Mapping UK Food Insecurity
Remarkably, despite being the 5th wealthiest country in the world, the UK is having vast amounts of food-waste, at the same time as seeing increasing levels of food-insecurity. Food-insecurity, in particular, is made worse by the lack of large-scale longitudinal government statistics without which interventions are occurring in the dark.
In response to this, N/LAB has worked with food-sharing app OLIO, to develop AI to build the first-ever-data-driven food-insecurity map for the UK.
The ongoing COVID-19 pandemic has further disrupted people’s access to a healthful diet, while the lack of real-time and longitudinal data about the prevalence of food-insecurity has made it even more challenging for local authorities to act.
Despite being the 5th wealthiest country in the world, the UK is experiencing increasing levels of food-insecurity.
In this programme of work at N/LAB, we examined the relationship between food sharing and deprivation generally. We then developed a machine learning model that could accurately estimate food-insecurity based upon aggregated food seeking behaviours by OLIO users in the UK. We found that data from food sharing systems such as OLIO’s can help quantify a previously hidden aspect of deprivation; with collaboration from Greater London Assembly, we also developed a proof-of-concept map to help policymakers, local authorities, and social scientists monitor, evaluate, and intervene, to reduce the burden of hunger.
Our work addressing the issue of food-insecurity in the UK was initiated in 2019 as part of a Knowledge Transfer Partnership with OLIO. With more than 3 million users, OLIO is an app that connects neighbours with each other, and with local businesses, so surplus food can be shared, not thrown away. In 2020, a a proof-of-concept food-insecurity predictive model and map covering all LSOA’s across the UK was developed. In 2021 a pilot was undertaken with Havering Borough Council in London, as part of an Innovate UK Sustainable Innovation Fund, producing the first data-driven map of food-insecurity to be used in real-world practice.
Our food-insecurity model was developed based upon fine-grained and anonymised proprietary data from OLIO, following three key steps:
- Building on machine learning and food-sharing research developed at N/LAB (Harvey et. al, 2020; Nica-Avram, et al., 2020), we started by labelling instances of food-insecurity in collaboration with qualitative experts.
- This was followed by pseudonymized user modelling across three core dimensions: (1) users’ neighbourhood deprivation characteristics, (2) food-seeking behaviours reflected via OLIO, and (3) network features, reflecting their location within the food sharing network topology as a whole.
- The resulting AI models can then be applied to the whole sample population, analyse real-time food-seeking behaviours and recognise instances of food-insecurity. These predictions can then be aggregated into geographic clusters, resulting in food-insecurity predictions from local to national levels, not from surveys or proxies, by real-world behaviours.
Our approach is summarised in the diagram below:
With high levels of precision and recall, our models suggest that about 5% of OLIO’s users in the UK are experiencing food-insecurity.Â Those most in need also live in areas affected by health and education deprivation, and are characterised by a particular profile of network usage. They are not sharers on the whole; they are takers; reciprocity is largely absent; there is also a dependency effect within the network, with recipients in acute need relying on the highly active community of volunteers prepared to travel to enable re-distribution.
Our initial prototype produced in 2021 with Havering Borough Council is live and being used to inform interventions for addressing food-insecurity. With increased interest from councils across the UK, the ambition going forward is to develop a nation-wide food-insecurity map to help policymakers, local authorities, and social scientists monitor, evaluate, and intervene, to reduce the burden of hunger.
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Clarke, T., 2021. Hunger: an ugly truth in the Covid mirror. Medium.com [Online]. URL: https://tessa-92849.medium.com/hunger-an-ugly-truth-in-the-covid-mirror-d5ddfdbf1760