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FIMS: Identifying, Predicting and Visualising Food Insecurity

Lucas, Benjamin; Smith, Andrew; Smith, Gavin; Perrat, Bertrand; Nica-Avram, Georgiana; Harvey, John; Goulding, James

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Research & Knowledge Exchangedevelopment Manager

Andrew Smith

Bertrand Perrat


Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies - and this lack of data leads to any local estimates that do exist being routinely questioned by policymakers. We demonstrate a data-driven approach to address this issue, deriving national estimates of food insecurity via combination of supervised machine learning with network analysis of user behaviour, extracted from the world's most popular peer-to-peer food sharing application (OLIO). Despite long-standing theoretical links between social graph topologies and physical neighbourhoods, prior research has not considered dimensions of geography, network interactions and behaviours in the digital/analogue space simultaneously. In addressing this oversight, we produce a browser-based, interactive and rapidly updateable visualisation, which can be used to analyse the spatial distribution of food insecurity across the UK, and provide new perspective for policy research.


Lucas, B., Smith, A., Smith, G., Perrat, B., Nica-Avram, G., Harvey, J., & Goulding, J. (2020). FIMS: Identifying, Predicting and Visualising Food Insecurity. In WWW '20: Companion Proceedings of the Web Conference 2020 (190-193).

Conference Name The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
Conference Location Taipei Taiwan
Start Date Apr 20, 2020
End Date Apr 24, 2020
Acceptance Date Mar 1, 2020
Online Publication Date Apr 1, 2020
Publication Date Apr 1, 2020
Deposit Date Jun 8, 2020
Publicly Available Date Jul 31, 2020
Publisher Association for Computing Machinery (ACM)
Pages 190-193
Book Title WWW '20: Companion Proceedings of the Web Conference 2020
ISBN 9781450370240
Keywords CCS CONCEPTS • Computing methodologies → Machine learning approaches; • Applied computing → Sociology; • Human-centered com- puting → Geographic visualization KEYWORDS Food Insecurity, Hunger, Poverty, Geospatial, Data, Visualization, Computat
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