Urban greenery plays a pivotal role in urban environments, impacting the environmental well-being and people’s comfort. Several studies have demonstrated a strong link between urban greenery and socioeconomic status but still lack an analysis of greenery on uneven distribution in social vulnerability. This study assesses how multi-level greenery rates distribute and associate the social vulnerability of people in 429 census tracts in the Seattle metropolitan area. It integrates multi-source urban informatics data, including remote sensing data and street view imagery, to identify various vegetation types. Then, it uses the interpretable machine learning model to explore the relationship between street-level green space distribution and community vulnerability. The results show a serious problem of uneven distribution of green spaces in urban centers since urban areas are built up and fragmented the landscape. Areas with low urban greening in the Seattle area have higher rates of poverty, unemployment, racial segregation, and housing overcrowding. Besides, greening features like street green views, which are more related to human perception, have a great association with social vulnerability. These findings contribute to the urban green spaces to better promote community equity and vulnerability.

中文翻译：

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城市绿化分布及其与社会脆弱性的关系


城市绿化在城市环境中起着举足轻重的作用，影响着环境福祉和人们的舒适度。几项研究表明，城市绿化与社会经济地位之间存在密切联系，但仍然缺乏对绿化对社会脆弱性分布不均的分析。本研究评估了多级绿化率如何分布和关联西雅图大都会区 429 个人口普查区中人们的社会脆弱性。它集成了多源城市信息学数据，包括遥感数据和街景图像，以识别各种植被类型。然后，它使用可解释的机器学习模型来探索街道级绿地分布与社区脆弱性之间的关系。结果表明，由于城市地区的建设和景观的碎片化，城市中心绿地分布不均存在严重问题。西雅图地区城市绿化程度低的地区贫困率、失业率、种族隔离率和住房过度拥挤率较高。此外，街道绿化景观等绿化特征与人类感知更相关，与社会脆弱性密切相关。这些发现有助于城市绿地更好地促进社区公平和脆弱性。