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一篇论文发表了--Using machine learning to examine street green space types at a high spatial resolution- Application in Los Angeles County on socioeconomic disparities in exposure

Yi Sun, Xingzhi Wang, Jiayin Zhu, Liangjian Chen, Yuhang Jia, Jean M. Lawrence, Luo-hua Jiang, Xiaohui Xie, Jun Wu, Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure, Science of The Total Environment, Volume 787, 2021, 147653, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2021.147653. (https://www.sciencedirect.com/science/article/pii/S0048969721027248)

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Abstract:

Background Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health. Objectives This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California. Methods SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status. Results The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): −3.02, −2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities. Conclusion Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Keywords: Green space; Street view image; Machine learning; Socioeconomic status; Environmental health disparity

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