The Consumer Data Research Centre invited proposals from UK-based academics for projects that capitalise on our core consumer data sets, extend our network of partners, and drive forward substantive and innovative social science research across a broad range of disciplines and research areas.
The call invited proposals that would contribute to our existing CDRC research themes, as well as extensions into other research areas of strategic significance, including those aligned with the ESRC’s priorities in:
- Mental Health
- Understanding the Macro-Economy
- Ways of Being in a Digital Age
Using small area income data to better understand society
PI: Kirby Swales, National Centre for Social Research
This project has both methodological and substantive purposes. We aim to add substantially to national debates about the distribution of household income and geographical polarisation by describing patterns of income at the very local level.
This will include examining between and within area measures of the income distribution. We will also show how small area income data can be used to add explanatory power to the housing thematic areas of interest to CDRC by including it in a model to predict residential property prices. In addition, we will also undertake validation work on the administrative data held by CDRC by comparing it with government survey data. The outputs will include a substantive policy report, methodological discussion paper, as well a new dataset. The resulting report, data and analytical models will enable new measures to be included in indices of local areas, such as the Index of Multiple Deprivation.
Data on housing returns for the UK, with an application to quantifying the value of local healthcare and schools quality
PI: Monique Ebell, National Institute of Economic and Social Research
Housing is by far the largest asset in UK household portfolios, and its importance has grown over the last two decades. It is necessary to have data on the returns to owning a home in order to be able to investigate a wide range of key policy issues for the UK.
Because housing is such an important asset, gaining a full picture of regional inequalities requires data on how the returns to home ownership vary across regions. Similarly, understanding trends in intergenerational inequality requires evidence on returns to home ownership. However, there is little evidence on the returns to owner-occupied housing in the UK. Our project uses the unique CDRC data on UK housing sales prices, rental prices and mortgage lending to construct measures of the returns, to owner occupied housing for local areas in the UK, and uses them to value local amenities such as school and healthcare quality.
A Data-Driven Approach to Unlocking the Probiotics Controversy, Understanding Public Confidence and Analysing Consumption in the UK
PI: Dr. Santosh Vijaykumar, Northumbria University
The demand for probiotics (or good bacteria) products surges on in the UK and globally despite lack of consumer understanding of how they work, equivocal evidence about their effectiveness, and regulatory caution about health claims made by probiotics manufacturers. The project deconstructs this important public health debate by asking a fundamental question: what drives consumer confidence in probiotics products when the evidence, and labelling policies, are fraught with controversy?
We explore this question using datasets from three sources: a) a national socio-behavioural online survey of probiotics confidence and consumption, b) a social media analytics platform, and c) YouGov and British Population surveys from CDRC’s data repository. Using geo-visualization methods, we will construct geographical profiles of probiotics understanding, confidence, sentiments, and consumption in the UK.
Our project will culminate with dissemination of findings to schools, parents, practitioners and policymakers through theatre workshops, research and policy briefs, and a virtual seminar series.
FixMyStreet: Micro-geographies of civic engagement and neighbourhood environmental quality
PI: Dr Alasdair Rae, University of Sheffield
This project makes use of a large street quality reporting dataset in combination with existing CDRC data assets, in order to explore micro-geographies of civic engagement and neighbourhood environmental quality. Specifically, the team will use data provided by FixMyStreet (c.1m records) to explore local neighbourhood conditions across the UK in relation to income, deprivation, household moves, transport, health and internet penetration.
A secondary aim of the project is to engage MySociety, the creators of FixMyStreet, more fully in the wider CDRC project. The research team have already secured access to the data and begun to explore it. With the support of the CDRC, the team will produce findings of particular interest to local authorities across the UK, in addition to academic researchers and neighbourhood residents.
Urban Mobility and Crime: Examining consumer data to better measure crime rates in urban centres
PI: Dr Andrew Newton, University of Leicester
Crime in urban centres is well understood not to be random and concentrated in particular locations (hot spots) at certain times. Much research has sought to explain these spatio-temporal crime patterns in terms of neighbourhood, physical, and socio-demographic characteristics. However, crime rates are generally identified based on census population and household counts, which are often not representative of urban mobility patterns during the day, and do not account for the dynamic mobility of people in urban spaces.
