Log in
With global demand for energy ever increasing, environmental impact has become a major priority for the oil industry. A collaboration between researchers at the University of Glasgow and Shell Global Solutions has developed GWSDAT (GroundWater Spatiotemporal Data Analysis Tool). This easy-to-use interactive software tool allows users to process and analyse groundwater pollution monitoring data efficiently, enabling Shell to respond quickly to detect and evaluate the effect of a leak or spill. Shell estimates that the savings gained by use of the monitoring tool exceed $10m over the last three years. GWSDAT is currently being used by around 200 consultants across many countries (including the UK, US, Australia and South Africa) with potentially significant impacts on the environment worldwide.
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
In a series of papers published from 1999 on, Aitken (Maxwell Institute) and collaborators applied Bayesian statistics to develop a methodology for the quantification of judicial evidence derived from forensic analyses. They proposed and implemented procedures for (i) determining the optimal size of samples that should be taken from potentially incriminating material (such as drugs seized); and (ii) the estimation of likelihood ratios characterising evidence provided by multivariate hierarchical data (such as the chemical composition of crime-scene samples). Their procedures have been recommended in international guideline documents (including a 2009 publication by the United Nations Office on Drugs and Crime) and have been routinely used by forensic science laboratories worldwide since 2008. The research has therefore had an impact on the administration of justice, leading to a better use of evidence and accompanying judicial and economic benefits. Examples are given from laboratories in Australia, Sweden and The Netherlands.
Research conducted in UCL's Department of Statistical Science has led to the development of a state-of-the-art software package for generating synthetic weather sequences, which has been widely adopted, both in the UK and abroad. The synthetic sequences are used by engineers and policymakers when assessing the effectiveness of potential mitigation and management strategies for weather-related hazards such as floods. In the UK, the software package is used for engineering design; for example, to inform the design of flood defences. In Australia it is being used to inform climate change adaptation strategies. Another significant impact is that UCL's analysis of rainfall trends in southwest Western Australia directly supported the decision of the state's Department of Water to approve the expansion of a seawater desalination plant at a cost of around AUS$450 million. The capacity of the plant was doubled to 100 billion litres per year in January 2013 and it now produces nearly one third of Perth's water supply.
The WinBUGS software (and now OpenBUGS software), developed initially at Cambridge from 1989-1996 and then further at Imperial from 1996-2007, has made practical MCMC Bayesian methods readily available to applied statisticians and data analysts. The software has been instrumental in facilitating routine Bayesian analysis of a vast range of complex statistical problems covering a wide spectrum of application areas, and over 20 years after its inception, it remains the leading software tool for applied Bayesian analysis among both academic and non-academic communities internationally. WinBUGS had over 30,000 registered users as of 2009 (the software is now open-source and users are no longer required to register) and a Google search on the term `WinBUGS' returns over 205,000 hits (over 42,000 of which are since 2008) with applications as diverse as astrostatistics, solar radiation modelling, fish stock assessments, credit risk assessment, production of disease maps and atlases, drug development and healthcare provider profiling.
Wavelets and multiscale methods were introduced and rapidly became popular in scientific academic communities, particularly mathematical sciences, from the mid-1980s. Wavelets are important because they permit more realistic modelling of many real-world phenomena compared to previous techniques, as well as being fast and efficient. Bristol's research into wavelets started in 1993, has flourished and continues today. Multiscale methods are increasingly employed outside academia. Examples are given here of post-2008 impact in central banking, marketing, finance, R&D in manufacturing industry and commercial software, all originating from research at Bristol. Much of the impact has been generated from the original research via software. This software includes freeware, distributed via international online repositories, and major commercial software, such as Matlab (a preeminent numerical computing environment and programming language with over one million users worldwide).
A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.
Bath research has substantially improved the estimation and formulation of GAMs and hence
This improved statistical infrastructure has resulted in improved data analysis by practitioners in fields such as natural resource management, energy load prediction, environmental impact assessment, climate policy, epidemiology, finance and economics. In REF impact terms, such changes in practice by practitioners leads ultimately to direct economic and societal benefits, health benefits and policy changes. Below, these impacts are illustrated via two specific examples: (1) use of the methods by the energy company EDF for electricity load forecasting and (2) their use in environmental management. The statistical methods are implemented in R via the software package mgcv, largely written at Bath. As a `recommended' R package mgcv has also contributed to the global growth of R, which currently has an estimated 1.2M business users worldwide [A].
The research created programs for the Stata statistical software environment that are used by thousands of researchers in economics and other fields around the world in academia, the private sector, government and quasi-governmental organisations, with approximately 400,000 downloads in the REF 2014 period. The core programs enable researchers to rigorously analyse the causal impact of a policy in settings where an experiment is infeasible and for experiments where take-up of treatment is incomplete, i.e. for the settings in which the vast majority of empirical work is done. The programmes are used to analyse complex data to establish causal links across a broad range of policy areas.
In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst — Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao's research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.
Our research has been applied directly by Aviva plc. to develop improved products in the general insurance market (e.g. household and car) and in the more specialised area of enhanced pension annuities. As a result, Aviva has become more competitive in these markets and customers are enjoying better value for money. In the case of enhanced annuities, the benefits are in the form of higher pension income for those accurately identified as facing shortened life expectancies. Aviva is the largest insurance company in the UK and the sixth largest in the world.