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Introduction & BackgroundThe ability of policymakers to positively transform food environments requires robust empirical 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 authorities, due to the prohibitive costs and logistical challenges of administering longitudinal surveys. This study builds on existing research and a key pilot study developed in partnership between Olio - a food-sharing app with 7 million registered 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.
Objectives & ApproachOur 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-insecurity across London. We used Olio's extensive network of users to distribute 2,849 surveys, asking respondents across London about their experiences of food-insecurity. The survey was distributed online, adapting the US Department of Agriculture Food Security module. Respondents were asked about their experiences, including (1) eating smaller meals or skipping meals, (2) being hungry but being unable to eat, and (3) not eating for a whole day, because they could not afford food or because they could not get access to food. Using the household, rather than the individual-level version of the food insecurity module helped shed light on the experience of vulnerable groups - such as children.
Relevance to Digital FootprintsThe survey responses provided a ground truth about users' experiences of destitution. Deprivation metrics and digital footprint data in the form of food-acquisition behavioural data were then used in a Random Forests Machine Learning model to predict whether households were experiencing food-insecurity, achieving high accuracy. Food-sharing data from almost 50,000 London-based users active on Olio's platform were then used to identify relevant food-seeking behaviours and aggregate recognised instances of food-insecurity at neighbourhood (MSOA) level.
Conclusions & ImplicationsTo identify and rank relevant socio-demographics and food-seeking behaviours most informative for describing food-insecurity an extensive variable selection analysis was performed. The resulting SHAP (SHapley Additive exPlanations) values showed that a combination of food solicitation and the general deprivation of an area were important predictors of food-insecurity.

Introduction & BackgroundPrevious literature has found that financially vulnerable households often make involuntary spending trade-offs between necessities, particularly energy and food. This effect is especially pronounced during winter, when homes require greater energy expenditure to maintain an adequate temperature. Despite frequent colloquial and journalistic references to the "heat-or-eat dilemma", there remains limited recent empirical evidence of this phenomenon in the UK. This is a considerable knowledge gap, given recent economic hardship and rising energy costs.
Objectives & ApproachThis study uses survey data (n=2877), collected during winter 2022 in London, UK, to analyse the sociodemographic and behavioural characteristics of respondents affecting self-reported heat-or-eat trade-offs. The survey was deployed via users of the food-sharing app, OLIO, and quota restraints were enforced to ensure the socioeconomic representativeness of the sample (based on Index of Multiple Deprivation). The survey question of interest (i.e., the dependent variable) was ""in the past year, how frequently did your household reduce or forego expenses for basic household necessities, such as medicine or food, in order to pay an energy bill?"" and responses were recorded using a discrete, ordinal scale: never; 1-2 months; some months but not every month; almost every month.
Given the nature of the dependent variable, the Random Parameters Ordered Probit (RPOP) model, a statistical modelling framework used in the case of discrete, ordered outcomes, was considered suitable. The RPOP approach allows the effect of various independent variables to be explored, which in this case, are sociodemographic and behavioural characteristics of respondents.
Relevance to Digital FootprintsThe relevance to the digital footprints theme is embedded in the study's aim: to draw insights into social issues through the analysis of sociodemographic and behavioural data retrieved from the users of a mobile app.
ResultsInitial results show that a considerable proportion (~37%) of the sample made heat-or-eat trade-offs at least one month of the year. Interestingly, this is several times higher than the official rate of fuel poverty in London (11.9%), suggesting that the government's fuel poverty metric fails to capture many homes that display signs of energy unaffordability. The RPOP model estimation results show that a broad range of sociodemographic variables (including features of household composition and disability), as well as several behavioural features derived from the respondents' use of the OLIO app, including the frequency of app usage and food requests, significantly affected the likelihood of heat-or-eat trade-offs.
Conclusions & ImplicationsOur results can be used to guide remedial food and fuel poverty policies. It may be particularly useful to focus on the sociodemographic variables that lead to heat-or-eat trade-offs, given that the English fuel poverty metric places arguably unjust focus on a home's energy efficiency, rather than occupant characteristics.

