This article - reposted from Guardian Voluntary Sector Network - highlights an AAM project that allows The Trussell Trust to map current demand, highlight places with unmet need and predict future patterns.
The Trussell Trust’s latest statistics show that food bank use in the UK remains at record levels, and that the supply of emergency food parcels has increased by 2% in the last year.
Amid the passionate discussion that these figures arouse, one comment by David McAuley, the trust’s chief executive, deserves attention: “Reducing UK hunger will require a collective effort from the voluntary sector, government, the Department for Work and Pensions, businesses and the public. The Trussell Trust is keen to work with all these groups to find solutions that stop so many people needing food banks in future.”
An admirable aspiration, but how can you unite these disparate agencies around a problem with such jagged edges and complex causes and effects? I believe the answer might lie in data science.
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 initial research phase identified 56 technologies that are already being used in food banks globally, and we held workshops with Trussell Trust staff to identify what might help them most with their operational needs. While digital fundraising methods and volunteer management systems were of great interest, most ideas felt like “nice-to-haves” rather than game changers. There was one exception – data analysis.
Charities all over the world, such as Citizens Advice and the South Carolina Campaign to Prevent Teen Pregnancy, and public bodies including the New Orleans Fire Department, have shared encouraging results from data analysis projects.
This aligned well with the trust’s More Than Food initiative, which looks beyond providing emergency food and towards tackling the underlying causes of hunger and poverty. To do that, they need 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 recruited to the project, and having taken advice directly from the Information Commissioner’s Office on appropriate data security, our initial analysis highlighted some noticeable regional variations and delivery patterns year on year.
On the map
A mapping tool was 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.
Even a cursory glance at the map suggests countless questions: how far do most clients travel? Are food banks located in the areas of greatest need? Why are particular crisis types more prevalent in certain areas?
More creatively, the platform also looks to predict potential areas of “unmet” need. The data was 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?
For charities – especially those on the frontline helping people in crisis – this project highlights the importance of collecting data, and how the potential for aligning open data sets (a charge led well by the Open Data Institute) with that data is rapidly becoming a reality.
There are two next steps for the Trussell Trust in relation to this work – and neither is easy. First, they need to answer some of the many questions highlighted here. Data can provide new insights, but how do you act on them? How can you change focus, practice or even business model to respond to the issues?
More strategically, the trust is beginning to look outside of its own walls for solutions. Sharing data with other charities – safely, securely and with the appropriate permissions – could create a significant step forward in understanding and answering local need.
The causes of poverty are complex, brittle and often unique to the individual case. Each cry for help, each visit to a food bank, each call to a debt advice line, paints an individual brush stroke in a much richer picture of local need.
Pooling data might provide users of crisis services with some small comfort that they are helping to deepen this country’s understanding of poverty, and in doing so, perhaps shielding others from its harsh glare.