A Geo-computational Analysis of Health Inequality in London

Abstract It utilised multi-sourced urban datasets on health and socio-economic conditions in London, in order to map out physical health and mental health spatio-temporal patterns in the city. Recognising the spatial patterns trajectory changes of obesity and mental health prevalence among adults and children in London, facilitated the investigation on influential conditions with selected demographic, socio-economic and environmental factors at varied scales. Spatial models were compared to identify the optimal model capturing neighbourhood spillover effect, and further to find significantly influential factors in urban health, such as age group, green space access, household deprivation, income deprivation, and air quality. The findings underscore the necessity for tailored and localised public health interventions to effectively combat obesity and urban mental health conditions; highlight the importance of spatial heterogeneity and regional variations; and suggest adaptive strategies for local public health strategies embracing geographically informed evidence.