Using data science to generate better policies to improve people’s lives in Holme Wood and beyond
The Bradford area of Holme Wood has been a focus for action and investment for many years, but ‘top down’ interventions to improve life for residents often failed to deliver impact. Some of the world’s leading data scientists at LIDA have formed multi-disciplinary groups working with the community and Local Authority to see how data science can aid collective understanding of a place like Holme Wood. The data scientists are exploring how they can work with the community and policymakers to use data to generate better interventions and policies to improve people’s lives.
Mark Mon-Williams, Professor in Cognitive Psychology and Director of the Centre for Applied Education Research, which is coordinating the study says: “A generic policy just can’t work. Ilkley is an area of Bradford, and one of most affluent areas outside of London. Holme Wood is also an area of Bradford, but it’s one of the most deprived areas of the UK outside of London, so many policies that work in Ilkley won’t work in Holme Wood and vice versa. And it’s the same across the UK, one policy is unlikely to work equally effectively across different areas.” He adds that top down interventions typically do not work, and that’s why the team are working with front-line practitioners, community organisations, public service providers, local elected members, and local and national policy makers.
Using relevant anonymised local and national data sets – including The Born in Bradford project, one of the world’s largest longitudinal birth cohort studies tracking the lives of 13,818 children – the team are building an accurate model of Holme Wood that represents the reality for the community. The wider team consists of seven LIDA interns (partly funded by the Alan Turing Institute) who are helping LIDA bring world class data science to regional projects such as Born in Bradford.
Patterns and relationships relevant to recognised issues are being identified and publicly held data sets are tested against community generated data to identify any disparities. A model is then built which can be visualised and explored by scientists, practitioners and residents. The model is then tested to measure its effectiveness and this feedback loop means the model is continuously improving.
“This work is a great example of the general approach within LIDA – build models that give you the opportunity to say what happens if you change this or that. The work creates a formal, quantitative model and when policy is implemented based on that model, it’s possible to measure the outcomes, then change and improve the model” says Professor Mon-Williams. He adds that this work is different to the projects that have failed in the past because it allows citizens and practitioners to calibrate the model, and asks them about what they want to change in their area – the community get to co-produce the work.
The project is also allowing a much better understanding of how different factors influence outcomes in people’s lives. Previously, policymaking work was done in silos, but data science allows everything to be connected and enables the development of a much more holistic model.
“One example is looking at the connection between exclusion from school and crime.” Professor Mon-Williams adds. “We work with the community to find innovative way to tackle this – in a way that police or schools understandably find difficult from their single viewpoints, because we can use data to involve stakeholders from across the system to create a holistic view and tailored policy.”
The work is looking at five research projects: Exclusions, children not in school, and crime; Healthy choices: healthy lives; The impact of poor mental health on individuals, the community and services; Pride, aspirations, role models and careers; and Adverse Childhood Experiences.
This work is a formal quantitative data science project that allows local and national policy makers, stakeholders, front line services and residents to have a shared understanding of their area, and to measure effects of policies and projects in that place. For Holme Wood this will allow for early intervention, helping decision makers and planners to make sure they are targeting services where and when they are needed, whilst also empowering communities to take ownership. But this work has far reaching outputs, as it can be used as an archetype for the wider Bradford area and other areas throughout the UK and beyond.
This work uses the power of data science to take a more holistic approach to service delivery and use data science to help tailor general policies to meet the needs of residents in a specific area. Ultimately the goal of the work is to improve outcomes for all.
To find out more about the Born In Bradford project visit: https://borninbradford.nhs.uk
For more information on the Act Early Holme Wood work visit: https://caer.org.uk/data-modelling/ or https://caer.org.uk/wp-content/uploads/2020/10/ActEarly-Holme-Wood-booklet.pdf
For more information on LIDA’s data science interns and the Holme Wood project visit: https://lida.leeds.ac.uk/news/improving-lives-through-big-data-holly-clarke/