Residential green and blue spaces may be therapeutic for the mental health. However, solid evidence on the linkage between exposure to green and blue spaces and mental health among the elderly in ...non-Western countries is scarce and limited to exposure metrics based on remote sensing images (i.e., land cover and vegetation indices). Such overhead-view measures may fail to capture how people perceive the environment on the site.
This study aimed to compare streetscape metrics derived from street view images with satellite-derived ones for the assessment of green and blue space; and to examine associations between exposure to green and blue spaces as well as geriatric depression in Beijing, China.
Questionnaire data on 1190 participants aged 60 or above were analyzed cross-sectionally. Depressive symptoms were assessed through the shortened Geriatric Depression Scale (GDS-15). Streetscape green and blue spaces were extracted from Tencent Street View data by a fully convolutional neural network. Indicators derived from street view images were compared with a satellite-based normalized difference vegetation index (NDVI), a normalized difference water index (NDWI), and those derived from GlobeLand30 land cover data on a neighborhood level. Multilevel regressions with neighborhood-level random effects were fitted to assess correlations between GDS-15 scores and these green and blue spaces exposure metrics.
The average cumulative GDS-15 score was 3.4 (i.e., no depressive symptoms). Metrics of green and blue space derived from street view images were not correlated with satellite-based ones. While NDVI was highly correlated with GlobeLand30 green space, NDWI was moderately correlated with GlobeLand30 blue space. Multilevel regressions showed that both street view green and blue spaces were inversely associated with GDS-15 scores and achieved the highest model goodness-of-fit. No significant associations were found with NDVI, NDWI, and GlobeLand30 green and blue space. Our results passed robustness tests.
Our findings provide support that street view green and blue spaces are protective against depression for the elderly in China, yet longitudinal confirmation to infer causality is necessary. Street view and satellite-derived green and blue space measures represent different aspects of natural environments. Both street view data and deep learning are valuable tools for automated environmental exposure assessments for health-related studies.
•Deep learning and street view images were used to assess streetscape green and blue space.•Street view green and blue spaces were uncorrelated with satellite-derived metrics (i.e., NDVI, NDWI, and GlobeLand30).•The mental health of elderly people seemed enhanced by exposure to street view green and blue spaces.•No evidence of depression-green and blue space associations when remote sensing-based metrics were used.•People-centric exposure assessments using street view data provide great potential for environmental health studies.
Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of ...remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithm by considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes.
The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully ...convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand, to improve computational efficiency, depthwise separable convolutions and convolution factorization are introduced to significantly decrease the number of model parameters. The proposed model is evaluated on the Inria Aerial Image Labeling Dataset and the Wuhan University (WHU) Aerial Building Dataset. The experimental results show that the proposed methods exhibit significant improvements compared with several state-of-the-art FCNs, including SegNet, U-Net, RefineNet, and DeepLab v3+. The proposed model shows promising potential for building detection from remote sensing images on a large scale.
Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions ...remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities. Using the state-of-the-art Fully Convolutional Network (FCN) and Random Forest (RF) algorithms, the proposed method provides perception estimations with errors less than 10%. The driving factor analysis from both the visual and urban functional aspects demonstrated its feasibility in facilitating local urban perception derivations. With high-throughput and high-accuracy scorings, the proposed human-machine adversarial framework offers an affordable and rapid solution for urban planners and researchers to conduct local urban perception assessments.
•PM2.5 concentration was positively associated with depression symptoms.•Physical activity, neighborly reciprocity, and sunlight mediated the association.•PM2.5 level moderated the link between ...depression and the three mediators.
Although numerous studies have speculated about the direct and indirect linkage between long-term air pollution (i.e., PM2.5) concentrations and mental health in developed countries, evidence for developing countries is limited. Our aim was to examine the mediation effects of sunlight, physical activity, and neighborly reciprocity on the association between air pollution and depression.
In a sample of 20,861 individuals in China in 2016, depression was measured using the Center for Epidemiological Studies Depression screener (CES-D) and linked to annual city-wide PM2.5 data. We used multilevel regression models to assess the associations between depressive symptoms and PM2.5 and tested the mediation of sunlight, physical activity, and neighborly reciprocity in this association. Propensity score matching was used to evaluate whether selection bias may affect the association between CES-D scores and PM2.5.
PM2.5 concentration was positively associated with depression symptoms. All mediators were significantly and negatively associated with PM2.5. Our mediation analyses indicated that physical activity, neighborly reciprocity, and exposure to sunlight are important mechanisms through which PM2.5 affects depressive symptoms.
The limitations of the present study were the cross-sectional nature of the data and modifiable areal unit problem.
Our findings suggest not only that PM2.5 is directly associated with depression, but also that this association seems to be partially mediated by physical activity, neighborly reciprocity, and sunlight.
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Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood ...environment characteristics are often subjective, costly, and time-consuming, and can be applied only on a small scale. Recent developments in deep learning algorithms and the recent availability of street view images enable researchers to assess multiple aspects of neighbourhood environment perceptions more efficiently on a large scale. This study aims to examine the relationship between each of six neighbourhood environment perceptual indicators-namely, wealthy, safe, lively, depressing, boring and beautiful-and residents' time spent on PA in Guangzhou, China.
