. We constructed different urban/rural classification systems based on multiple population-based (e.g., total population, population density, and commuting) and built-environment-based (e.g., nighttime light intensity, building density, road density, distance to urban centers, point of interest density, and urban area coverage) indicators and various classification schemes. These classification systems were applied to Virginia and West Virginia, United States. We compared differences in urban/rural spatial patterns, demographic compositions, and exposures of particulate matter (PM2.5), greenspace, and land surface temperature using these urban/rural classification systems to understand their impacts on environmental exposure and health research. Our findings reveal clear differences in spatial patterns and demographic compositions across various systems. We also observed that different systems can lead to changes in the magnitude and direction of urban/rural disparities in environmental exposure assessment. Addressing the complexities in delineating urbanicity and rurality may include careful consideration of classification systems to reflect those aspects of urbanicity and rurality that are relevant to the research question or the use of multiple, complementary systems.

Extensive inequality of residential greenspace exposure within urban areas in China

Ensuring residents' equal access to high quality urban greenspace is vital for urban environmental justice and sustainable urban development. However, most previous studies have mainly focused on greenspace quantity, overlooking its quality. Moreover, the national-level spatial distribution pattern of residential greenspace exposure (RGE) within urban areas remains unclear. Here, we have improved the existing RGE assessment framework by integrating both the quality and quantity of urban greenspace to evaluate RGE and its associated inequality across 119,692 blocks in 334 Chinese cities in 2020. We find that the spatial distribution pattern of RGE varies with urban size. Large cities exhibit a distinct clustering of low RGE in their central areas, whereas small cities tend to show a pronounced clustering of high RGE in the central areas. RGE in Chinese cities indicates extensive inequality, as the average RGE of high-exposed people is nearly four times greater than that of low-exposed people. Moreover, residents in larger cities are more prone to experiencing greater inequalities compared to those in smaller cities. We also find that the landscape metrics (i.e., connectance index and mean Euclidean nearest-neighbor distance) of greenspace possess a strong explanatory power (R2 = 0.431) for the observed inequality. Our study underscores the importance of optimizing the landscape structure of urban greenspace and enhancing equality in the quality of greenspace. These findings provide novel insights for urban greenspace planning and promoting urban environmental justice.

How mobility pattern shapes the association between static green space and dynamic green space exposure

Greenspaces are crucial for enhancing mental and physical health. Recent research has shifted from static methods of assessing exposure to greenspaces, based on fixed locations, to dynamic approaches that account for individual mobility. These dynamic evaluations utilize advanced technologies like GPS tracking and remote sensing to provide more precise exposure estimates. However, little work has been conducted to compare dynamic and static exposure assessments and the effect of individual mobility on these evaluations. This study delves into how greenspaces around homes and workplaces, along with mobility patterns, affect dynamic greenspace exposure in Hong Kong. Data was collected from 787 participants in four communities in Hong Kong using GPS, portable sensors, and surveys. Using multiple statistical tests, our study revealed significant variations in participants' daily mobility patterns across socio-demographic and temporal factors. Further, using linear mixed-effects models, we identified complex and statistically significant interactions between participants' static greenspace exposure and their mobility patterns. Our findings suggest that individual mobility patterns significantly modify the relationship between static and dynamic greenspace exposure and play a critical role in explaining socio-demographic and temporal context differences in the relationship between static and dynamic greenspace exposure.

Visiting natural open spaces in urban areas during pregnancy and its association with daily physical activity

We examined the association between visiting natural open spaces (NOS) and physical activity (PA) at different trimesters of pregnancy. We used GPS and accelerometer data from women residing in Donostia-San Sebastian and Barcelona. Daily visits to NOS were associated with an increase of circa 8 min of moderate to vigorous physical activity (MVPA) in the first and second trimesters of pregnancy. Women who visited NOS were more likely to meet the WHO daily PA guidelines during the first trimester. Visiting NOS can promote PA consistently during pregnancy, improving maternal health and well-being in urban settings.

High-resolution greenspace dynamic data cube from Sentinel-2 satellites over 1028 global major cities

Greenspace, offering multifaceted ecological and socioeconomic benefits to the nature system and human society, is integral to the 11th Sustainable Development Goal pertaining to cities and communities. Spatially and temporally explicit information on greenspace is a premise to gauge the balance between its supply and demand. However, existing efforts on urban greenspace mapping primarily focus on specific time points or baseline years without well considering seasonal fluctuations, which obscures our knowledge of greenspace's spatiotemporal dynamics in urban settings. Here, we combined spectral unmixing approach, time-series phenology modeling, and Sentinel-2 satellite images with a 10-m resolution and nearly 5-day revisit cycle to generate a four-year (2019-2022) 10-m and 10-day resolution greenspace dynamic data cube over 1028 global major cities (with an urbanized area >100 km2). This data cube can effectively capture greenspace seasonal dynamics across greenspace types, cities, and climate zones. It also can reflect the spatiotemporal dynamics of the cooling effect of greenspace with Landsat land surface temperature data. The developed data cube provides informative data support to investigate the spatiotemporal interactions between greenspace and human society.

Mapping environmental suitability changes for arbovirus mosquitoes in Southeast Asia: 1960-2020

Spatial epidemiology recognizes the impact of environmental factors on human infectious diseases through disease vectors. The expansion of Aedes aegypti and Aedes albopictus raises concerns about health risks due to their changing distribution. However, current mosquito mapping methods have low spatial resolution and limited focus on long-term trends and factors. This study develops a high-resolution framework (500 m) to map mosquito distribution in Southeast Asia from 1960 to 2020. It includes a species distribution model, a spatial autocorrelation model, and a geographical detector model. The study produces Southeast Asia's first 500 m resolution map of mosquito suitability, revealing significant increases in mosquito suitability in most cities over the past 60 years. The analysis indicates a shift in high-suitability areas from coastal to inland regions, with nighttime land surface temperature playing a key role. These findings are crucial for regional risk assessments and mitigation strategies related to vector-borne diseases.

