All Outputs (16)

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., …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.

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.

Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data (2022)
Presentation / Conference Contribution
Mansilla, R., Smith, G., Smith, A., & Goulding, J. (2022). Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data. In Proceedings 2022 IEEE International Conference on Big Data (1044-1053). https://doi.org/10.1109/BigData55660.2022.10021062

Understanding and measuring the predictability of consumer purchasing (basket) behaviour is of significant value. While predictability measures such as entropy have been well studied and leveraged in other sectors, their development and application t... Read More about Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data.

Using mobile money data and call detail records to explore the risks of urban migration in Tanzania (2022)
Journal Article
Lavelle-Hill, R., Harvey, J., Smith, G., Mazumder, A., Ellis, M., Mwantimwa, K., & Goulding, J. (2022). Using mobile money data and call detail records to explore the risks of urban migration in Tanzania. EPJ Data Science, 11(8), Article 28. https://doi.org/10.1140/epjds/s13688-022-00340-y

Understanding what factors predict whether an urban migrant will end up in a deprived neighbourhood or not could help prevent the exploitation of vulnerable individuals. This study leveraged pseudonymized mobile money interactions combined with cell... Read More about Using mobile money data and call detail records to explore the risks of urban migration in Tanzania.

Machine learning methods for “wicked” problems: exploring the complex drivers of modern slavery (2021)
Journal Article
Lavelle-Hill, R., Smith, G., Mazumder, A., Landman, T., & Goulding, J. (2021). Machine learning methods for “wicked” problems: exploring the complex drivers of modern slavery. Humanities and Social Sciences Communications, 8, Article 274. https://doi.org/10.1057/s41599-021-00938-z

Forty million people are estimated to be in some form of modern slavery across the globe. Understanding the factors that make any particular individual or geographical region vulnerable to such abuse is essential for the development of effective inte... Read More about Machine learning methods for “wicked” problems: exploring the complex drivers of modern slavery.

Model Class Reliance for Random Forests (2020)
Presentation / Conference Contribution
Smith, G., Mansilla Lobos, R., & Goulding, J. (2020). Model Class Reliance for Random Forests. In Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)

Variable Importance (VI) has traditionally been cast as the process of estimating each variable's contribution to a predictive model's overall performance. Analysis of a single model instance, however, guarantees no insight into a variables relevance... Read More about Model Class Reliance for Random Forests.

Exogenous cognition and cognitive state theory: The plexus of consumer analytics and decision-making (2020)
Journal Article
Smith, A., Harvey, J., Goulding, J., Smith, G., & Sparks, L. (2020). Exogenous cognition and cognitive state theory: The plexus of consumer analytics and decision-making. Marketing Theory, 21(1), 53-74. https://doi.org/10.1177/1470593120964947

We develop the concept of exogenous cognition (ExC) as a specific manifestation of an external cognitive system (ECS). Exogenous cognition describes the technological and algorithmic extension of (and annexation of) cognition in a consumption context... Read More about Exogenous cognition and cognitive state theory: The plexus of consumer analytics and decision-making.

Identifying food insecurity in food sharing networks via machine learning (2020)
Journal Article
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. https://doi.org/10.1016/j.jbusres.2020.09.028

© 2020 Elsevier Inc. Food insecurity in the UK has captured public attention. However, estimates of its prevalence are deeply contentious. The lack of precision on the volume of emergency food assistance currently provided to those in need is made ev... Read More about Identifying food insecurity in food sharing networks via machine learning.

The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products (2020)
Journal Article
Harvey, J., Poorrezaei, M., Woodall, T., Nica-Avram, G., Smith, G., Ajiboye, T., …Zhu, K. (2020). The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products. Journal of Service Research, 23(4), 504-526. https://doi.org/10.1177/1094670520929095

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 addr... Read More about The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products.

FIMS: Identifying, Predicting and Visualising Food Insecurity (2020)
Presentation / Conference Contribution
Lucas, B., Smith, A., Smith, G., Perrat, B., Nica-Avram, G., Harvey, J., & Goulding, J. (2020). FIMS: Identifying, Predicting and Visualising Food Insecurity. In WWW '20: Companion Proceedings of the Web Conference 2020 (190-193). https://doi.org/10.1145/3366424.3383538

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... Read More about FIMS: Identifying, Predicting and Visualising Food Insecurity.

The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions (2018)
Presentation / Conference Contribution
Engelmann, G., Smith, G., & Goulding, J. (2018). The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions.

Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio... Read More about The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions.

Event series prediction via non-homogeneous Poisson process modelling (2016)
Presentation / Conference Contribution
Goulding, J., Preston, S. P., & Smith, G. (2016). Event series prediction via non-homogeneous Poisson process modelling. In 2016 IEEE 16th International Conference on Data Mining (ICDM). https://doi.org/10.1109/ICDM.2016.0027

Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature... Read More about Event series prediction via non-homogeneous Poisson process modelling.

A novel symbolization technique for time-series outlier detection (2015)
Presentation / Conference Contribution
Smith, G., & Goulding, J. (2015). A novel symbolization technique for time-series outlier detection. In 2015 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/BigData.2015.7364037

The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where sp... Read More about A novel symbolization technique for time-series outlier detection.

The potential of electromyography to aid personal navigation (2014)
Presentation / Conference Contribution
Pinchin, J., Smith, G., Hill, C., Moore, T., & Loram, I. (2014). The potential of electromyography to aid personal navigation.

This paper reports on research to explore the potential for using electromyography (EMG) measurements in pedestrian navigation. The aim is to investigate whether the relationship between human motion and the activity of skeletal muscles in the leg mi... Read More about The potential of electromyography to aid personal navigation.

A refined limit on the predictability of human mobility (2014)
Presentation / Conference Contribution
Smith, G., Wieser, R., Goulding, J., & Barrack, D. (2014). A refined limit on the predictability of human mobility. In 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom). https://doi.org/10.1109/PerCom.2014.6813948

It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiot... Read More about A refined limit on the predictability of human mobility.

Towards optimal symbolization for time series comparisons (2013)
Presentation / Conference Contribution
Smith, G., Goulding, J., & Barrack, D. (2013). Towards optimal symbolization for time series comparisons. In 2013 IEEE 13th International Conference on Data Mining Workshops. https://doi.org/10.1109/ICDMW.2013.59

The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the... Read More about Towards optimal symbolization for time series comparisons.