The focus of the environment theme is to seek methodological innovations that can transform our understanding and management of the natural environment. This is a major cross-disciplinary challenge requiring a close collaboration between environmental scientists, computer sciences, statisticians, social scientists, and many others.
We are particularly interested in new methods that:
- recognise the intrinsic features of complexity/ interconnectedness and uncertainty in the natural world
- can deal with the often sparse data emanating from extreme events
- recognise the higher heterogeneous and fragmented nature of environmental data (sometimes referred to as the long tail of science)
- can detect significant phenomena and changepoints associated with the environment at different scales
We are also interested in the transformative potential of underlying digital innovations, in particular, Internet of Things (IoT) and cloud computing: IoT has the potential to provide rich, real-time data about many facets of the natural environment at a scale previously unimaginable; cloud computing offers elastic storage and computational capacity to bring together diverse data-sets from different geographical locations and at different scales and open this up to a range of scientists and stakeholders.
Data science then has the potential to offer a rich mix of analysis techniques to make sense of the data and hence to inform environmental management strategies and policies. This builds on real areas of strength at Lancaster, across the environmental sciences (for example in modelling and in managing uncertainty), an underlying distributed computer systems architectures, in areas of theory including extreme value theory, changepoint analysis, machine learning and decision making. Building on this, our goal is to establish the Data Science Institute as the world leader in data science for the environment.