A human-machine adversarial scoring system was developed to predict perceptions of neighbourhood environments based on Tencent Street View imagery and deep learning techniques. Image segmentation was conducted using a fully convolutional neural network (FCN-8s) and annotated ADE20k data. A human-machine adversarial scoring system was constructed based on a random forest model and image ratings by 30 volunteers. Multilevel linear regressions were used to examine the association between each of the six indicators and time spent on PA among 808 residents living in 35 neighbourhoods.
Total PA time was positively associated with the scores for "safe" Coef. = 1.495, SE = 0.558, "lively" 1.635, 0.789 and "beautiful" 1.009, 0.404. It was negatively associated with the scores for "depressing" - 1.232, 0.588 and "boring" - 1.227, 0.603. No significant linkage was found between total PA time and the "wealthy" score. PA was further categorised into three intensity levels. More neighbourhood perceptual indicators were associated with higher intensity PA. The scores for "safe" and "depressing" were significantly related to all three intensity levels of PA.
People living in perceived safe, lively and beautiful neighbourhoods were more likely to engage in PA, and people living in perceived boring and depressing neighbourhoods were less likely to engage in PA. Additionally, the relationship between neighbourhood perception and PA varies across different PA intensity levels. A combination of Tencent Street View imagery and deep learning techniques provides an accurate tool to automatically assess neighbourhood environment exposure for Chinese large cities.
Multiple mechanisms have been proposed to explain how greenery in the vicinity of people's homes enhances their mental health and wellbeing. Mediation studies, however, focus on a limited number of ...mechanisms and rely on remotely sensed greenery measures, which do not accurately capture how neighborhood greenery is perceived on the ground.
To examine: 1) how streetscape and remote sensing-based greenery affect people's mental wellbeing; 2) whether and, if so, to what extent the associations are mediated by physical activity, stress, air quality and noise, and social cohesion; and 3) whether differences in the mediation across the streetscape greenery and NDVI exposure metrics occurred.
We used a population sample of 1029 adult residents of the metropolis of Guangzhou, China, from 2016. Mental wellbeing was quantified by the World Health Organization Well-Being Index (WHO-5). Two objective greenery measures were extracted at the neighborhood level: 1) streetscape greenery from street view data via a convolutional neural network, and 2) the normalized difference vegetation index (NDVI) from Landsat 8 remote sensing images. Single and multiple mediation analyses with multilevel regressions were conducted.
Streetscape and NDVI greenery were weakly and positively, but not significantly, correlated. Our regression results revealed that streetscape greenery and NDVI were, individually and jointly, positively associated with mental wellbeing. Significant partial mediators for the streetscape greenery were physical activity, stress, air quality and noise, and social cohesion; together, they explained 62% of the association. For NDVI, only physical activity and social cohesion were significant partial mediators, accounting for 22% of the association.
Mental health and wellbeing and both streetscape and satellite-derived greenery seem to be both directly correlated and indirectly mediated. Our findings signify that both greenery measures capture different aspects of natural environments and may contribute to people's wellbeing by means of different mechanisms.
•Mechanisms underlying mental health and wellbeing benefits of greenery are not well established.•Streetscape greenery from street view images and remotely sensed vegetation index (NDVI) served as exposure measure.•Higher mental wellbeing was correlated with more greenery, independently of the measure.•Greenery–wellbeing associations were explained by mediators; for streetscape greenery, mediation was higher.•Different pathways seem to be at play across both greenery measures.
In response to carbon dioxide (CO2) emissions, numerous studies have investigated the link between CO2 emissions and urban structures, and pursued low-carbon development from the standpoint of urban ...spatial planning. However, most of previous efforts only focused on urban structures in term of two-dimensional space, whereas the vertical influence of urban buildings (three-dimensional space) plays an important role in CO2 emissions. To address this issue, we took the cities in mainland China as study case to quantitatively explore how the three-dimensional urban structure affects CO2 emissions. First, we collected the city-level CO2 emission data from a greenhouse gas emission dataset released by the China City Greenhouse Gas Working Group. Then, a series of spatial metrics were established to quantify three-dimensional urban structures based on urban building data derived from Baidu Map. On the strength of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, an extended approach and ridge regression analysis were finally utilized to investigate the consequences of three-dimensional urban structures on CO2 emissions at the city level. The results indicate that the total building volume is the largest driving force accelerating CO2 emissions due to the massive consumption of energies for human activities during rapid urbanization. Besides, urban buildings with taller height and large heat dissipation area also have significant positive effects on promoting CO2 emissions. Although a compact coverage of urban buildings at a two-dimensional scale contributes to the reduction of CO2 emissions, urban structure characterized by an intense and congested pattern in three-dimensional space can lead to more CO2 emissions because of the adverse impacts from surrounding environment and traffic congestion. Additionally, an irregular pattern of three-dimensional urban structure would help reduce CO2 emissions to some extent. Such study results highlight the importance of urban planning for the development of a low-carbon city, and suggest the compact patterns of three-dimensional urban structures should be controlled within a reasonable range to avoid more CO2 emissions caused by excessive centralization and aggregation.
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•3-D spatial structures have different impacts on CO2 emissions at urban scales.•The increase in total building volume and building height can promote CO2 emissions.•3-D urban structures with intense and congested patterns cause more emissions.•Irregular pattern of three-dimensional urban structures can help reduce CO2 emissions.
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