Introduction & BackgroundIn England, The Indices of Deprivation (IoD) are a widely used and referenced measure to assess local levels of deprivation across a range of domains, including health and disability. However, due to their complex nature and the number of inputs required to generate these measures, they are only updated infrequently. Typically every 4-5 years, with the most recent versions released in 2019 and 2015.
This study expands on previous research looking at the feasibility of using digital footprint data, in the form of retail loyalty card transactions, to predict local deprivation. This work focuses specifically on the health and disability subdomain of IoD. Our hypothesis is that retail behaviour relating to food purchases and their associated nutritional content, can be used to predict health deprivation.
Objectives & ApproachThe work utilises loyalty card data from a large UK grocery retailer. Anonymised geo-location data for loyalty card members was used to assign retail grocery transactions to individual Lower Layer Super Output Areas (LSOAs) for each of the ten quarters in the study period (July 2019 - December 2021). A nutritional lookup was developed to enable the nutritional content of food transactions to be assigned to each LSOA.
A number of metrics based on categories of food sold and their nutritional content were developed and used in a Machine Learning model, based on a Random Forest classifier, to predict areas with high levels of health related deprivation.
Relevance to Digital FootprintsThis study uses data derived from digital footprint data of grocery transactions. It demonstrates the potential for utilising digital footprint data as a proxy for traditional demographic data without the need for expensive, both in terms of time and cost, surveying to be performed.
ResultsThe random forest classifier was able to predict neighbourhoods (at the LSOA level) with the top 20% of health related deprivation. A high level of predictive power was identified (Overall accuracy 80%). SHAP (SHapley Additive exPlanations) and Model Class Reliance (MCR) were used to determine the importance of the input features. Areas with higher proportional spending on cigarettes and soft drinks and lower spending on fish, wine and fruit and vegtables were found to be associated with extreme levels of health deprivation. In terms of nutrition, two derived metrics, calories per pound spend and the obesogenicity of food purchased, were found to be important predictors of health deprivation.
Conclusions & ImplicationsDigital footprint data on grocery purchases have been shown to be highly effective at predicting areas of extreme health related deprivation at the LSOA level. Features related to proportional spend on food categories and proportions of nutrients associated with these purchases were identified as optimal for predicting health related deprivation.
The number of calories per pound spent and, to a lesser extent, the proportion spent on cigarettes, in an LSOA was found to be the most important predictor of high levels of health related deprivation.
The high level of predictive accuracy obtained offers the potential for using digital footprint data as a proxy for traditional deprivation measures. This could enable rapid and near real-time surveillance of areas with poor health outcomes compared to traditional approaches. This could allow early interventions to be put in place mitigating some of the negative impacts of health related deprivation.

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 pol-icymakers. 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.

Service research suggests homes are becoming increasingly connected as consumers automate and personalize new forms of service provision. Yet, large-scale empirical evidence on how and why consumers automate smart domestic products (SDPs) is lacking. To address this knowledge gap, we analyze 13,905 consumer-crafted, automated combinations of SDPs, totaling 1,144,094 installations, across 253 separate service providers on the web service IFTTT.com. An exploratory network analysis examines the topology of the network and an interpretive coding exercise reveals how consumers craft different styles of human-computer interaction to cocreate value. The results reveal that the SDP network is disassortative, is imbalanced, has a long-tailed degree distribution, and that popular services have high centrality across all product category combinations. We show that popular combinations of SDPs are primarily motivated by utilitarian value-seeking enacted through a preference for automated tasks outside of conscious attention, though more individualistic combinations are slightly more likely to be hedonistically inclined. We conclude by showing how these consumer-crafted forms of service provision within domestic environments reveal design redundancy and opportunities for service innovation.

Service research suggests homes are becoming increasingly connected as consumers automate and personalize new forms of service provision. Yet large-scale empirical evidence on how and why consumers automate smart domestic products is lacking. To address this knowledge gap we analyze 13905 consumer-crafted, automated combinations of smart domestic products, totalling 1,144,094 installations, across 253 separate service providers on the Web service IFTTT.com. An exploratory network analysis examines the topology of the network and an interpretive coding exercise reveals how consumers craft different styles of human-computer interaction to co-create value. The results reveal the smart domestic product network is disassortative, imbalanced, has a long-tailed degree distribution, and that popular services have high centrality across all product category combinations. We show that popular combinations of smart domestic products are primarily motivated by utilitarian value seeking enacted through a preference for automated tasks outside of conscious attention, though more individualistic combinations are slightly more likely to be hedonistically inclined. We conclude by showing how these consumer-crafted forms of service provision within domestic environments reveal design redundancy and opportunities for service innovation.

Introduction & BackgroundThe shift towards plant-based diets remains on the rise. Several observational studies have suggested that adopting these diets can result in some fundamental nutrient deficiencies, such as iodine deficiency. This can be especially harmful to a developing fetus, leading to growth impairment and, in extreme cases, cretinism. Nonetheless, understanding of long-term health consequences of this shift remains a challenge, particularly regarding nutritional impact at broader population scales.
Objectives & ApproachOur study focuses on the effects of transitioning to plant-based diets on the purchasing and assumed intake of essential nutrients like iodine, calcium, and vitamin B12. We analysed anonymized shopping records of 10,626 loyal customers who switched from regular milk to alternative milk products. By matching the transaction data with nutritional information, we estimated the weekly nutrient purchases before and after the transition. Our data was collected from a national food retailer across the UK.
Relevance to Digital FootprintsLoyalty-card transactional logs held by retailers reflect a valuable lens into nutritional intake data. This data can provide insight into the potential impact of purchasing behaviours, such as the potential health effects of dietary changes at scale. Our approach leverages AI modelling accompanied by rigorous variable importance methods to uncover potentially hidden insights on the impact of nutritional shifts to plant-based goods.
ResultsResults indicate that 83% of individuals deemed regular customers, who switched to plant-based milk, experienced a decrease in their purchases of iodine (44%), calcium (30%), and vitamin B12 (39%) from their normal purchase patterns at the retailer. Additionally, 57% of these individuals decreased their iodine purchases by more than 50%. The reduction in these nutrients is even more significant for those who switch to plant-based dairy and meat products.
Conclusions & ImplicationsOur research indicates that dietary changes, such as switching from purchasing regular milk to alternative milks, may lead to insufficient intake of essential dietary nutrients such as iodine. This represents a significant potential health concern for the public if not remediated, especially in countries that do not require salt to be fortified with iodine.