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

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

Equity evaluation of urban green space in the main urban area of Wuhan based on green view index.

Green space is a kind of resource welfare. The evaluation of green space equity based on green view index (GVI) is important to ensure the equitable distribution of green resources. Taking the central urban area of Wuhan as the research object, based on multi-source data such as Baidu Street View Map, Baidu Thermal Map, and satellite remote sensing images, we evaluated the equity of spatial distribution of GVI in Wuhan by using the locational entropy, Gini coefficient and Lorenz curve. The results showed that 87.6% of the points in the central urban area of Wuhan were below the level of poor green vision, which mainly concentrated in Wuhan Iron and Steel Industrial Base of Qingshan District and south of Yandong Lake. The number of points reaching an excellent level was the least (0.4%), mainly concentrated around the East Lake. The overall Gini coefficient of GVI in the central urban area of Wuhan was 0.49, which indicated that the distribution of GVI was heterogeneous. The Gini coefficient of Hongshan District was the largest at 0.64, indicating a huge gap in the distribution of GVI, while the Gini coefficient of Jianghan District was the smallest at 0.47, with a large gap in the distribution. The central urban area of Wuhan had the most low-entropy areas for 29.7% and the least high-entropy areas for 15.4%. There were two-level differences in entropy distribution within Hongshan District, Qingshan District, and Wuchang District. The nature of land use and the role of linear greenery were the main factors affecting the equity of green space in the study area. Our results could provide theoretical basis and planning reference for optimizing urban green space layout.

Do Greener Urban Streets Provide Better Emotional Experiences? An Experimental Study on Chinese Tourists.

Compared to the usual environment, the potential momentary emotional benefits of exposure to street-level urban green spaces (UGS) in the unusual environment have not received much academic attention. This study applies an online randomized control trial (RCT) with 299 potential tourists who have never visited Xi'an and proposes a regression model with mixed effects to scrutinize the momentary emotional effects of three scales (i.e., small, medium and large) and street types (i.e., traffic lanes, commercial pedestrian streets and culture and leisure walking streets). The results identify the possibility of causality between street-level UGS and tourists' momentary emotional experiences and indicate that tourists have better momentary emotional experiences when urban streets are intervened with large-scale green vegetation. The positive magnitude of the effect varies in all three types of streets and scales of intervention, while the walking streets with typical cultural attractions, have a larger impact relative to those with daily commute elements. These research results can provide guidance for UGS planning and the green design of walking streets in tourism.

Assessing streetscape greenery with deep neural network using Google Street View.

The importance of greenery in urban areas has traditionally been discussed from ecological and esthetic perspectives, as well as in public health and social science fields. The recent advancements in empirical studies were enabled by the combination of 'big data' of streetscapes and automated image recognition. However, the existing methods of automated image recognition for urban greenery have problems such as the confusion of green artificial objects and the excessive cost of model training. To ameliorate the drawbacks of existing methods, this study proposes to apply a patch-based semantic segmentation method for determining the green view index of certain urban areas by using Google Street View imagery and the 'chopped picture method'. We expect that our method will contribute to expanding the scope of studies on urban greenery in various fields.

Enhanced green view index.

Quantifying street-level greenery has been the subject of interest for researchers as it has several implications for community residents. Green View Index (GVI) is a widely used parameter to compute the greenery along the streets. However, it does not account for the health of the greenery. The new Enhanced Green View Index (EGVI) that we propose computes the amount of greenery along the streets along with the health of the greenery. • The new indicator computes street-level greenery; • Considers the health of vegetation while calculating greenery; and • Helps to study the impact of street-level greenery on community residents precisely.

[Urban thermal environment effects based on visual indices: A case study in Xuzhou City, China]. / 基于视觉指数的城市热环境效应­­ä»¥å¾å·žå¸‚为例.