Towards a Green Equal City: Measuring and matching the supply-demand of green exposure in urban center

Exposure to green environments is crucial for human health. However, urbanization has reduced the contact of urban residents with natural environments, causing a mismatch between the supply and demand for green exposure. Research in this field is hindered by the lack of long-term, reliable data sources and methodologies, leading to insufficient consideration of temporal variations in green exposure. This study presented a comprehensive methodology for assessing green exposure at a fine scale utilizing satellite images for urban tree canopy identification. We conducted a case study in the core area of Beijing from 2010 to 2020 and examined the effects of urban renewal and alleviation efforts. The results revealed a slight decrease in green exposure for the elderly over the decade, with minimal changes in equity. In contrast, green exposure for children has increased, with increasing inequality. Moreover, urban renewal has improved green exposure for nearly half of the low-supply blocks. However, a significant mismatch was observed between supply and demand for blocks with increased demand but limited supply. This study enhances the assessment of green exposure and provides guidance for planning and constructing a "Green Equal City".

Green-gray imbalance: Rapid urbanization reduces the probability of green space exposure in early 21st century China

Green space exposure provides greater beneficial effects on residents compared to unnatural spaces, commonly referred to as "gray spaces". However, during rapid urbanization, gray spaces expand more quickly than green spaces, often encroaching upon and overtaking these natural environments. This unchecked growth leads to detrimental impacts on the human habitat and overall environmental quality. Consequently, it is essential to meticulously assess the spatial and temporal patterns of residents' exposure levels, as well as to thoroughly investigate the underlying driving mechanisms behind these changes. This study used population-weighted exposure level measurements to assess gray and green space exposure in Chinese cities in the early 21st Century (2000-2019). Additionally, the Gray-Green space Exposure Ratio (GER) was calculated, and the spatiotemporal driving mechanism of GER by each factor was analyzed by geostatistical modeling. The results show that gray space exposure is generally increasing in China, especially in eastern parts of China. The probability of exposure to gray spaces exceeds that of green spaces in some high urbanization rate cities. This trend will continue, albeit at a slower rate. Urban sprawl, built-up area density, and increased electricity consumption were the main drivers of rising GER, whereas greenspace integrity contributed to lower GER; the driving mechanisms for GER changes were spatiotemporal heterogeneous. This study provides a reliable reference for restoring the green space exposure to promote the living environment constructing and residents' access to nature.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Urbanization promotes carbon storage or not? The evidence during the rapid process of China

China's commitment to attaining carbon neutrality by 2060 has galvanized research into carbon sequestration, a critical approach for mitigating climate change. Despite the rapid urbanization observed since the turn of the millennium, a comprehensive analysis of how urbanization influences urban carbon storage throughout China remains elusive. Our investigation delves into the nuanced effects of urbanization on carbon storage, dissecting both the direct and indirect influences by considering urban-suburban gradients and varying degrees of urban intensity. We particularly scrutinize the roles of climatic and anthropogenic factors in mediating the indirect effects of urbanization on carbon storage. Our findings reveal that urbanization in China has precipitated a direct reduction in carbon storage by approximately 13.89 Tg of carbon (Tg C). Remarkably, urban sprawl has led to a diminution of vegetation carbon storage by 8.65 Tg C and a decrease in soil carbon storage by 5.24 Tg C, the latter resulting from the sequestration of impervious surfaces and the elimination of organic matter inputs following vegetation removal. Meanwhile, carbon storage in urban greenspaces has exhibited an increase of 6.90 Tg C and offsetting 49.70% of the carbon loss induced by direct urbanization effects. However, the indirect effects of urbanization predominantly diminish carbon storage in urban greenspaces by an average of 5.40%. The degree of urban vegetation management emerges as a pivotal factor influencing the indirect effects of urbanization on carbon storage. To bolster urban carbon storage, curbing urban sprawl and augmenting urban green spaces are imperative strategies. Insights from this study are instrumental in steering sustainable urban planning and advancing towards the goal of carbon neutrality.

Cooling Effect of Green Spaces on Urban Heat Island in a Chinese Megacity: Increasing Coverage versus Optimizing Spatial Distribution

Enhancing the cooling effectiveness of green spaces (GSs) is crucial for improving urban thermal environments in the context of global warming. Increasing GS coverage and optimizing its spatial distribution individually proved to be effective urban cooling measures. However, their comparative cooling effectiveness and potential interaction remain unclear. Here, using the moving window approach and random forest algorithm, we established a robust model (R2 = 0.89 ± 0.01) to explore the relationship between GS and land surface temperature (LST) in the Chinese megacity of Guangzhou. Subsequently, the response of LST to varying GS coverage and its spatial distribution was simulated, both individually and in combination. The results indicate that GS with higher coverage and more equitable spatial distribution is conducive to urban heat mitigation. Increasing GS coverage was found to lower the city's average LST by up to 4.73 °C, while optimizing GS spatial distribution led to a decrease of 1.06 °C. Meanwhile, a synergistic cooling effect was observed when combining both measures, resulting in additional cooling benefits (0.034-0.341 °C). These findings provide valuable insights into the cooling potential of GS and crucial guidance for urban green planning aimed at heat mitigation in cities.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Lower regional urbanicity and socioeconomic status attenuate associations of green spaces with hypertension and diabetes mellitus: a national representative cross-sectional study in China

BACKGROUND

High blood pressure (HBP) and diabetes mellitus (DM) are two of the most prevalent cardiometabolic disorders globally, especially among individuals with lower socio-economic status (SES). Studies have linked residential greenness to decreased risks of HBP and DM. However, there has been limited evidence on whether SES may modify the associations of residential greenness with HBP and DM.

METHODS

Based on a national representative cross-sectional study among 44,876 adults, we generated the normalized difference vegetation index (NDVI) at 1 km spatial resolution to characterize individuals' residential greenness level. Administrative classification (urban/rural), nighttime light index (NLI), individual income, and educational levels were used to characterize regional urbanicity and individual SES levels.

RESULTS

We observed weaker inverse associations of NDVI with HBP and DM in rural regions compared to urban regions. For instance, along with per interquartile range (IQR, 0.26) increment in residential NDVI at 0∼5 year moving averages, the ORs of HBP were 1.04 (95%CI: 0.94, 1.15) in rural regions and 0.85 (95%CI: 0.79, 0.93) in urban regions (P = 0.003). Along with the decrease in NLI levels, there were continuously decreasing inverse associations of NDVI with DM prevalence (P for interaction <0.001). In addition, weaker inverse associations of residential NDVI with HBP and DM prevalence were found among individuals with lower income and lower education levels compared to their counterparts.

CONCLUSIONS

Lower regional urbanicity and individual SES could attenuate the associations of residential greenness with odds of HBP and DM prevalence.

Multi-scale spatiotemporal trends and corresponding disparities of PM2.5 exposure in China

Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.

An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services

The rapid urbanization has accelerated the destruction of regional ecosystems, triggering ecological risks and threatening sustainable development. Landscape ecological risk (LER) evaluation is an effective tool to mitigate such negative impacts. However, the existing evaluation systems exhibit certain subjectivity. Therefore, an improved LER evaluation method was proposed, which incorporates ecosystem services (ESs) to characterize landscape vulnerability. The method was validated using the Pearl River Delta urban agglomeration (PRDUA) as the study area. The results showed that the optimal grain size and extent for landscape pattern analysis in the PRDUA were determined to be 150 m and 6km × 6 km, respectively. The comparison results with the traditional LER evaluation method demonstrated the improved method's superior rationality and reliability. The hotspot analysis based on the Getis-Ord Gi* method revealed that the hotspots of LER were mainly concentrated in the densely populated areas of the south-central region of the PRDUA. The coupling coordination degree (CCD) between LERs and ESs showed four different levels of development in both temporal and spatial dimensions, generally dominated by moderately balanced development and lagging ESs, reflecting the unbalanced ecological environment and socio-economic development of the PRDUA. It is recommended that the ecosystems in the PRDUA be managed and protected separately according to the delineated Ecological Protection Area (EPA), Urban Built-up Area (UBA), and Urban Ecological Boundary Area (UEBA). This study can provide an important reference for regional ecosystem conservation and management.

Does urban growth mean the loss of greenness? A multi-temporal analysis for Chinese cities

Urban growth is recognized as the conversion of vegetated surface to built-up surface. However, there is still no consensus about the urbanization-induced dynamic of vegetation greenness in view of existing literatures. In this study, we aimed to empirically investigate whether urban growth mean the loss of vegetation greenness. We selected 340 Chinese cities as the study areas, relied on consistent multi-temporal remotely sensed data and adopted linear regression analysis, annual growth area, Tail-Sen slope and Mann-Kendall models. Results show that although vegetation greening generally lagged behind urban growth in the monitoring period, a tendency of their consistent speeding up can be observed over time. By categorizing four forms and four trends of vegetation greenness dynamics related to urban growth, we revealed the diversity of Chinese cities. The former focused on the velocity of urban growth and vegetation greenness dynamics within newly urbanized area in three phases, i.e., 2003-2008, 2008-2013 and 2013-2018. The latter focused on the interannual trends of vegetation greenness dynamics among the previously existing and newly urbanized areas. The key finding is that, in over 85 % of the cities, we measured an increase of vegetation greenness along with urban growth. In addition, our detailed results allow quantifying the impact of urbanization in Chinese cities on vegetation protection and sustainable development.

Improved human greenspace exposure equality during 21st century urbanization

Greenspace plays a crucial role in urban ecosystems and has been recognized as a key factor in promoting sustainable and healthy city development. Recent studies have revealed a growing concern about urban greenspace exposure inequality; however, the extent to which urbanization affects human exposure to greenspace and associated inequalities over time remains unclear. Here, we incorporate a Landsat-based 30-meter time-series greenspace mapping and a population-weighted exposure framework to quantify the changes in human exposure to greenspace and associated equality (rather than equity) for 1028 global cities from 2000 to 2018. Results show a substantial increase in physical greenspace coverage and an improvement in human exposure to urban greenspace, leading to a reduction in greenspace exposure inequality over the past two decades. Nevertheless, we observe a contrast in the rate of reduction in greenspace exposure inequality between cities in the Global South and North, with a faster rate of reduction in the Global South, nearly four times that of the Global North. These findings provide valuable insights into the impact of urbanization on urban nature and environmental inequality change and can help inform future city greening efforts.

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale

Due to expanding populations and thriving economies, studies into the built environment's thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3 °C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST's effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30 °C and 29.91 °C, respectively, with a mean LST of 25.1 °C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31 °C and 25.35 °C, respectively, with a mean LST of 21.33 °C. There was a 3.8 °C drop in the LST of this grid. The minimum and maximum NDVI were also - 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

Examining individual-level tri-exposure to greenspace and air/noise pollution using individual-level GPS-based real-time sensing data

OBJECTIVE

Although exposure to air/noise pollution and greenspace has been found to significantly affect people's physical and mental health outcomes, there is still a lack of knowledge on what built-environment and socioeconomic factors are significantly associated with people's tri-exposure to air/noise pollution and greenspace. This study analyzes the associations between built-environment and socioeconomic factors and the tri-exposure to greenspace and air/noise pollution in Hong Kong.

METHOD

Based on individual-level activity data, real-time GPS trajectories, and exposure data collected by portable sensors as well as remote sensing satellite imagery, we employ multinomial logistic regression to determine the socioeconomic and built-environment factors that are significantly associated with the type of participants' tri-exposure at the grid cell level.

RESULTS

The results show that higher transit nodal accessibility, building density, building height and land-use mix are significantly associated with a higher likelihood of being disadvantaged in terms of tri-exposure to air/noise pollution and greenspace. While more advantageous tri-exposures are significantly related to higher median monthly household income and sky view factor.

CONCLUSION

Old high-rise high-density neighborhoods are more likely to be triply disadvantaged with low greenspace exposure but high air pollution and noise pollution exposure. The findings provide policymakers with critical reference in terms of addressing the inequalities in the tri-exposure outcomes.

Disparities in greenspace access during COVID-19 mobility restrictions

More than half of the human population lives in cities and therefore predominantly experience nature in urban greenspace, an important contributor to wellbeing. As the world faces a pandemic which threatens the physical and mental health of billions of people, it is crucial to understand that all have the possibility to access nature exposure to alleviate some of these challenges. Here, for the first time, we integrate data from Facebook, Twitter, and Google Search users to show that people looked for greenspace during COVID-19 mobility restrictions but may not have always managed to reach it. We used a longitudinal approach, replicated in three European cities, to assess whether people spent more time in locations with more greenspace, and whether this change in urban density remained for the whole pandemic, pre-vaccine, period. We coupled this human density study with a longitudinal study of web search patterns for Parks and online discussion about urban greenspace. People searched for Parks near them more during the pandemic, particularly when they were allowed to visit them. They discussed in positive terms greenspace particularly more at the start of the pandemic. People spent more time in areas with greenspace when they could and that depended on the level of multiple deprivation of their neighbourhood. Importantly, while people sought greenspace throughout the first 20 months of the pandemic, this preference intensified through the waves of lockdown. Living in an affluent area conferred a greenspace advantage in London and Paris but we find that in Berlin greenspace in more deprived neighbourhoods were used more. Overall, urban greenspace occupied a greater place in people's lives during the pandemic. Whether people could realise greenspace access depended on the deprivation level of the neighbourhood. Public greenspace access should be integrated in national indices of deprivation given its importance for wellbeing.

