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Research by the University of Southampton has led to an entirely new approach to the creation and management of small geographical areas for the publication of official statistics, including those from the 2001 and 2011 UK Censuses and the Neighbourhood Statistics Service. The software at the heart of this transformation is now used in 10 countries, while the academics responsible for it have helped inform government decisions, are integral to the policy and practice of the Office of National Statistics and have presented evidence to various influential committees. The research continues to deliver benefits to a large user community.
There is growing evidence that official population statistics based on the decennial UK Census are inaccurate at the local authority level, the fundamental administrative unit of the UK. The use of locally-available administrative data sets for counting populations can result in more timely and geographically more flexible data which are more cost-effective to produce than the survey-based Census. Professor Mayhew of City University London has spent the last 13 years conducting research on administrative data and their application to counting populations at local level. This work has focused particularly on linking population estimates to specific applications in health and social care, education and crime. Professor Mayhew developed a methodology that is now used as an alternative to the decennial UK Census by a large number of local councils and health care providers. They have thereby gained access to more accurate, detailed and relevant data which have helped local government officials and communities make better policy decisions and save money. The success of this work has helped to shape thinking on statistics in England, Scotland and Northern Ireland and has contributed to the debate over whether the decennial UK Census should be discontinued.
Small area estimation (SAE) describes the use of Bayesian modelling of survey and administrative data in order to provide estimates of survey responses at a much finer level than is possible from the survey alone. Over the recent past, academic publications have mostly targeted the development of the methodology for SAE using small-scale examples. Only predictions on the basis of realistically sized samples have the potential to impact on governance and our contribution is to fill a niche by delivering such SAEs on a national scale through the use of a scaling method. The impact case study concerns the use of these small area predictions to develop disease-level predictions for some 8,000 GPs in England and so to produce a funding formula for use in primary care that has informed the allocation of billions of pounds of NHS money. The value of the model has been recognised in NHS guidelines. The methodology has begun to have impact in other areas, including the BIS `Skills for Life' survey.
Research at the University of Manchester (UoM) has developed new approaches, methods and algorithms to improve the statistical confidentiality practices of data stewardship organisations (DSOs), such as the UK's Office for National Statistics. The research and its products have had significant impacts on data dissemination practice, both in the UK and internationally, and have been adopted by national statistical agencies, government departments and private companies. The primary beneficiaries of this work are DSOs, who are able to both disseminate useful data products, and protect respondent confidentiality more effectively. Secondary beneficiaries are respondents, whose confidentiality is better protected, and the research community, as without `gold standard' disclosure risk analysis, data holders can be overcautious.
The Scottish Longitudinal Study (SLS) is a pioneering study, combining census, civil registration, health and education data (administrative data). It has established an approach that allows the legal and ethical use of personal, sensitive information by maintaining anonymity within the data system. This approach has become a model for the national data linkage systems that are now being established across the UK. The SLS has also enabled policy analysts to monitor key characteristics of the Scottish population in particular health inequalities (alerting policy makers to Scotland's poor position within Europe), migration (aiding economic planning) and changing tenure patterns (informing house building decisions). Finally, the study has become fully embedded in Scotland's National Statistical agency, allowing it to produce new informative statistical series.
Successful planning in Scotland requires a set of geographical units for which data can be collected and analysed. Researchers at St Andrews have developed a new `small area' geography for Scotland. `Data zones' (DZs) provide a scientifically-based template for data mapping and has been adopted as the default geography used by public and private organisations to display and analyse data on topics as diverse as economic planning, health, education and transport, thus impacting how and where policy is enacted. To be statistically appropriate these units have to be compact, homogenous, with approximately the same size population and publically acceptable. This is not a trivial task, involving millions of potentially different solutions. In 2001, Scottish Neighbourhood Statistics (SNS) commissioned St Andrews to study how such units should be defined and to develop a methodology for creating them. Using the experience and skills developed over many years working in this area, the team developed a methodology and established the official small area geography of Scotland.
Southampton statisticians have made a valuable contribution to government policy formulation across the UK and further afield to areas of North America and Europe. Novel methods for delivering more accurate estimates of socio-economic indicators at neighbourhood level have given local authorities, national government agencies and MPs the tools to implement more effective policies designed to assist the poorest communities and strengthen community cohesion. The UK's Office for National Statistics (ONS) has described Southampton's contribution as `a breakthrough', while the Mexican government agency, CONEVAL, regards this work as `the most prestigious' of its kind.
Edward Higgs' research on the history of state and citizen data collection has become the standard work in the field. This research has included the I-CeM Project, which is creating a digital version of the British censuses 1851 to 1911 from data supplied by a commercial genealogical services provider, Find My Past. I-CeM's on-going collaboration with Find My Past has already improved the operations of the latter's data team. The anticipated future benefits of these digital data have already been used to support Find My Past publicity and tender applications. Higgs' expertise in the area has also informed the arguments and evidence he has provided for the UK Government and German state archivists on their census strategies. It also underpins his participation in the EU-sponsored Ethics Committee of the European Association for Biometrics, in which Higgs contributed historical knowledge to the development of ethical standards for biometrics companies.
The quality of estimates in the 2000s of immigration to local authority areas in England needed radical improvement. School of Geography (SoG) researchers Rees and Boden developed a method, based on administrative data including NHS Patient, National Insurance and Higher Education records, to improve the accuracy of immigration estimates. The research had a direct impact on the Office for National Statistics (ONS), which adopted and further developed the method for annual estimates of local immigration statistics. These are used as inputs to mid-year population estimates and projections, which play an important role in formulae for allocating funds to local authorities and health bodies.
Coombes' research to advance spatial-analysis methodology has re-defined Travel-to-Work Areas (TTWAs) — the only official UK boundaries defined by academics — and produced three distinct strands of impact.