To complement traditional dietary surveys, which are costly and of limited scale, researchers have resorted to digital data to infer the impact of eating habits on people's health. However, online studies are limited in resolution: they are carried out at country or regional level and do not capture precisely the composition of the food consumed. We study the association between food consumption (derived from the loyalty cards of the main grocery retailer in London) and health outcomes (derived from publicly-available medical prescription records of all general practitioners in the city). The scale and granularity of our analysis is unprecedented: we analyze 1.6B food item purchases and 1.1B medical prescriptions for the entire city of London over the course of one year. By studying food consumption down to the level of nutrients, we show that nutrient diversity and amount of calories are the two strongest predictors of the prevalence of three diseases related to what is called the "metabolic syndrome": hypertension, high cholesterol, and diabetes. This syndrome is a cluster of symptoms generally associated with obesity, is common across the rich world, and affects one in four adults in the UK. Our linear regression models achieve an R 2 of 0.6 when estimating the prevalence of diabetes in nearly 1000 census areas in London, and a classifier can identify (un)healthy areas with up to 91% accuracy. Interestingly, healthy areas are not necessarily well-off (income matters less than what one would expect) and have distinctive features: they tend to systematically eat less carbohydrates and sugar, diversify nutrients, and avoid large quantities. More generally, our study shows that analytics of digital records of grocery purchases can be used as a cheap and scalable tool for health surveillance and, upon these records, different stakeholders from governments to insurance companies to food companies could implement effective prevention strategies.
We present the Tesco Grocery 1.0 dataset: a record of 420 M food items purchased by 1.6 M fidelity card owners who shopped at the 411 Tesco stores in Greater London over the course of the entire year of 2015, aggregated at the level of census areas to preserve anonymity. For each area, we report the number of transactions and nutritional properties of the typical food item bought including the average caloric intake and the composition of nutrients. The set of global trade international numbers (barcodes) for each food type is also included. To establish data validity we: i) compare food purchase volumes to population from census to assess representativeness, and ii) match nutrient and energy intake to official statistics of food-related illnesses to appraise the extent to which the dataset is ecologically valid. Given its unprecedented scale and geographic granularity, the data can be used to link food purchases to a number of geographically-salient indicators, which enables studies on health outcomes, cultural aspects, and economic factors. Background & SummaryTesco is a British multinational grocery and general merchandise retailer. In 2015, it was 9th highest-grossing retailer in the world, with 81B in global revenue 1 and the biggest grocery retailer in UK, with 28% of market share 2 . Tesco operates a loyalty scheme where customers apply for a Clubcard that is used for both in-store and online purchases to accumulate points that can be later spent to redeem prizes or discount vouchers. With the customer consent, the record of their purchases is archived and anonymously linked to their Clubcard number. In this paper, we focus on the in-store purchases done in the 411 Tesco shops within the boundaries of Greater London during the entire year of 2015. We present aggregated and privacy-preserving data views that combine individual purchases at different spatial granularities, from Lower Super Output Areas (containing around 2,000 residents each, on average) to Boroughs (more than 250k residents, on average).Despite the importance of studying food consumption at scale, there is little data about what people actually eat over long periods of time. The fine-grained geographical information included in Tesco Grocery 1.0 is the key to link food consumption data of an entire city to any attribute that can be measured at the level of statistical census areas. These include cultural aspects (ethnicity 3 , migration 4,5 ), societal aspects (youth alcohol use 6 ), economic factors (deprivation 7 , inequality 8 ), health determinants (medical prescriptions 9 , health awareness and daily habits 10,11 ), and social media discourse (textual 12 or visual 13 descriptors of geo-referenced posts).Several studies mined grocery sales data (which has not been made publicly available) to, for example, build recommender systems that are able to suggest what people might like based on their past purchases 14-16 , or establish whether healthy foods tend to be pricey 17 and, ultimately, whether their purchase tends to be mediated by pri...
Abstract-Thanks to advances in mobile technology, modern mobile devices have become essential companions, assisting their users in attaining their daily tasks. It will not be long before these devices will become recommending companions, advising users about what data (e.g., restaurants) and what services (e.g., podcast channels) they may enjoy in the local area at the present time. Because of the very nature of the items (both data and services) being suggested (i.e., location dependent and mobile with respect to the consuming user), recommendations cannot be computed on central servers and then pushed to the users. Rather, a novel decentralised mobile recommender service will have to be developed and deployed; instead of relying on global knowledge about users' profiles, such service will have to exploit the wisdom of local communities to compute recommendations. Moreover, because of resource limitations of mobile devices, the algorithms it will employ will have to be computationally light. In this paper, we propose diffeRS, a totally decentralised mobile recommender service specifically designed for pervasive environments. diffeRS crafts a virtual view of the local community's preferences, by exchanging users' profiles via radio technology (e.g., Bluetooth) during periods of colocation. Profiles are stored locally and recommendations are computed using a lightweight algorithm.As our experimental evaluations demonstrate, diffeRS achieves an accuracy and coverage that are comparable to those of centralized recommender systems in use today. .
Service providers as we know them nowadays are the always-on "static" web service providers, that aim at Five9 availability (99.999%). Formal, or de-facto, standards, such as WSDL and BPEL, have become technology enablers for the easy discovery, use and coordination of such services. However, we envisage tomorrow's services to become increasingly pervasive, being deployed within buildings, transport systems, markets, as well as people portable devices. Such services will be, by their own nature, simple and fine grained; as a consequence, service composition will become crucial to deliver rich functionalities that satisfy end users requests. Composing services in mobile environments opens up significant challenges. In particular, the Five9 availability assumption no longer holds: the higher the dynamic nature of the environment, the higher the chances that services will move out-of-reach before the composition completes, causing the service as a whole to fail. We argue that, in order to enable the successful completion of compound services, the reliability of the composition must be measured and reasoned about. In order to do so, we propose to dynamically deploy a prediction model to estimate the duration of colocation between component services. These estimates are fed in input to a service composition semantics reasoner, which then autonomically selects those providers, within the current environment, that maximise the chances of successful compound service completion. We demonstrate the positive impact that the reliability reasoning has onto the ratio of successfully completed compound services in a typical human movement scenario.
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