How do forms and characteristics of Asian public housing neighbourhoods affect dementia risk among senior population? A cross-sectional study in Hong Kong

BACKGROUND

Public housing estate is a key determinant of community health risk in American/European cities. However, how forms/characteristics of compact/hilly public housing's neighbourhoods affect dementia among Asian seniors was underestimated.

DESIGN

This was a cross-sectional study.

METHODS

A total of 2,077 seniors living in Hong Kong's public housing estates were included. Dementia was measured by a Cantonese version of Montreal - Cognitive Assessment. Built environment was measured based on three dimensions (greenery, walkability, accessibility), including 11 metrics. Circular buffers (without walking paths) and service areas (considering walking paths) with two-dimensional/three-dimensional (terrain) adjustment were applied to quantify forms/characteristics of neighbourhoods. Two spatial buffers were applied: immediate distance (200 m) and walkable distance (500 m). Exposure-by-exposure regressions were applied to evaluate the associations between form/characteristics of neighbourhood and dementia.

RESULTS

Forms/characteristics without considering walking paths may overestimate health benefits from built environment. For circular buffers, higher percentage of building coverage, higher land use mix and more community/transportation/leisure facilities were negatively associated with dementia. All measures of greenery were positively associated with dementia. For service areas, measures of walkability and accessibility became insignificant except more community facilities at the immediate distance. Furthermore, terrain effect was insignificant when it was compared with the impacts of walking paths.

CONCLUSION

Dementia among seniors in hilly public housing estates was negatively associated with neighbourhood's walkability and accessibility and was influenced by walking paths. For healthy ageing, improved forms/characteristics of public housing neighbourhoods should include more accessible spaces and community facilities along walking paths for physical activities and basic daily needs.

Evaluating urban green and blue spaces with space-based multi-sensor datasets for sustainable development

Urban green and blue spaces refer to the natural and semi-natural areas within a city or urban area. These spaces can include parks, gardens, rivers, lakes, and other bodies of water. They play a vital role in the sustainability of cities by providing a range of ecosystem services such as air purification, carbon sequestration, water management, and biodiversity conservation. They also provide recreational and social benefits, such as promoting physical activity, mental well-being, and community cohesion. Urban green and blue spaces can also act as buffers against the negative impacts of urbanization, such as reducing the heat island effect and mitigating the effects of stormwater runoff. Therefore, it is important to maintain and enhance these spaces to ensure a healthy and sustainable urban environment. Assessing urban green and blue spaces with space-based multi-sensor datasets can be a valuable tool for sustainable development. These datasets can provide information on the location, size, and condition of green and blue spaces in urban areas, which can be used to inform decisions about land use, conservation, and urban planning. Space-based sensors, such as satellites, can provide high-resolution data that can be used to map and monitor changes in these spaces over time. Additionally, multi-sensor datasets can be used to gather information on a variety of environmental factors, such as air and water quality, that can impact the health and well-being of urban residents. This information can be used to develop sustainable solutions for preserving and enhancing urban green and blue spaces. This study examines how urban green and blue infrastructures might improve sustainable development. Space-based multi-sensor datasets are used to estimate urban green and blue zones for sustainable development. This work can inform sustainable development research at additional spatial and temporal scales.

Differential characteristics of carbon emission efficiency and coordinated emission reduction pathways under different stages of economic development: Evidence from the Yangtze River Delta, China

Regional carbon emission efficiency (CEE) has differentiated characteristics under different economic development stages and patterns, and identifying such characteristics is important for formulating corresponding policies for high-quality regional development. Using input‒output data related to economic development and energy consumption, a comprehensive evaluation model of the Super-SBM and Malmquist‒Luenberger (ML) index is constructed to evaluate the spatial and temporal changes and driving forces of CEE. Based on this index, a proposal is designed for collaborative carbon emission reduction zoning. The results indicate that the CEE of the Yangtze River Delta shows a fluctuating upward trend with obvious spatial agglomeration characteristics, and CEE changes are closely related to economic development stages. The annual average CEE values in each stage show positive changes, indicating that economic development gradually evolves to low carbonization levels. Moreover, CEE improvement gradually shifts from being driven by efficiency changes to being driven by technological changes. Finally, according to the characteristics of total carbon emissions and the efficiency of different cities, a synergistic emission reduction path is proposed with four aspects: land use optimization, ecological co-preservation, innovation cooperation and low carbon development.

The Impact of Campus Outdoor Space Features on Students' Emotions Based on the Emotion Map

To explore the influence of campus public space characteristics on students' emotions, we investigated the association mechanism between public space characteristics and students' emotions concerning the distribution of students' emotions in public spaces. The present study used photographs of facial expressions taken over two consecutive weeks as a source of data regarding the students' affective reactions. The collected facial expression images were analyzed using facial expression recognition. Values were assigned to the expression data, combined with geographic coordinates to create an emotion map of the campus public space using GIS software. Then, spatial feature data via emotion marker points were collected. We used smart wearable devices to combine the ECG data with spatial characteristics and took SDNN and RMSSD as ECG indicators to assess mood changes. We analyzed the correlation between these spatial features and heart rate variability and developed regression models for the ECG data. The findings show that sky visibility, space D/H, green visibility, skyline change index, and boundary permeability can engage students' positive emotions in a meaningful way. On the other hand, paving visibility and road linearity tends to induce negative emotions in students' minds.

Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen

Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

Landscape Aesthetic Value of Waterfront Green Space Based on Space-Psychology-Behavior Dimension: A Case Study along Qiantang River (Hangzhou Section)

As an important part of urban green infrastructure, the landscape effect of the urban waterfront green space varies, and sometimes, the green space with an excellent landscape aesthetic value fails to serve the needs of most citizens. This seriously affects the construction of a green ecological civilization and the implementation of the concept of "common prosperity" in China. Based on multi-source data, this study took the Qiantang River Basin as an example, selected 12 representative waterfront green spaces along the river as the research objects, and used qualitative and quantitative analysis methods to determine the landscape aesthetic value of the research area from the different dimensions of space, psychology, and physiology. We examined the relationship between each dimension so as to objectively and comprehensively reflect the landscape value characteristics of the waterfront green space in the study area and provide a reasonable theoretical framework and practical development path for future urban waterfront green space landscape design. We obtained the following results: (1) The results of the spatial dimension research indicated that the spatial value index of the waterfront green space in the study area was three-dimensional space > vertical space > horizontal space, and the overall spatial value was low; Qianjiang Ecological Park obtained the highest value (0.5473), and Urban Balcony Park obtained the lowest value (0.4619). (2) The results of the psychological dimension indicated that people's perceptions of the waterfront green space in the study area were relatively weak, mainly focusing on visual perception, but the waterfront green space with a relative emotional value greater than one accounted for 75%, and the overall recognition of the landscape was high. (3) The results of the behavioral dimension showed that the overall heat of the waterfront green space in the study area was insufficient (1.3719-7.1583), which was mainly concentrated in low-heat levels, and the population density was unevenly distributed (0.0014-0.0663), which was mainly concentrated in the medium-density level. The main purpose of users was to visit, and they stayed an average of 1.5 h. (4) The results of the coupling coordination analysis of the spatial-psychological-behavioral dimensions showed that the landscape value of the waterfront green space in the study area presented a form of 'high coupling degree and low coordination degree'.

Integrating Landscape Ecological Risks and Ecosystem Service Values into the Ecological Security Pattern Identification of Wuhan Urban Agglomeration

Urban agglomerations are the main form of China's future promotion of new urbanization development. Nevertheless, their accelerated expansion and development are increasingly threatening the security of regional ecosystems. The identification and optimization of ecological safety patterns (ESPs) is the fundamental spatial way to guarantee the ecological safety of urban circles and realize the sustainable development of the socio-economic and ecological environment. Nevertheless, from the perspective of urban green, low-carbon, and ecological restoration, regional safety evaluation still lacks a complete framework integrating ecological elements and social and natural indicators. Moreover, the evaluation method of ESPs also has a lack of judgment on the long-term change dynamics of regional landscape ecological risks and ecosystem service values. Thus, we proposed a new regional ecological security evaluation system based on ecosystem service value (ESV) and landscape ecological risk (LER), using the Wuhan urban agglomeration (WUA) as the research object. This study analyzed LER and ESV's spatial and temporal changes over nearly 40 years from 1980 to 2020. LER and LSV were used as ecological elements combined with natural and human-social elements to jointly model the resistance surface of the landscape pattern. Applying the minimum cumulative resistance model (MCR), we identified green ecological corridors, constructed the ESPs of WUA, and proposed optimization measures. Our results show that: (1) The proportion of higher- and high-ecological-risk areas in WUA has decreased from 19.30% to 13.51% over the past 40 years. Over time, a "low-high-low" hierarchical distribution characteristic centered on Wuhan city was gradually formed in the east, south, and north; the total value of ecosystem services increased from CNY1110.998 billion to CNY1160.698 billion. The ESV was higher in the northeastern, southern, and central parts of the area. (2) This study selected 30 ecological source areas with a total area of about 14,374 km2 and constructed and identified 24 ecological corridors and 42 ecological nodes, forming a multi-level ecological network optimization pattern with intertwined points, lines, and surfaces, increasing the connectivity of the ecological network and improving the ecological security level of the study area to a large extent, which is of great significance to promote the ecological priority and green-rise strategy of WUA and the high-quality development path of the green ecological shelter.

Spatial distribution vegetation density, land surface temperature, and land surface moisture of Banda Aceh, Indonesia after 17 years of tsunami: a multitemporal analysis approaches

Although some studies on vegetation cover in relation to the land surface temperature (LST) and land surface moisture (LSM) have been carried out, but not yet been investigated in relation to the pre-disaster and after-tsunami disasters. This paper investigates the spatial extent of vegetation cover density, land surface temperature, and land surface moisture of Banda Aceh from 2000 to 2020 to represent the pre-disaster phase, during the disaster, post-disaster, rehabilitation, and reconstruction stages, and current conditions using remote sensing data. NDVI, LST, and NDMI indexes were utilized, and the mapping process of multitemporal images of Banda Aceh was carried out. The results show that after the tsunami, the distribution of vegetation density varies, tending to decrease for densely vegetated areas before increasing more than 5 years later. For medium categories, the vegetation density increased by 22% in 2020 and almost 26% in 2015. Meanwhile, dense vegetation grew by 19% and 13%, respectively, according to data for 2015 and 2020. In a similar outlook, the increasing vegetation density has demonstrated that the LST across Banda Aceh is dominated by 27-30 °C, with 2005 data occupying the coldest area. The city's moderate moisture content covered 85.43% of its surface. The low to medium vegetation density, LST, and LSM indexes from 2000 to 2010 is shown by overlaying a combination of three spatial extent indexes. It is shown that Banda Aceh's medium-to-high density, medium LSM, and medium-to-high LST spatially. Banda Aceh's dynamic changes in vegetation, LST, and LSM over the past two decades have been proven in this research; the Banda Aceh's spatial variation in vegetation density, LST, and LSM is influenced by the tsunami.

Assessment of Doubly Disadvantaged Neighborhoods by Healthy Living Environment Exposure

Good access to greenspace and healthy food has commonly been found to be positively associated with health outcomes, despite some studies finding no significant relationship between them. Examining inequalities in accessing greenspace and healthy food among different disadvantaged neighborhoods can help reveal the disadvantaged races/ethnicities in cities with a high level of residential segregation (i.e., population of the same race/ethnicity concentrated in the same neighborhoods). However, existing studies have mostly focused on measuring the inequalities in accessing either greenspace or healthy food alone, which can lead to the inaccurate depiction of disadvantaged neighborhoods in healthy living environments. Therefore, this paper aims at improving the assessment of doubly disadvantaged neighborhoods by considering accessibility to both greenspace and healthy food in the City of Chicago. Our results show that black-majority neighborhoods are the most doubly disadvantaged in terms of exposure to healthy living environments. This study can help guide policymakers to divert more resources towards the improvement of the urban environment for the most doubly disadvantaged neighborhoods.

