@inproceedings { , title = {FIMS: Identifying, Predicting and Visualising Food Insecurity}, abstract = {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.}, conference = {The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020}, doi = {10.1145/3366424.3383538}, isbn = {9781450370240}, note = {Exclude from Review}, pages = {190-193}, publicationstatus = {Published}, publisher = {Association for Computing Machinery (ACM)}, url = {https://nottingham-repository.worktribe.com/output/4607565}, keyword = {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}, year = {2020}, author = {Lucas, Benjamin and Smith, Andrew and Smith, Gavin and Perrat, Bertrand and Nica-Avram, Georgiana and Harvey, John and Goulding, James} }