Research papers that have benefited from digital footprint data.

2022

Muggleton, N., Trendl, A., Walasek, L., Leake, D., Gathergood, J., Stewart, N. (2022) Workplace inequality is associated with status-signaling expenditure. PNAS, 119 (15) e2115196119.

2021

Bannister, A. and Botta, F., 2021. Rapid indicators of deprivation using grocery shopping data. Royal Society open science, 8(12), p.211069.

Clark, S. D. et al. (2021). Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles. Nutrients, 13(5), 1481.

Dolan, E. H. & Skatova, A. (2021) Ovarian cancer, misdiagnosis and shopping for healthcare products: loyalty card data sharing study. Webinar for Ovacome.

Fard, N. A. et al. (2021). On the interplay between educational attainment and nutrition: a spatially-aware perspective. EPJ Data Science, 10(1), 1-21.

Jenneson, V. L. et al. (2021). A systematic review of supermarket automated electronic sales data for population dietary surveillance. Nutrition Reviews.

Nica-Avram, G., Harvey, J., Smith, G., Smith, A., Goulding, J. (2021) Identifying food insecurity in food sharing networks via machine learning. Journal of Business Research. 131, 469-484.

Rains, T., Longley, P. (2021) The provenance of loyalty card data for urban and retail analytics.

Spooner, F. et al. (2021). A dynamic microsimulation model for epidemics. Social Science & Medicine, 291, 114461.

2020

Aiello, L. M. et al. (2020). Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London. Scientific data, 7(1), 1-11.

Grantz, K.H., Meredith, H.R., Cummings, D.A.T. et al. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun 11, 4961 (2020).

Harvey, J., Smith, A., Goulding, J., Branco-Illodo, I., (2020) Food Sharing, Redistribution, and Waste Reduction via Mobile Applications: A Social Network Analysis. Industrial Marketing Management, 88, pp. 437-448.

Lavelle-Hill, R., Goulding, J., Smith, G., Clarke, D.D. and Bibby, P.A., 2020. Psychological and demographic predictors of plastic bag consumption in transaction data. Journal of Environmental Psychology, 72, p.101473.

Ljevar, Vanja, James Goulding, and Gavin Smith. Exploration of links between anxiety purchases, deprivation and personality traits, 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020.

Nica-Avram et al. (2020) FIMS: Identifying, Predicting and Visualising Food Insecurity. WWW '20: Companion Proceedings of the Web Conference.

2019

Aiello, L. M. et al, (2019). Large-scale and high-resolution analysis of food purchases and health outcomes. EPJ Data Science, 8(1), 14.

Bandy, L. et al, (2019). The use of commercial food purchase data for public health nutrition research: A systematic review. PLoS One, 14(1).

Birkin, M. et al. (2019). Creating a long-term future for big data in obesity research. International Journal of Obesity. 43(12), pp. 2587-2592.

Lomax N. (2019). Independent repository of gambling industry data – a scoping study.

Skatova, A., Stewart, N., Flavahan, E. and Goulding, J., 2019. Those Whose Calorie Consumption Varies Most Eat Most.

YiChun, Liu., Lichung, Jen., Wanyu, Yeh. (2019) Looking inside your shopping bags: The use of retail data to capture health lifestyle

2018

Davies, A. et al. (2018). Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data. PloS One, 13(11), e0207523.

Lloyd, A., Chesire, J., Squires, M.  (2018). The Provenance of Customer Loyalty Card Data In Consumer Data Research. United Kingdom: UCL Press.

Morris M. A. et al.  (2018). Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map. International Journal of Obesity. 42 (12).

2017

Bidargaddi, N., Musiat, P., Makinen, VP. et al. Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Mol Psychiatry 22, 164–169 (2017).

Bryson, A. and MacKerron, G., 2017. Are you happy while you work?. The Economic Journal, 127(599), pp.106-125.

Dzogang, F., Goulding, J., Lightman, S. and Cristianini, N., 2017, October. Seasonal variation in collective mood via twitter content and medical purchases. In International Symposium on Intelligent Data Analysis (pp. 63-74). Springer, Cham.

2015

Blumenstock, J., Cadamuro, G. and On, R., 2015. Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), pp.1073-1076.

Jain, S. H. et al. (2015). The digital phenotype. Nature Biotechnology, 33(5), 462-463.

Saeb, S., Zhang, M., Karr, C.J., Schueller, S.M., Corden, M.E., Kording, K.P. and Mohr, D.C., 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. Journal of medical Internet research, 17(7), p.e175.

2010

Cummins, D. et al. (2010) Neighbourhood deprivation and the price and availability of fruit and vegetables in Scotland. Journal of Human Nutrition and Dietetics, 23(5), 494-501.

2009

Cummins, D. et al. (2009) Variations in fresh fruit and vegetable quality by store type, urban–rural setting and neighbourhood deprivation in Scotland. Public Health Nutrition. 12(11), 2044-2050.