Urban thermal environments are closely related to habitats, citizens' health, and sustainable development. Based on green view index (GVI), we proposed two new visual indices, construction view index (CVI) and imperious surface view index (R&PVI). Mobile observation was used to obtain urban thermal environment data, images and coordinates synchronously in Xuzhou City in late summer, including urban area (U), scenic area (S), exterior of university campus (E), and university campus inside (CUMT). We analyzed the impacts of the urban composition represented by the visual index on the urban thermal environment. The results showed that, along the sampling line, mean air temperature (Ta) was highest (30.42 ℃) and mean relative humidity (RH) was lowest (40.7%) in urban area, while mean Ta was lowest (29.35 ℃) and mean RH was highest (48.4%) in scenic area. The situation of mean wind-chill temperature (TaW) was the highest (32.95 ℃) in the urban area and the lowest (31.93 ℃) in the scenic area. As for CVI, urban area, university campus inside, exterior of university campus and scenic area ranked in descending order, while GVI showed an opposite pattern. CVI was significantly positively correlated to Ta and TaW, but negatively to RH. GVI was significantly negatively correlated to Ta and TaW, but positively to RH. R&PVI was significantly positively correlated to Ta and TaW, but not correlated to RH. CVI and GVI influenced Ta significantly, with the independent effects being 10.4% and 18.9%, and joint effects being 7.8% and 11.3%, respectively. As for RH, CVI and GVI contributed significantly as well, independent effects were 37.5% and 15.7%, and joint effects were 51.4% and 30.2%, respectively. As for TaW, the three visual indices contributed significantly, but independent and joint effects were lower than those on Ta. Moreover, visual indices contributed more on RH than Ta or TaW. The results could provide ideas for optimizing urban thermal environments and mitigating urban heat island effects, and have practical implications for urban renewal and improvement of the quality of human living environment.

Association between street view greenness and allergic rhinitis in children / 环境与职业医学

Background Studies on the association between greenness exposure and allergic rhinitis (AR) in children are mostly conducted in developed countries, and the conclusion is not consistent. Objective Using street view data to explore the association between greenness exposure and allergic rhinitis (AR) prevalence in Chinese children. Methods A cross-sectional study was conducted among 40868 children aged 2-17 years in three cities of Northeast China from 2012 to 2013, which consisted of 20886 (51.1%) boys and 19982 (48.9%) girls. The information of AR prevalence was obtained through questionnaire. Based on downloaded street view images from Tencent Maps, a green view index (GVI) of green vegetation (trees and grass) within 800 m and 1000 m buffer of the participants' schools was calculated by using artificial intelligence, and it was used as a surrogate of the greenness exposure. A mixed-effect logistic regression model was used to estimate the odds ratio (OR) of AR prevalence in children for per increase of inter-quartile range (IQR) of GVI. In addition, according to ambient PM2.5 concentration, the participants were divided into a low PM2.5 exposure group (≤56.23 μg·m−3) and a high exposure group (>56.23 μg·m−3) to investigate whether PM2.5 was a modifier on the association between GVI and AR. Results The average age of the subjects was (10.40±3.68) years and 3 963 (9.7%) subjects reported diagnosed AR. Within 800 m buffer, an IQR increase in GVI for trees (IQR=0.031, OR=0.85, 95%CI: 0.81-0.90) and overall greenness (IQR=0.029, OR=0.86, 95%CI: 0.81-0.90) was associated with lower adjusted odds ratio of AR. The interaction between PM2.5 and GVI was statistically significant (P< 0.1), that is, the negative associations of trees and overall greenness with AR were observed only at low PM2.5 exposure levels. The sensitivity analysis results of GVI within 1000 m buffer was consistent with that within 800 m buffer. Conclusion Exposure to green vegetation, especially trees, may be associated with decreased risks of AR in children, and such associations may be more obvious in areas with a low PM2.5 concentration.

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

Limitations of Normalized Difference Vegetation Index (NDVI) potentially contributed to the inconsistent findings of greenspace exposure and childhood asthma. The aim of this study was to use a novel greenness exposure assessment method, capable of overcoming the limitation of NDVI to determine the extent to which it was associated with asthma prevalence in Chinese children. During 2009-2013, a cross-sectional study of 59,754 children aged 2-17 years was conducted in northeast China. Tencent street view images surrounding participants' schools were segmented by a deep learning model, and streetscape greenness was extracted. The green view index (GVI) was used to assign exposure and higher value indicates more green coverage. Mixed-effects logistic regression models were used to calculate the adjusted odds of asthma per interquartile range (IQR) increase of GVI for trees and grass. Participants were further stratified to investigate whether particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) was a modifier. An IQR increase in GVI800m for trees was associated with lower adjusted odds of doctor-diagnosed asthma (OR: 0.76; 95%CI: 0.72-0.80) and current asthma (OR: 0.82; 95%CI: 0.75-0.89). An IQR increase in GVI800m for grass was associated with higher adjusted odds of doctor-diagnosed asthma (OR: 1.04; 95%CI: 1.00-1.08) and current asthma (OR: 1.08; 95%CI: 1.02-1.14). After stratification by PM2.5 exposure level, the negative association between trees and asthma, and the positive association between grass and asthma were observed only in low PM2.5 exposure levels (≤median: 56.23 µg/m3). Our results suggest that types of vegetation may play a role in the association between greenness exposure and childhood asthma. Exposure to trees may reduce the odds of childhood asthma, whereas exposure to grass may increase the odds. Additionally, PM2.5 may modify the associations of trees and grass with childhood asthma.

