@article{deee0f1262364537accddf7c4209f240,
title = "Street view greenness is associated with lower risk of obesity in adults: Findings from the 33 Chinese community health study",
abstract = "Background: Urban greenness may protect against obesity, but very few studies have assessed {\textquoteleft}street view{\textquoteright} (SV) greenness metrics, which may better capture people's actual exposure to greenness compared to commonly-used satellite-derived metrics. We aimed to investigate these associations further in a Chinese adult study. Methods: Our analysis included 24,845 adults in the 33 Chinese Community Health Study in 2009. SV images from Tencent Map, segmented by machine learning algorithms, were used to determine the average proportion of green vegetation in SV images at community level in 800m road network buffer. Sensitivity analyses were performed with an alternative buffer size. Overall greenness was assessed as normalized difference vegetation index (NDVI) in 800 m buffer. We used predicted PM2.5 and monitored NO2 as proxies of air pollution. Body mass index (BMI), waist circumference (WC) and hip circumference (HC) were regressed on SV greenness by generalized linear mixed models, with adjustment for covariates. Mediation analyses were performed to assess the mediation effects of air pollution. Results: Each interquartile range (IQR = 3.6\%) increase in street view greenness was associated with a 0.15 kg/m2 (95\% CI: −0.22, −0.09) decrease in BMI and 0.23 cm (95\% CI: −0.35, −0.11) reduction in HC, and was associated with 7\% lower odds of overweight (OR = 0.93, 95\% CI:0.90, 0.96) and 18\% lower odds of obesity (OR = 0.82, 95\% CI:0.76, 0.89). Similar effect estimation was observed compared with commonly-used NDVI measures. PM2.5 and NO2 mediated 15.5\% and 6.1\% of the effects of SV greenness with BMI, respectively. Conclusions: Our findings suggest beneficial associations between community-level SV greenness and lower body weight in Chinese adults. The effects were observed in women but not in men. Air pollution may partially mediate the association. These findings may have implications to support efforts to promote greening in urban areas.",
keywords = "Artificial intelligence, Greenness, Machine learning, Obesity, Overweight, Street view",
author = "Xiang Xiao and Ruoyu Wang and Knibbs, \{Luke D.\} and Bin Jalaludin and Joachim Heinrich and Iana Markevych and Meng Gao and Xu, \{Shu Li\} and Wu, \{Qi Zhen\} and Zeng, \{Xiao Wen\} and Chen, \{Gong Bo\} and Hu, \{Li Wen\} and Yang, \{Bo Yi\} and Yunjiang Yu and Dong, \{Guang Hui\}",
note = "Funding Information: This work was supported by the National Natural Science Foundation of China ( 81872582 , and 81872583 ), the National Key Research and Development Program of China ( 2018YFE0106900 , and 2018YFC1004300 ), the Guangdong Provincial Natural Science Foundation Team Project ( 2018B030312005 ), the Science and Technology Program of Guangzhou ( 201807010032 , and 201803010054 ), the Science and Technology Program of Zhongshan ( 2019B1110 ), and the Natural Science Foundation of Guangdong Province ( 2017A090905042 , 2018B05052007 , 2019A050510017 , and 2020A1515011131 ). Funding Information: Street view images capture different aspects of greenery (e.g., street trees, bushes or shrubs, green walls, etc.) compared to NDVI, which captures green space areas from a bird's eye perspective. One potential explanation for the effect difference is that weight status measures were, to a greater extent, affected by NDVI representing parks where people are more likely to be involved in physical activity (Li and Ghosh, 2018). However, we acknowledge that these explanations are speculative and warrant further corroboration to establish their plausibility. Compared with satellite-derived NDVI, this new approach, SV greenness estimated using a machine learning segmentation algorithm provides information on a different dimension of urban greenspace. On the other hand, satellite-derived greenness is substantially less computationally intensive and does not require the same level of user skill. Furthermore, in places where street view image services are unavailable, satellite images can still be obtained and used for greenness exposure assessment. Such images are also available back to the 1990s, as opposed to the {\textquoteleft}snapshot{\textquoteright} offered by the SV images in our study. Overall, our findings support the incorporation of both bird's eye and SV estimates of greenness into epidemiological studies, where there is scope to do so, as they offered complementary exposure information in the Chinese cities we assessed.This work was supported by the National Natural Science Foundation of China (81872582, and 81872583), the National Key Research and Development Program of China (2018YFE0106900, and 2018YFC1004300), the Guangdong Provincial Natural Science Foundation Team Project (2018B030312005), the Science and Technology Program of Guangzhou (201807010032, and 201803010054), the Science and Technology Program of Zhongshan (2019B1110), and the Natural Science Foundation of Guangdong Province (2017A090905042, 2018B05052007, 2019A050510017, and 2020A1515011131). Publisher Copyright: {\textcopyright} 2021 Elsevier Inc.",
year = "2021",
month = sep,
doi = "10.1016/j.envres.2021.111434",
language = "English",
volume = "200",
journal = "Environmental Research",
issn = "0013-9351",
publisher = "Elsevier",
}
SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities