Food purchase data reveals the locations of London’s ‘food deserts’

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.Author summary: Poor diets are a major risk to health, contributing to 13% of deaths in the UK, and over half of London’s residents are now overweight or obese. While the concept of ‘food deserts’ are commonly thought of as areas lacking nearby supermarkets, in urban environments the issue is more complex—as barriers such as affordability and availability are also prevalent. In this study, we analyse supermarket transaction data from 1.6 million London customers to understand how food purchase patterns vary across the city. We apply an unsupervised statistical method to identify a dominant purchasing pattern, which distinguishes between diets high in sugar and carbohydrates to those richer in fibre and protein. We then explore how these patterns relate to local conditions using geographically weighted regression, finding that both the drivers of food deserts and the demographics most affected by them vary widely across London. This study highlights the value of large-scale consumer data for understanding urban health challenges and provides a new data-driven way to identify areas where barriers to healthy diets exist.