Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies. Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines heterogeneous features related to socio-demographics, population activity, mobility, and the built environment and their non-linear interactions to examine intra- and inter-city disparity in prevalence of four disease types: obesity, diabetes, cancer, and heart disease. Features related to population activity, mobility, and facility density are obtained from large-scale anonymized mobility data. These features are used in training and testing graph attention network (GAT) models to capture non-linear feature interactions as well as spatial interdependence among neighborhoods. We tested the models in five U.S. cities across the four disease types. The results show that the GAT model can predict the health status of people in neighborhoods based on the top five determinant features. The findings unveil that population activity and built-environment features along with socio-demographic features differentiate the health status of neighborhoods to such a great extent that a GAT model could predict the health status using these features with high accuracy. The results also show that the model trained on one city can predict health status in another city with high accuracy, allowing us to quantify the inter-city similarity and discrepancy in health status. The model and findings provide novel approaches and insights for urban designers, planners, and public health officials to better understand and improve health disparities in cities by considering the significant determinant features and their interactions.
翻译:了解城市健康状况差异的决定因素对于为城市设计和规划以及公共卫生政策提供信息十分重要。多种不同的城市特征可以调节城市和城市不同城市不同街区疾病流行情况。本研究审查了社会人口、人口活动、流动性、建筑环境及其非线性互动等与社会人口、人口活动、人口活动、流动性、建筑环境及其非线性互动有关的不同特征,以检查城市内部和城市间在四种疾病(肥胖症、糖尿病、癌症和心脏病)发病率方面的差距。与人口活动、流动性和设施密度有关的特点,来自大规模匿名流动数据。这些特征可用于培训和测试图表关注网络(GAT)模型,以了解非线性特征相互作用以及各社区之间的空间相互依存。我们研究了五个美国城市的社会人口、人口活动、流动、建筑环境密度等模式及其非线性互动情况。结果显示,GAT模型可以根据五大决定因素预测社区居民的健康状况状况,同时从社会-人口结构特征中区分社区健康状况。这些特征用于培训和城市之间空间状况的模型可以以更精确的方式预测城市健康状况。通过这些特征、经过培训的准确性来预测城市健康状况。