2024 Projects
Finding Healthy Online: What the Eyes Reveal
In partnership with Ocado Retail Ltd.
This project uses data science and eye-tracking data to describe real-life consumer’s decision-making and website use during online supermarket food shopping.
The aim was to understand the shoppers’ website navigation patterns, their website interactions, and engagement with nutrition and sustainability information. The project explores the influence of website design and digital food environment on consumer behaviour in partnership with Ocado Retail.
By Gauri Venkatachalapathi
Read morePsychiatric History and Routinely Assessed Outcomes of Hospitalisation (PHARAOH)
In partnership with NHS West Yorkshire Integrated Care Board (ICB) -Leeds
About 30% of general hospital inpatients have both mental and physical health conditions (Mental-Physical Multimorbidity-MPM). Compared to patients with only a physical illness, MPM patients experience worse outcomes, like longer hospital stays, more complications, and increased emergency readmissions.
This research will aid in the design and implementation of targeted, cost-effective interventions that improve patient care and reduce NHS costs.
By Precious-Gift Alele
Read moreScalable Visualization and Explainability of Synthetic Datasets
In partnership with 4-Xtra Technologies Ltd, a University of Leeds start-up.
Driven by the current lack of available visualization techniques that are efficient at scale for various types of data, this project explores and implements new methods for visual comparison and evaluation of real versus synthetic data.
The project seeks to improve the visual assessment of data quality and model performance, including the discovery of hidden patterns, detection of anomalies or outliers, and more generally, evaluation and validation of faithfulness of AI-generated synthetic data in relation to real-world counterparts.
By Netochukwu Onyiaji
Read moreCan we estimate rock strength from rock texture alone?
In partnership with the University of Leeds School of Earth & Environment, British Geological Survey, Geosolutions Leeds.
As the Energy Transition accelerates, the demand for useable geo-mechanical data – especially the strengths of different potential reservoirs, aquifers, or storage sites – will massively increase. Traditional methods for obtaining this data are time-consuming and expensive.
We are developing a data mining tool to extract rock mechanics data (grain size, porosity, and strength) from published papers and open access theses. Using machine learning and statistical analysis, we test the hypothesis that rock strength can be predicted from grain size and porosity alone.
Sadiq Balogun
Read moreUnderstanding Economic Resilience through Consumer Spending Trends
When the COVID-19 pandemic swept across the World, households faced unprecedented financial insecurity through lost jobs, reduced incomes, and dramatic shifts in expenses. Families had to make tough economic decisions—discretionary spending took a backseat, and essentials became a priority.
But was the impact the same everywhere and in all sectors of the economy? This study leverages GeoInsights spending data to explore how different communities adapted to economic disruptions.
By Gauri Venkatachalapathi
Read moreAssessing Patient Safety Inequities in Ambulance Non-Conveyance
A 999 call doesn’t always result in hospital transport, as millions of cases are managed without conveyance. This research investigates how socioeconomic and demographic factors influence ambulance non-conveyance decisions, highlighting potential inequities that impact patient safety and healthcare outcomes.
By Angeliki Fragkeskou
Read more
Identifying Protein Patterns to Improve Diagnosis and Treatment Options for Giant Cell Arteritis
Approximately 3-7% of all giant cell arteritis (GCA) patients may experience GCA-related stoke, and up to 30% may suffer permanent vision loss. Thus, timely diagnosis and therapeutic intervention are critical, but remains challenging.
In this project, we identify protein signatures that provide new insights into the underlying biology of GCA and could lead to a biomarker for faster and more accurate diagnosis of GCA.
By Precious-Gift Alele
Read moreCausal Inference Frameworks for Individual Based Models
Most human beings have an intuitive understanding of the concept of causation; however, it is a complex phenomenon which remains largely unexplained.
This project is building towards a framework that integrates DAG-based causal inference with agent-based modelling. We aim to provide a novel approach for simulating complex systems by combining causal structure with individual-level interactions.
By Lynette Linzbuoy
Read moreWhere do household e-cargo bikes go?
E-cargo bikes, for household use, are novel transport mode, but we know very little about their movement patterns. There is research about e-cargo bikes being used for logistics, but much less about domestic use.
E-cargo bikes have strong potential to contribute to transport decarbonisation particularly in suburban areas where usage is growing and car dependence is high. However, there is a lack of research on both domestic e-cargo bike use and suburban use.
Jayita Chakraborty
Read moreDeveloping a Standardised Approach for Local Analysis of Crime and Community Safety Indicators
This project, part of the LIDA Data Scientist Development program and funded by the ESRC Vulnerability and Policing Futures Research Centre, is being conducted in collaboration with Leeds City Council’s Safer, Stronger Communities Team. It aims to develop consistent methods
for analysing crime and community safety issues across neighbourhoods in Leeds, addressing the current challenge of inconsistent approaches across areas.
Using publicly available data, including anonymized crime data from data.police.uk and geodemographic indicators from the Office for National Statistics (ONS), the project will explore and prototype various analytical methods including visualisations. In 2025, additional crime data from Leeds City Council relating to particular issues and their associated harms will be incorporated to refine these approaches further.
