ML4EJ：使用可解释机器学习解码城市特征在塑造环境不公中的作用
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning
AI总结：
本研究构建可解释机器学习模型，分析城市特征对空气污染、城市热岛和洪水暴露差异的非线性影响，发现社会人口特征最为关键且模型跨区域迁移性有限，为促进社会公平的城市设计政策提供新视角。
摘要：
理解塑造环境危害暴露及其相关环境不公问题的关键因素，对于制定公平的政策措施至关重要。传统上对环境不公的视角主要集中于社会经济维度，往往忽略了异质城市特征的影响。这种局限的视角可能阻碍对环境公正的复杂本质及其与城市设计特征之间关系的全面理解。为填补这一空白，本研究创建了一个可解释的机器学习模型，以检验各种城市特征及其非线性交互作用对三种主要危害暴露差异的影响：空气污染、城市热岛和洪水。分析使用 Random Forest 和 XGBoost 对来自美国六个大都市县的数据进行模型训练和测试。模型性能用于衡量城市特征的变化在多大程度上塑造了环境危害水平的差异。此外，特征重要性分析显示，与社会人口特征相关的特征是塑造危害程度的最显著城市特征。与基础设施分布和土地覆盖相关的特征分别对城市热岛和空气污染暴露相对重要。此外，我们评估了模型在不同地区和危害间的可迁移性。结果突显了有限的可迁移性，强调了危害与地区之间复杂的差异，以及城市特征塑造危害暴露的方式。本研究获得的洞察为城市特征及其与环境危害暴露差异间的相互作用提供了新视角，为制定更综合的城市设计政策以促进社会公平和改善环境不公问题提供了参考。
英文摘要：
Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily focused on the socioeconomic dimensions, often overlooking the influence of heterogeneous urban characteristics. This limited view may obstruct a comprehensive understanding of the complex nature of environmental justice and its relationship with urban design features. To address this gap, this study creates an interpretable machine learning model to examine the effects of various urban features and their non-linear interactions to the exposure disparities of three primary hazards: air pollution, urban heat, and flooding. The analysis trains and tests models with data from six metropolitan counties in the United States using Random Forest and XGBoost. The performance is used to measure the extent to which variations of urban features shape disparities in environmental hazard levels. In addition, the analysis of feature importance reveals features related to social-demographic characteristics as the most prominent urban features that shape hazard extent. Features related to infrastructure distribution and land cover are relatively important for urban heat and air pollution exposure respectively. Moreover, we evaluate the models' transferability across different regions and hazards. The results highlight limited transferability, underscoring the intricate differences among hazards and regions and the way in which urban features shape hazard exposures. The insights gleaned from this study offer fresh perspectives on the relationship among urban features and their interplay with environmental hazard exposure disparities, informing the development of more integrated urban design policies to enhance social equity and environmental injustice issues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
