首页|Researchers from Nottingham University Business School Discuss Findings in Machi ne Learning (Foodinsecurity.london: Developing a food-insecurity prevalence map for London - a machine learning from food-sharing footprints)
Researchers from Nottingham University Business School Discuss Findings in Machi ne Learning (Foodinsecurity.london: Developing a food-insecurity prevalence map for London - a machine learning from food-sharing footprints)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news originating from Nottingham, United Kingdom , by NewsRx editors, the research stated, "Introduction & The abil ity of policymakers to positively transform food environments requires robust em pirical evidence that can inform decisions. At present, there is limited data on food-insecurity in the UK that can be used to inform interventions by local aut horities, due to the prohibitive costs and logistical challenges of administerin g longitudinal surveys." The news correspondents obtained a quote from the research from Nottingham Unive rsity Business School: "This study builds on existing research and a key pilot s tudy developed in partnership between Olio - a food-sharing app with 7 million r egistered users as of 2023, the University of Nottingham and Havering Council in 2020, which resulted in the world's first map prototype of food-insecurity. Obj ectives & Approach Our approach leverages Machine Learning methods applied to unprecedented food-acquisition behavioural data and open area-level deprivation statistics to model and predict individuals' experience of food-inse curity across London. We used Olio's extensive network of users to distribute 2, 849 surveys, asking respondents across London about their experiences of food-in security. The survey was distributed online, adapting the US Department of Agric ulture Food Security module. Respondents were asked about their experiences, inc luding (1) eating smaller meals or skipping meals, (2) being hungry but being un able to eat, and (3) not eating for a whole day, because they could not afford f ood or because they could not get access to food. Using the household, rather th an the individual-level version of the food insecurity module helped shed light on the experience of vulnerable groups - such as children. Relevance to Digital Footprints The survey responses provided a ground truth about users' experiences of destitution. Deprivation metrics and digital footprint data in the form of f ood-acquisition behavioural data were then used in a Random Forests Machine Lear ning model to predict whether households were experiencing foodinsecurity, achi eving high accuracy. Food-sharing data from almost 50,000 London-based users act ive on Olio's platform were then used to identify relevant food-seeking behaviou rs and aggregate recognised instances of food-insecurity at neighbourhood (MSOA) level."
Nottingham University Business SchoolN ottinghamUnited KingdomEuropeCybersecurityCyborgsEmerging TechnologiesMachine Learning
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