Integrated analysis of doubly disadvantaged neighborhoods by considering both green space and blue space accessibility and COVID-19 infection risk

The ongoing COVID-19 pandemic has taken a heavy toll on the physical and mental health of the public. Nevertheless, the presence of green and blue spaces has been shown to be able to encourage physical activities and alleviate the mental distress caused by COVID-19. However, just as the impact of COVID-19 varies by geographical region and area, the distribution of green and blue spaces is also different across different neighborhoods and areas. By using Hong Kong as the study area, we determine the local neighborhoods that suffer from both high COVID-19 infection risk as well as low green and blue space accessibility. The results show that some of the poorest neighborhoods in the territory such as Sham Shui Po, Kwun Tong and Wong Tai Sin are also among the most doubly disadvantaged in terms of COVID-19 infection risk as well as green and blue space accessibility.

Contrasting inequality in human exposure to greenspace between cities of Global North and Global South

The United Nations specified the need for “providing universal access to greenspace for urban residents” in the 11th Sustainable Development Goal. Yet, how far we are from this goal remains unclear. Here, we develop a methodology incorporating fine-resolution population and greenspace mappings and use the results for 2020 to elucidate global differences in human exposure to greenspace. We identify a contrasting difference of greenspace exposure between Global South and North cities. Global South cities experience only one third of the greenspace exposure level of Global North cities. Greenspace exposure inequality (Gini: 0.47) in Global South cities is nearly twice that of Global North cities (Gini: 0.27). We quantify that 22% of the spatial disparity is associated with greenspace provision, and 53% is associated with joint effects of greenspace provision and spatial configuration. These findings highlight the need for prioritizing greening policies to mitigate environmental disparity and achieve sustainable development goals.

Through an analysis of global differences in human exposure to greenspace, a new study identifies a contrasting pattern of greenspace exposure between Global South and North cities and finds seasonal variations in greenspace exposure inequality.

Evaluating the Coordinated Development between Urban Greening and Economic Growth in Chinese Cities during 2005 to 2019

Balancing economic growth with environmental protection is vital for the sustainable development of cities and regions. However, urban greening has rarely been considered in extensive studies. This study incorporates urban greening into a coupling coordination degree (CCD) model, in order to evaluate its coordination with economic performance. A total of 286 cities in China between 2005 and 2019 were selected as specific study subjects. Meanwhile, clustering method was used to classify different clusters based on CCD values, the Gini coefficient analysis was applied to discover the CCD values inequality characteristics and the exploratory spatial data analysis (ESDA) method was employed to study the CCD values spatial aggregation features. The results indicate that the CCD values presented significant spatial heterogeneity. Spatially, the CCD values were divided into eight clusters, with those in the eastern region generally being higher than in the central and western regions. Temporally, the CCD in all cities showed an increasing trend, but more than 60% of cities were still in the uncoordinated or low-level coordination stage. In addition, inequality and spatial aggregation characteristics were observed in CCD values, both of which presented decreasing trends. Greening has a stronger influence on the linked and coordinated growth of the two systems; therefore, we propose policy recommendations for pursuing the development of environmentally friendly cities from different aspects. In summary, this research allows for a better understanding of economic and environmental relationships, thus contributing to the objective of creating sustainable cities and communities.

Beyond green environments: Multi-scale difference in human exposure to greenspace in China

Greenspace exposure metrics can allow for comparisons of green space supply across time, space, and population groups, and for inferring patterns of variation in opportunities for people to enjoy the health and recreational benefits of nearby green environments. A better understanding of greenspace exposure differences across various spatial scales is a critical requirement for lessening environmental health disparities. However, existing studies are typically limited to a single city or across selected cities, which severely limits the use of results in measuring systemic national and regional scale differences that might need policy at above individual city planning level. To close this knowledge gap, our study aims to provide a holistic assessment of multi-scale greenspace exposure across provinces, cities, counties, towns, and land parcels for the whole of China. We mapped the nationwide fractional greenspace coverage at 10 m with Sentinel-2 satellite imagery, and then modeled population-weighted greenspace exposure to examine variation of greenspace exposure across scales. Our results show a prominent scaling effect of greenspace exposure across multi-scale administrative divisions in China, suggesting, as expected, an increase in heterogeneity with finer spatial scales. We also identify an asymmetric pattern of the difference between greenspace exposure and greenspace coverage, across a geo-demographic demarcation boundary (i.e., along the Heihe-Tengchong Line). In general, the greenspace coverage rate will overestimate more realistic human exposure to greenspace in East China while underestimating in West China. We further found that, in China, more recently urbanized areas have much better greenspace exposure than older urban areas. Our study provides a spatially explicit greenspace exposure metric for discovering multi-scale greenspace exposure difference, which will enhance governments' capacity to quantify environmental justice, detect vulnerable greenspace exposure risk hotspots, prioritize greenspace management at the supra-city scale, and monitor the balance between greenspace supply and demand.

Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China

With the unprecedented urbanization processes around the world, cities have become the main areas of political, cultural, and economic creation, but these regions have also caused environmental degradation and even affected public health. Ecological landscape is considered as an important way to mitigate the impact of environmental exposure on urban residents. Therefore, quantifying the quality of urban road landscape and exploring its spatial heterogeneity to obtain basic data on the urban environment and provide ideas for urban residents to improve the environment will be a meaningful preparation for further urban planning. In this study, we proposed a framework to achieve automatic quantifying urban street quality by integrating a mass of street view images based on deep learning and landscape ecology. We conducted a case study in Xiamen Island and mapped a series of spatial distribution for ecological indicators including PLAND, LPI, AI, DIVISION, FRAC_MN, LSI and SHDI. Additionally, we quantified street quality by the entropy weight method. Our results showed the streetscape quality of the roundabout in Xiamen was relatively lower, while the central urban area presented a belt-shaped area with excellent landscape quality. We suggested that managers could build vertical greening on some streets around the Xiamen Island to improve the street quality in order to provide greater well-being for urban residents. In this study, it was found that there were still large uncertainties in the mechanism of environmental impact on human beings. We proposed to strengthen the in-depth understanding of the mechanism of environmental impact on human beings in the process of interaction between environment and human beings, and continue to form general models to enhance the ability of insight into the urban ecosystem.

