OLS regression estimates and associated p-values.

A nutritious diet is essential for preventing diet-related diseases. In the UK, obesity and related diseases are leading causes of death, with more than half of London’s residents classified as overweight or obese. ‘Food deserts’ refer to areas where residents are unable to access a nutritious diet, where barriers to obtaining healthy foods are thought to underpin dietary behaviour. Previous attempts to identify ‘food deserts’ have relied on assumptions about the relationships between store locations, sociodemographic factors, and access to healthy food. These methods typically classify areas as ‘food deserts’ without any direct, quantitative link to food purchase data or dietary patterns. By utilising food purchase records from Tesco transactions, we explore the relationship between food purchasing patterns and sociodemographic factors in London, with a focus on identifying food deserts and their drivers. Food purchasing patterns vary spatially, with significant spatial clustering of nutritionally deficient food purchases across London’s boroughs. These clusters are statistically explained by sociodemographic factors using a geographically weighted regression model, which enables the exploration of how the influence of sociodemographics, walk time, and car ownership varies across different areas of London. Our findings demonstrate the potential of analysing food purchase data to identify food deserts and their drivers, and suggest that area-specific, context-sensitive interventions are necessary for the implementation of local public health strategies.