Association between eye-level greenness and lung function in urban Chinese children.

BACKGROUND: Health effects of greenness perceived by residents at eye level has received increasing attention. However, the associations between eye-level greenness and respiratory health are unknown. The aim of the study was to investigate the associations between exposure to eye-level greenness and lung function in children. METHODS: From 2012 to 2013, a total of 6740 school children in seven cities in northeast China were recruited into this cross-sectional study. Forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), peak expiratory flow rate (PEF), and maximum mid expiratory flow rate (MMEF) were measured to evaluate lung function and to define lung impairment. Eye-level greenness was extracted from segmented Tencent Map street view images, and a corresponding green view index (GVI) was calculated. Higher GVIs mean more greenness coverage. Mixed-effects logistic regressions were used to estimate the health effects on lung impairment per interquartile range (IQR) increase in GVI. Linear regressions were used to estimate the associations between GVI and lung function. The health effects of ambient air pollutants were also assessed, including particulate matter with an aerodynamic diameter <1.0 µm (PM1), <2.5 µm (PM2.5), <10 µm (PM10) as well as nitrogen dioxide (NO2). RESULTS: An increase of GVI800m was associated with lung impairment in FEV1, FVC, PEF and MMEF, with ORs ranging from 0.68 (95% CI: 0.59, 0.79) to 0.83 (95% CI: 0.74, 0.93). The associations between an IQR increase of GVI800m and FEV1 (48.15 ml, 95% CI: 30.33-65.97 ml), FVC (50.57 ml, 95% CI: 30.65-70.48 ml), PEF (149.59 ml/s, 95% CI: 109.79-189.38 ml/s), and MMEF (61.18 ml/s, 95% CI: 31.07-91.29 ml/s) were significant, and PM1, PM2.5, and PM10 were found to be mediators of this relationship. CONCLUSION: More eye-level greenness was associated with better lung function and reduced impairment. However, eye-level greenness associations with lung function became non-significant once lower particulate matter air pollution exposures were considered.

The green view dataset for the capital of Finland, Helsinki.

Recent studies have incorporated human perspective methods like making use of street view images and measuring green view in addition to more traditional ways of mapping city greenery [1]. Green view describes the relative amount of green vegetation visible at street level and is often measured with the green view index (GVI), which describes the percentage of green vegetation in a street view image or images of a certain location [2]. The green view dataset of Helsinki was created as part of the master's thesis of Akseli Toikka at the University of Helsinki [3]. We calculated the GVI values for a set of locations on the streets of Helsinki using Google Street View (GSV) 360° panorama images from summer months (May through September) between 2009 and 2017. From the available images, a total of 94 454 matched the selection criteria. These were downloaded using the Google application programming interface (API). We calculated the GVI values from the panoramas based on the spectral characteristics of green vegetation in RGB images. The result was a set of points along the street network with GVI values. By combining the point data with the street network data of the area, we generated a dataset for GVI values along the street centre lines. Streets with GVI points within a threshold distance of 30 meters were given the average of the GVI values of the points. For the streets with no points in the vicinity (∼67%), the land cover data from the area was used to estimate the GVI, as suggested in the thesis [3]. The point and street-wise data are stored in georeferenced tables that can be utilized for further analyses with geographical information systems.

How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index.

Street greenery, an important urban landscape component, is closely related to people’s physical and mental health. This study employs the green view index (GVI) as a quantitative indicator to evaluate visual greenery from a pedestrian’s perspective and uses an image segmentation method to calculate the quantity of visual greenery from Tencent street view pictures. This article aims to quantify street greenery in the area within the sixth ring road in Beijing, analyse the relations between road parameters and the GVI, and compare the visual greenery of different road types. The authors find that (1) the average GVI value in the study area is low, with low-value clusters inside the third ring road and high-value clusters outside; (2) wider minor roads tend to have higher GVI values than motorways, major roads and provincial roads; and (3) longer roads, except expressways, tend to have higher GVI values. This case study demonstrates that the GVI can effectively represent the quantity of visual greenery along roads. The authors’ methods can be employed to compare street-level visual greenery among different areas or road types and to support urban green space planning and management.