The main goal is to create tools that help identify neighbourhood-specific challenges, detect outlier areas with unusual crime patterns, and emerging safety issues. By focusing on Lower Super Output Areas (LSOAs), the project will analyse crime trends and identify clusters of neighbourhoods with shared characteristics, while also exploring ways to flag areas where crime or safety issues deviate from the norm or where some crime problems might represent precursors of others.
To make these insights accessible, the project will prototype visualisations for potential use in interactive dashboards. These tools will allow local authorities to explore data trends, making it easier to understand safety challenges and support more effective policy planning and intervention. This exploratory phase will guide future developments, ensuring that the tools are practical and aligned with real-world needs.
Netochukwu Onyiaji
Educational Trajectories to NEET Status
Previous research has established a strong association between a measure of ‘school readiness’ obtained from the Early Years Foundation Stage Profile (EYFSP) scores, later academic engagement/attainment, school absences and the risk of an individual becoming classified as Not in Employment, Education or Training (NEET) in early adulthood. However, these findings are currently based on analysis that has compared the relationship between pairs of factors (such as school absence and NEET status) and using the whole Bradford region.
This project seeks to build upon these findings, further exploring the relationships between the school data and NEET status in early adulthood using statistical modelling techniques. Additionally, the project aims to explore spatial differences in the longitudinal trajectories of children through the education system and help to identify what factors might explain them. The data utilised is a combination of administrative data obtained from the Department for Education and NEET data from the Connected Bradford platform.
The partners involved in the project are Connected Bradford and the ESRC Vulnerability & Policing Futures Research Centre.
Objectives:
-
- To understand the trajectories of young people through education systems.
- To understand longitudinal relationships between early years assessments, school engagement, school absence and the later Not in Employment, Education or Training (NEET) status of individuals.
Lewis Shaw
Understanding the Wider Determinants Associated with Looked After Children in Bradford
Looked after children are children that have been removed from parental care and placed in out-of-home care. This group of children are being looked after by the local authority. The increasing number of looked after children underscores a critical need to understand the underlying factors driving this trend. Children who become "looked after" by local authorities often face long-term challenges affecting their emotional well-being, educational, and social development. While national policies provide frameworks for safeguarding these children, there is need to understand the determinants—such as socio-demographic factors, geographic disparities, and childhood vulnerabilities—that influence their entry into care. Gaining insights into these factors is essential for developing preventative strategies to support at-risk children and reduce the demand on social care services.
By enhancing our understanding of the wider determinants associated with children entering social care, this research will offer valuable insights to inform policy and practice in Bradford. Identifying key risk factors and emerging trends will enable the development of targeted preventative interventions, aiming to reduce the number of children requiring social care services and to improve long-term outcomes for vulnerable children.
This project seeks to investigate the patterns and predictors associated with children entering social care in Bradford. By leveraging the Connected Bradford database—an administrative dataset linking health, education, social care, and environmental data—we aim to address the following research questions:
- What do changes in children's care provision across Bradford look like?
- What factors within children's care data are predictive of engaging with children's care?
- What factors within linked education and health data are predictive of engaging with children's care?
This research will employ spatial analysis, time series analysis, and statistical modelling to examine administrative data.
This project is conducted in conjunction with, and with the supervision and support of, the ESRC Vulnerability & Policing Futures Research Centre, Bradford Metropolitan District Council, and Bradford Children’s Trust.
Sadiq Balogun
Identifying Genetic Predictors of Vascular Complications in Giant Cell Arteritis
In partnership with National Institute for Health and Care Research (NIHR) Leeds Biomedical Research Centre (BRC).
Giant Cell Arteritis (GCA) is the most common type of vasculitis affecting older people. It occurs when the arteries (particularly those at the side of the head) become inflamed leading to a narrowing of the vessels and reduced blood supply. The main symptoms include headaches, fever, scalp tenderness, and jaw pain when eating or talking. In more severe cases, and if not treated promptly, it can lead to serious complications like vision loss or stroke (ischaemic complications). Early diagnosis is essential; however, GCA can be difficult to diagnose with the initial symptoms often resembling those of other conditions.
The first line of treatment for GCA patients is with glucocorticoids (steroid medication). High-dose therapy is started on diagnosis, with even higher doses recommended for those with ischaemic complications. On subsequent tapering of the medication, relapse is seen in about half of all patients with GCA. This treatment is essential but there is also a considerable risk of complications arising due to long-term glucocorticoid treatment, with 86% of patients experiencing glucocorticoid toxicity in the form of cardiovascular or metabolic disease.
By identifying those at the highest risk of serious side effects, we could offer them additional treatments, such as tocilizumab. These treatments are currently considered too expensive for general use but may be cost-effective for high-risk patients before they have a major relapse. Some studies have shown that age and cardiovascular risk factors are linked to an increased risk of ischaemic complications in GCA. Building on this work, the aim of this study is to identify genetic predictors of GCA and recognise individuals at the highest risk. The study will use data from the UKGCA consortium, including patient records and genome-wide genotypic data.
By Lynette Linzbuoy