Urban Population Health

Urban population health research focuses on understanding and mitigating health disparities within and between cities, aiming to improve public health outcomes through informed urban planning and policy. Current research utilizes machine learning models, such as XGBoost and Graph Attention Networks, to analyze complex interactions between socio-demographic factors, the built environment, and disease prevalence, identifying key determinants like population density, green space, and access to resources. These studies leverage large datasets and advanced analytical techniques to reveal causal pathways linking urban characteristics to health outcomes, providing actionable insights for urban design and public health interventions. The resulting knowledge supports evidence-based decision-making to reduce health inequalities and improve the overall well-being of urban populations.