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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.
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 Centre for Urban Policy Studies (CUPS) at the University of Manchester (UoM) has contributed significantly to the improvement and targeting of resources to deprived urban areas. Through the development of a matrix approach, this work has both informed and transformed the UK Government's `deprivation index', the measure used to direct resources to areas most in need. More recently, a functional typology for use in the classification of deprived neighbourhoods has been developed. This was subsequently used by central government, local authorities and city-regions to better inform the nature and scope of regeneration initiatives.
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.
Statistical techniques developed at the University of Southampton have transformed the accuracy with which Census data can estimate the UK population's size and characteristics, delivering far-reaching socio-economic impact. The methodologies developed by Southampton have increased the accuracy and availability of the 2011 UK Census data, not only for the Office for National Statistics but for central government, local authorities, the NHS and the private sector, who all use the data as a basis for policy decisions. Preserving the privacy of the UK population, Southampton's work allowed, for the first time, the release of highly localised data, which is used by local authorities to target resources efficiently and meet the demands imposed by the Localism and Equality Acts.
The North East Economic Model (NEEM) was designed and developed at Durham University Business School (DUBS) from 2003. Customized to the regional economy, the aim of the research was for NEEM to model intra- and extra-regional economic relationships to provide quantitative estimates/projections of the impact of both long-term economic trends and shorter-term economic `shocks'. Its application has had significant impacts on policy practitioners in the region by: (1) facilitating more robust evidence-based policy analysis; (2) giving rise to knowledge transfer to policy-makers regarding the structure and workings of the regional economy; and (3) acting as a catalyst for an extended regional policy-modeling capacity. By influencing professional practice, it has had demonstrable impacts on regional economic policy, regional economic restructuring and local planning.
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.
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.
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.
Research into more accurate methods for measuring deprivation and `need' at the neighbourhood, `small area level', has led to older methods being abandoned. This has shaped government policy and practice, leading to the UK, local and central government changing where, geographically, to focus millions of pounds of spend. Our methods (Index of Multiple Deprivation (IMD) and Health Poverty Index (HPI)) are now used extensively in public, political and media discourses as the main reference point for any discussion of the distribution of need across the UK. The IMD has now also been adopted by the governments of South Africa, Nambia and Oman.