The growth of consumer data enables a much better understanding of how different crime types (burglary, criminal damage, theft, vehicle crime) may be related to urban mobility. This study will assess the utility of CDRC transport, retail and footfall data, and mobile data (twitter and crowd sourcing data) to better assess crime risk (rates) and for the police to better target resources in local crime hot spots in time and place, given the underlying urban mobility patterns present.
Data driven, social, economic and spatial profiles; obesity and physical activity
PI: Dr Claire Griffiths, Leeds Beckett University
Obesity and physical activity are both national priorities for the UK Government – yet to date, approaches to reduce obesity and increase physical activity, have been meet with limited success. Both are now viewed through a social lens (oppose to a medical one).
These aim of these projects are twofold: (1) investigate if the economic and social environment in which an individual lives is associated with attendance to and effectiveness of weight management interventions and physical activity levels; (2) identify if attendance at weight management interventions and self-tracking app use for physical activity are socially patterned.
At the individual level results will allow targeting of resources to individuals, or subgroups of individuals most in need and at the area level results could inform intervention design and also be of interest policy makers in terms of increasing the effectiveness of public services and policy decision.
Investigating Domestic Energy Efficiency Data
PI: Dr David Glew, Leeds Beckett University
Improving the energy efficiency of homes reduces energy bills and carbon emission. Energy Performance Certificates (EPCs) were introduced in the UK to measure the current efficiency of homes and encourage these improvements. Evidence from national EPC datasets show that the energy efficiency of homes is steadily improving over time. In these databases, many homes have multiple EPCs which indicate they have had some form of retrofit. However, it appears that the majority of houses with multiple EPCs do not, in fact, show much improvement in their score.
This project aims to determine if national datasets around household and community income, property-value or other geographic measures can explain why people improve their homes. The outcome of this research could shape targeted policy to improve the thermal performance of homes, specifically those in community whose socio economic characteristics suggest they may be in greatest need of support.
Online behaviour in the UK: an individual and contextual analysis
PI: Dr Emmanouil Tranos, University of Birmingham
This project will utilise the representative nature of the British Population Survey (BPS) to explain the determinants of individual internet usage and online shopping patterns in the UK. On top of individual and neighbourhood effects, this project will test, for the first time, if and how the availability of local internet content can influence the online behaviour of individuals.
Using the postcode information we will link the BPS with secondary data from ONS and with a novel source of big data from the Internet Archive and the British Library. The latter will enable the building of longitudinal measures about the richness of the local internet content at the level of the BPS responders in order to test, using multilevel modelling, whether the availability of online content of local interest can affect individual online behaviour.
Impact of investments in local public goods and planning decisions on house prices, rents and equilibrium sorting
PI: Dr Jonathan Halket, University of Essex
Detailed microdata on house prices, rents, infrastructure, and planning permission are crucial for understanding the impact of local public goods and planning decisions on prices, tax revenue, who can afford to live where, and on social welfare. We will combine microdata on local infrastructure and planning decisions with the CDRC’s Whenfresh/Zoopla data sets on house values and rents. We will then use these data to measure the social value of public goods and to predict the impacts of investments and planning decisions on urban mobility, house prices and rents, tax revenue, and social welfare.
Using Bayesian Surprise maps to explore debt across the UK
PI: Dr Reka Solymosi, University of Manchester
The provision of solid empirical data on companies is vital to inform policing of corporate crime. While analysis of ‘new’ sources of data allows for identification of crime hotspots, so does the application of ‘new’ techniques to traditional data. We consider the application of Surprise Maps to visualise County Court Judgements (CCJ).
Bayesian Surprise mapping addresses many biases associated with traditional thematic maps by weighting event data relative to a set of spatio-temporal models. By applying this methodology to the mapping of CCJ data, we identify unexpected events (those that induce large changes in belief over the model space).
We then consider differences in local circumstances, and draw conclusions about their effect on companies’ struggles, and possible implications for creditors, company law practitioners and law enforcement in corporate crime areas. Findings have implications for crime prevention, as identifying effective regulation can build evidence-base, and promote effective enforcement through identifying unknown risk predictors.