All Outputs (20)

Detecting iodine deficiency risks from dietary transitions using shopping data (2024)
Journal Article

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

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)
Conference Proceeding

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

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

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)
Conference Proceeding

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

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

© 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.

Psychological and Demographic Predictors of Plastic Bag Consumption in Transaction Data (2020)
Journal Article

Despite the success of plastic bag charges in the UK, there are still around a billion single-use plastic bags bought each year in England alone, and the government have made plans to increase the levy from 5 to 10 pence. Previous research has identi... Read More about Psychological and Demographic Predictors of Plastic Bag Consumption in Transaction Data.

The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products (2020)
Journal Article

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)
Conference Proceeding

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)
Conference Proceeding

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.

Data driven estimation of building interior plans (2017)
Journal Article

This work investigates constructing plans of building interiors using learned building measurements. In particular, we address the problem of accurately estimating dimensions of rooms when measurements of the interior space have not been captured. Ou... Read More about Data driven estimation of building interior plans.

Event series prediction via non-homogeneous Poisson process modelling (2016)
Conference Proceeding

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)
Conference Proceeding

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.

AMP: a new time-frequency feature extraction method for intermittent time-series data (2015)
Conference Proceeding

The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques... Read More about AMP: a new time-frequency feature extraction method for intermittent time-series data.

Modelling of building interiors with mobile phone sensor data (2015)
Journal Article

Creating as-built plans of building interiors is a challenging task. In this paper we present a semi-automatic modelling system for creating residential building interior plans and their integration with existing map data to produce building models.... Read More about Modelling of building interiors with mobile phone sensor data.

The potential of electromyography to aid personal navigation (2014)
Conference Proceeding

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)
Conference Proceeding

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)
Conference Proceeding

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.