Study on the Impacts of Urban Density on Common Chronic Diseases in American Metropolitan Areas
Abstract：
Existed research has revealed that urban density would influence the incidence rates of common chronic diseases, but lack theoretical mechanism and empirical studies. The development of artificial intelligence (AI) and the urban big data provide new opportunities to explore original ideas and methods of complex correlations. Aiming at identifying the relationships between urban density and chronic diseases, this paper takes metropolitan census tracts in U.S. as the study areas and analyzes three dimensions of population density, built environment density, and activity density. With the help of data visualization, this paper initially investigates the trends between variables. By building several machine learning (ML) models and using interpretable methods, this paper then uncovers the key influencing features, describes non-linear relationships, finds key thresholds, explains the interactions of factors, and discovers spatial distinction. Finally, this paper summarizes similarities and differences of common chronic diseases. The results verify the significant impacts of urban density on chronic diseases, interpret the complicated relationships and regional disparity. The conclusions are of great importance for constructing health cities, guiding sustainable development, and promoting livability.