Direct and indirect impacts of urbanization on vegetation growth across the world's cities

Urban environments, regarded as "harbingers" of future global change, may exert positive or negative impacts on urban vegetation growth. Because of limited ground-based experiments, the responses of vegetation to urbanization and its associated controlling factors at the global scale remain poorly understood. Here, we use satellite observations from 2001 to 2018 to quantify direct and indirect impacts of urbanization on vegetation growth in 672 worldwide cities. After controlling for the negative direct impact of urbanization on vegetation growth, we find a widespread positive indirect effect that has been increasing over time. These indirect effects depend on urban development intensity, population density, and background climate, with more pronounced positive effects in cities with cold and arid environments. We further show that vegetation responses to urbanization are modulated by a cities' developmental status. Our findings have important implications for understanding urbanization-induced impacts on vegetation and future sustainable urban development.

Spatiotemporal Variations in the Intensity of Human Activity in Inner Mongolia and the Identification of Influencing Forces

The quantitative evaluation of the intensity of regional human activity can reflect changes in the relationship between people and the environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. This paper adopted the ratio of the area of construction land to the total area to calculate human activity intensity in Inner Mongolia at the league/city, banner/county, and pixel levels, respectively. On this basis, the overall change in human activity intensity and its spatiotemporal differentiation, geographical unit change, and spatial autocorrelation were analyzed. The results are as follows: (1) The intensity of human activity increased from 2000 to 2020. The intensity of human activity in Inner Mongolia was 8.71% in 2000, and it increased to 8.73% in 2010 and to 9.71% in 2020. The growth rate has accelerated since 2010. (2) The areas with high and medium intensity of human activities are mainly concentrated in the area south of the Hinggan–Yinshan–Helan Mountains. The intensity in the Hulunbeier grassland area and Alxa desert area is relatively low, and the diffusion effect gradually becomes prominent in the proximal local area. (3) The results for the detection of influencing factors indicate that the livestock density, multi-year average precipitation conditions, and population density have significant driving effects on the changes in intensity of human activity in Inner Mongolia.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Assessing the Equity of Accessibility to Urban Green Space: A Study of 254 Cities in China

Urban green space has environmental benefits of purifying the air, reducing the heat island effect and providing the social and economic benefits of rest places and social platforms. An integrated and organized green space system is important for fully realizing the positive functions of an urban ecosystem. Previous studies have considered green space supply and demand, but few studies have examined large-scale, diverse and small-scale systems, making it difficult to conduct a comparative study of urban green space accessibility and equity under the same conditions (such as data sources and calculation methods). Using the two-step floating catchment area method, this study evaluates the equity of 254 urban green spaces in China within four ranges of accessibility: 1 km, 2.5 km, 5 km and 10 km. The study also considers urban house price in the research. The results show the following: (1) There are large differences in the accessibility of green space between different cities in China. Within the accessibility threshold of 10 km, the city with the most accessible urban green spaces has an accessibility level that is 27,813 times that of the city with the lowest accessibility. (2) Within the range of walking/cycling, there are significant inequalities in green space access in the 254 cities; the inequality of green space accessibility in most of the studied cities is at the “dangerous” level. (3) The two-step floating catchment area method indicates that the social superiority (high social class) represented by high housing prices is associated with a greater opportunity to access urban green space services. This paper highlights the main problems associated with the accessibility of urban green space in China and proposes targeted development recommendations. These recommendations provide a reference for urban managers to develop effective green space development policies and realize the optimal allocation of urban green space.

Optimization Strategy for Parks and Green Spaces in Shenyang City: Improving the Supply Quality and Accessibility

In this study, we evaluated the supply quality of parks and green spaces within the Third Ring Road area in Shenyang city by combining a landscape pattern index analysis with a principal component analysis. Moreover, a network analysis based on the ArcGIS platform was used to measure the accessibility of parks and green spaces. The research results showed that the overall supply quality of parks and green spaces (−9.55) must be improved. The supply quality levels of the four analyzed park types could be ranked as follows: garden parks (118.00) > community parks (73.67) > comprehensive parks (−16.64) > specific parks (−32.17). Among the analyzed recreation parks, the accessibility of daily recreation parks was poor, while the overall service efficiency of weekly recreation parks was better, except in a few regions. These research results can provide suggestions for future green space planning in Shenyang city. In addition, from the perspective of landscape patterns, studying the service quality of parks and green spaces can provide new ideas for further research on accessibility.

Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available.

A Geospatial Platform for Crowdsourcing Green Space Area Management Using GIS and Deep Learning Classification

Green space areas are one of the key factors in people’s livelihoods. Their number and size have a significant impact on both the environment and people’s quality of life, including their health. Accordingly, government agencies often rely on information relating to green space areas when devising suitable plans and mandating necessary regulations. At present, obtaining information on green space areas using conventional ground surveys faces a number of limitations. This approach not only requires a lengthy period, but also tremendous human and financial resources. Given such restrictions, the status of a green space is not always up to date. Although software applications, especially those based on geographical information systems and remote sensing, have increasingly been applied to these tasks, the capability to use crowdsourcing data and produce real-time reports is lacking. This is partly because the quantity of data required has, to date, prohibited effective verification by human operators. To address this issue, this paper proposes a novel geospatial platform for green space area management by means of GIS and artificial intelligence. In the proposed system, all user-submitted data are automatically verified by deep learning classification and analyses of the greenness areas on satellite imagery. The experimental results showed that the classification and analyses can identify green space areas at accuracies of 93.50% and 97.50%, respectively. To elucidate the merits of the proposed approach, web-based application software was implemented to demonstrate multimodal data management, cleansing, and reporting. This geospatial system was thus proven to be a viable tool for assisting governmental agencies to devise appropriate plans toward sustainable development goals.

