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Outputs (10)

Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation (2025)
Journal Article
Long, G., Nica-Avram, G., Harvey, J., Lukinova, E., Mansilla, R., Welham, S., Engelmann, G., Dolan, E., Makokoro, K., Thomas, M., Powell, E., & Goulding, J. (2025). Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation. Food Policy, 131, Article 102826. https://doi.org/10.1016/j.foodpol.2025.102826

Deprivation pushes people to choose cheap, calorie-dense foods instead of nutritious but expensive alternatives. Diseases, such as obesity, cardiovascular disease, and diabetes, resulting from these poor dietary choices place a significant burden on... Read More about Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation.

Predicting Healthy Start Scheme Uptake using Deprivation and Food Insecurity Measures (2024)
Presentation / Conference Contribution
Makokoro, K., Long, G., Harvey, J., Smith, A., Welham, S., Mansilla, R., Lukinova, E., & Goulding, J. (2024, May). Predicting Healthy Start Scheme Uptake using Deprivation and Food Insecurity Measures. Presented at 2nd Digital Footprints Conference: Linking Digital Data for Social Impact, Bristol, UK

Detecting iodine deficiency risks from dietary transitions using shopping data (2024)
Journal Article
Mansilla, R., Long, G., Welham, S., Harvey, J., Lukinova, E., Nica-Avram, G., Smith, G., Salt, D., Smith, A., & Goulding, J. (2024). Detecting iodine deficiency risks from dietary transitions using shopping data. Scientific Reports, 14(1), Article 1017. https://doi.org/10.1038/s41598-023-50180-7

Plant-based product replacements are gaining popularity. However, the long-term health implications remain poorly understood, and available methods, though accurate, are expensive and burdensome, impeding the study of sufficiently large cohorts. To i... Read More about Detecting iodine deficiency risks from dietary transitions using shopping data.

Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake (2023)
Presentation / Conference Contribution
Harvey, J., Long, G., Welham, S., Mansilla, R., Rose, P., Thomas, M., Milligan, G., Dolan, E., Parkes, J., Makokoro, K., & Goulding, J. (2023, December). Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake. Presented at 2023 IEEE International Conference on Big Data (BigData), Sorrento, Itlay

Anthocyanins are a class of polyphenols that have received widespread recent attention due to their potential health benefits. However, estimating the dietary intake of anthocyanins at a population level is a challenging task, due to the difficulty o... Read More about Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake.

Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models (2023)
Journal Article
Dolan, E., Goulding, J., Marshall, H., Smith, G., Long, G., & Tata, L. J. (2023). Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models. Nature Communications, 14, Article 7258. https://doi.org/10.1038/s41467-023-42776-4

The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved healthcare planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of... Read More about Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models.

Forecasting local COVID-19/Respiratory Disease mortality via national longitudinal shopping data: the case for integrating digital footprint data into early warning systems (2023)
Journal Article
Goulding, J., Dolan, E., Long, G., Skatova, A., Harvey, J., Smith, G., & Tata, L. (2023). Forecasting local COVID-19/Respiratory Disease mortality via national longitudinal shopping data: the case for integrating digital footprint data into early warning systems. International Journal of Population Data Science, 8(3), Article 24. https://doi.org/10.23889/ijpds.v8i3.2290

Introduction & Background
The COVID-19 pandemic led to unparalleled pressure on healthcare services, highlighting the need for improved healthcare planning for respiratory disease outbreaks. With rapid virus diversification, and correspondingly rapi... Read More about Forecasting local COVID-19/Respiratory Disease mortality via national longitudinal shopping data: the case for integrating digital footprint data into early warning systems.

Assuring the quality of VGI on land use and land cover: experiences and learnings from the LandSense project (2022)
Journal Article
Foody, G., Long, G., Schultz, M., & Olteanu-Raimond, A.-M. (2022). Assuring the quality of VGI on land use and land cover: experiences and learnings from the LandSense project. Geo-Spatial Information Science, 27(1), 16-37. https://doi.org/10.1080/10095020.2022.2100285

The potential of citizens as a source of geographical information has been recognized for many years. Such activity has grown recently due to the proliferation of inexpensive location aware devices and an ability to share data over the internet. Rece... Read More about Assuring the quality of VGI on land use and land cover: experiences and learnings from the LandSense project.