UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction
arXiv:2504.04739v3 Announce Type: replace Abstract: Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy. Traditional statistical approaches often struggle to capture complex non-linear relationships, while many machine learning methods overlook the joint roles of spatial autocorrelation and network topology in urban systems. Recent advances in GeoAI have addressed these challenges only partially, often treating spatial effects, graph structure, evaluation, and interpretab
The proliferation of urban data, coupled with advancements in graph neural networks, enables more sophisticated urban analytics previously constrained by computational and methodological limitations.
Sophisticated urban health prediction allows for proactive interventions and resource allocation, improving public welfare and urban sustainability for strategic decision-makers.
The integration of spatial, topological, and demographic factors into a single AI framework allows for more holistic and accurate urban health predictions compared to traditional isolated approaches.
- · Urban planners
- · Public health agencies
- · Smart city technology providers
- · Data scientists in urban analytics
- · Traditional statistical modeling firms
- · Legacy urban consultancy services
More precise identification of at-risk urban areas for health and social issues.
Improved efficiency in resource allocation for urban development and public health initiatives.
Enhanced resilience and sustainability of cities through data-driven policy and infrastructure planning.
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