Economic Valuation of Urban Green Spaces across a Socioeconomic Gradient: A South African Case Study

Urban green spaces (UGSs) may present economic contributions through increases in proximate property values, encapsulated in the proximity principle (PP). More data on the PP is required from the Global South, where the quality and equitable distribution of UGSs are important considerations. This paper investigates the PP in Potchefstroom, South Africa following a quantitative approach, by statistically analyzing municipal property valuations in three districts differentiated according to their socioeconomic status (SES). Districts are divided into sample areas where three zones are demarcated according to their proximity to a UGS. The results show that property valuations are generally higher for properties in closer proximity to UGSs in lower- and higher-income samples, but are lower in middle-income areas. Neighborhood characteristics and SES, UGS amenity and maintenance, ecosystem services and disservices, domestic garden area and residential property size may be connected to the confirmation or rejection of the PP. The rejection of the PP in middle-income areas indicates a need to improve public UGSs as amenity destinations. The results confirming the PP in low-income areas could incentivize expenditures to improve UGS area and quality to increase the willingness to pay for proximity to such spaces and, reciprocally, increase revenue from municipal property taxes.

An Impact Asymmetry Analysis of Small Urban Green Space Attributes to Enhance Visitor Satisfaction

Urban green spaces have beneficial effects on the health and well-being of citizens. Understanding the factors influencing visitor satisfaction with urban green spaces contributes to making more informed policies. Prior studies on green spaces satisfaction primarily focused on the linear correlation between small urban green space attributes and satisfaction. In this manuscript, we presented a study aimed to (1) identify the attributes of SUGS as frustrators, dissatisfiers, hybrids, satisfiers, and delighters; (2) prioritize attributes for effective satisfaction management; (3) assist managers in drafting guidelines for operational management decisions. We gathered a range of information about the users to nine SUGS in Shanghai, in 2020, via a questionnaire, and we found that safety, noise, and social interaction are improvement priorities. Squares and visitors’ behavior should not be ignored in SUGS management. Moreover, managers should carefully monitor SUGS attributes of the social environment to meet users’ expectations. The findings of this study have implications for SUGS management and future research.

Study on landscape evaluation and optimization strategy of Central Park in Qingkou Town

This article mainly discusses the evaluation and optimization of the green space utilization value of comprehensive parks used by people in dense urban areas based on the desire for green and healthy living in the postepidemic era. As a qualitative study of urban parks, this study builds an evaluation system based on the American landscape performance series and combines it with comprehensive indicators of China’s urban parks, including environmental performance (such as park planning, infrastructure, trails, and vegetation), health performance (such as cultural education, park activities, and transportation accessibility) economic performance (such as tourist consumption and stimulating the development of surrounding construction) and three other aspects: conducting a site evaluation; evaluating observed behavior, interviews and questionnaires; and performing the analytic hierarchy process–coefficient of variation weight comprehensive evaluation analysis. Additionally, the park comprehensive index, land use index, traffic convenience, park vitality index and other dynamic changes are analyzed over time. The purpose is to explore the foundation of urban parks after the epidemic. The role of the urban park environment in sustainable ecological development is verified, and appropriate optimization and improvement actions are determined.

Patterns of Urban Green Space Use Applying Social Media Data: A Systematic Literature Review

Scientific interest in the potential of urban green spaces, particularly urban parks, to improve health and well-being is increasing. Traditional research methods such as observations and surveys have recently been complemented by the use of social media data to understand park visitation patterns. We aimed to provide a systematic overview of how social media data have been applied to identify patterns of urban park use, as well as the advantages and limitations of using social media data in the context of urban park studies. We used the PRISMA method to conduct a systematic literature analysis. Our main findings show that the 22 eligible papers reviewed mainly used social media data to analyse urban park visitors’ needs and demands, and to identify essential park attributes, popular activities, and the spatial, social, and ecological coherence between visitors and parks. The review allowed us to identify the advantages and limitations of using social media data in such research. These advantages include a large database, real-time data, and cost and time savings in data generation of social media data. The identified limitations of using social media data include potentially biased information, a lack of socio-demographic data, and privacy settings on social media platforms. Given the identified advantages and limitations of using social media data in researching urban park visitation patterns, we conclude that the use of social media data as supplementary data constitutes a significant advantage. However, we should critically evaluate the possible risk of bias when using social media data.

The equigenic effect of greenness on the association between education with life expectancy and mortality in 28 large Latin American cities

BACKGROUND

Recent studies highlight the equigenic potential of greenspaces by showing narrower socioeconomic health inequalities in greener areas. However, results to date have been inconsistent and derived from high-income countries. We examined whether urban greenness modifies the associations between area-level education, as a proxy for socioeconomic status, and life expectancy and cause-specific mortality in Latin American cities.

METHODS

We included 28 large cities, >137 million inhabitants, in nine Latin American countries, comprising 671 sub-city units, for 2012-2016. Socioeconomic status was assessed through a composite index of sub-city level education, and greenness was calculated using the normalized difference vegetation index. We fitted multilevel models with sub-city units nested in cities, with life expectancy or log(mortality) as the outcome.

FINDINGS

We observed a social gradient, with higher levels of education associated with higher life expectancy and lower cause-specific mortality. There was weak evidence supporting the equigenesis hypothesis as greenness differentially modified the association between education and mortality outcomes. We observed an equigenic effect, with doubling magnitudes in the violence-related mortality reduction by education in areas with low greenness compared to medium-high greenness areas among men (16% [95% CI 12%-20%] vs 8% [95% CI 4%-11%] per 1 SD increase in area-level education). However, in contradiction to the equigenesis hypothesis, the magnitude in cardiovascular diseases (CVD) mortality reduction by education was stronger in areas with medium-high greenness compared to areas with low greenness (6% [95% CI 4%-7%] vs 1% [95% CI -1%-3%] and 5% [95% CI 3%-7%] vs 1% [95% CI -1%-3%] per 1 SD increase in area-level education, in women and men, respectively). Similarly, each 1-SD increase in greenness widened the educational inequality in life expectancy by 0.15 years and 0.20 years, in women and men, respectively. The equigenic effect was not observed in violence-related mortality among women and in mortality due to communicable diseases, maternal, neonatal and nutritional conditions (CMNN).

INTERPRETATION

Our results confirm socioeconomic health inequalities in Latin American cities and show that the equigenic properties of greenspace vary by health outcome. Although mixed, our findings suggest that future greening policies should account for local social and economic conditions to ensure that greenspaces provide health benefits for all, and do not further exacerbate existing health inequalities in the region.

FUNDING

Wellcome Trust (Grant, 205177/Z/16/Z).