Other
Geographic Data Science Lab - University of Liverpool
The Geographic Data Science Lab is a research centre at the University of Liverpool, working at the intersection between Data Science and Geography.
What is it about?
Data scientists at the University of Liverpool are learning how to collate and use huge volumes of public and commercial data for positive social outcomes. Professor Alex Singleton and his colleagues in the Geographic Data Science Lab (GDSL) at the University of Liverpool use established networks to engage with consumer data organisations that range from credit rating agencies to online and offline retailers. The GDSL team then use sophisticated machine learning algorithms to analyse data and reveal patterns of interest to social scientists and wider stakeholders.
The GDSL team are particularly interested in social morphology – the factors that determine the shape of communities – and use geodemographic analysis techniques based on their research to relate people and places. This approach can identify where people have low aspirations of going into further education and provide pointers on how to raise them.
The Consumer Data Research Centre (CDRC) was established in 2014 to lead academic engagement between industry and the social sciences, and utilise consumer data to provide unique insights into a diverse range of societal and economic challenges. Based at the University of Liverpool with Professor Singleton acting as Deputy Director, the CDRC brings expertise from the universities of Oxford, Leeds and University College London to collaborate with industry organisations such as Local Data Company, Experian, and CACI. Funded by ESRC, the CDRC is a part of phase two of their Big Data Network, a collective effort to utilise the huge volumes of people-centric data being generated for wider societal benefits.
Why is it important?
The use of big data can reveal patterns of exclusion and opportunity, and provide important insights for planners and policy makers. Social context is hugely important to the work, especially when looking at social outcomes in cities. For instance, people like to be close to shopping centres (but not too close) and no-one likes the ‘ghost mall’ feeling of wandering around a dying commercial sector. Therefore, data can be explored when considering the placement of a new retail centre, mapping factors such as population density, affluence, and urban infrastructure to identify the likely catchment area.
With many towns and cities forecasting “the death of the high street”, calculating an accurate measure of ‘e-resilience’ to online shopping can be the difference between successful urban regeneration and yet another failed, expensive project. Professor Singleton and colleagues successfully created the first UK e-resilience index that could be used to predict trends in online shopping on high street communities.
