LEC glasshouses

Geospatial Data Science

We are undertaking research to develop innovative spatial techniques in order to increase our understanding of a wide range of environmental and socio-ecological systems. We are constructing novel geospatial modelling and spatial data science approaches for application to new data streams such as spatial trajectories and satellite Earth Observation data.

Our goal is to apply the techniques that we are developing to answer a range of fundamental science and “grand challenge” questions:

Climate Change and the Cryosphere

We need to understand the impact of climate change on Earth’s frozen regions to evaluate the contribution from melted ice and snow to future sea level. At Lancaster we use geophysical modelling, remote sensing and data science methods to study the quantity and distribution of melting on the Greenland and Antarctic ice sheets and the interactions between meltwater and ice flow. Our research is quantifying meltwater-induced perturbations to the Greenland ice sheet dynamics, understanding the role of supraglacial lakes in Antarctic ice shelf collapse and investigating the role of advanced statistics in improving predictions of future ice melting.

Environmental Changes in Global Ecosystems

We are facing an unprecedented age of environmental changes in ecosystems across the globe. To tackle this grand challenge, we are examining how ecosystems function and respond to contemporary environmental changes, including climate change, biodiversity loss and ecosystem degradation, and exploring the social impacts of those interactions on local food security, forest fires and tropical infectious diseases. This research spans the natural and social sciences, and we apply the tools of plant ecology, ecosystem science, geostatistics and remote sensing.

Human Exposure

Through understanding the spatial and temporal constraints on individual human behaviour in relation to social and environmental hazards such as air pollution and vector-borne disease, for example, through the tracking of individual movements and agent-based modelling, we are able to develop improved measures of individual exposure. This, in turn, is leading to increased understanding of the impacts of exposure on health and well-being.  We are using new and emerging sources of data to develop more sophisticated models that operate across network and cellular representations of space.