data science

Hinterland has previously covered the remarkable and vital work done by The Trussell Trust in running food banks. This article tells us how research in data science is helping the charity to understand where and how people are using food banks.

Over the past 18 months, the trust has been working with researchers from Hull University Business School and us at social innovation agency AAM Associates to explore how new technologies can help them to fight UK hunger. The trust wanted to explore how data science could help it with its More Than Food initiative, which looks beyond providing emergency food and towards tackling the underlying causes of hunger and poverty. To do that, they needed to know more about the people who come to them for help.

The trust’s core data includes food bank locations and individual client data, such as their names, addresses, ages and underlying causes of crisis – benefit delays, school holidays, homelessness, etc. With data science firm Coppelia the data analysis highlighted some noticeable regional variations and delivery patterns year on year.

A mapping tool was then used to show how the trust’s users are distributed geographically. Heat maps showing demand – the darker the colour, the greater the demand – have allowed us to visualise regional patterns of need.

Data science also looks to predict potential areas of “unmet” need – with the information crunched to show the number of people fed by the trust, per head of population, in each local area since 2014. Various census statistics (levels of deprivation, unemployment) were then used to pinpoint key characteristics of the areas that received the most support. The results were used to predict – again using heat maps – where need for the trust’s services may be greatest across the country.

The model is not perfect, but it already poses the question: how might the charity respond to signs of unmet need?