Research Themes

Our CDT technical training and research is based around five carefully selected themes. These themes have been selected on the basis of:

(i)    Predictions of future technological and methodological developments that will shape both the $500bn global geospatial industry and also the wider $2.2tn economic impact of the industry in applied fields such as transport, location-based services and infrastructure;

(ii)   Consultations with 40 companies, regarding the key skills geospatial engineers, scientists and practitioners require; 

(iii)  A review of the emerging international trends in Geospatial Systems training, particularly in the USA, Canada, India, China, and more broadly Asia, which are current market leaders in geospatial technology and skills adoption and development.

Spatial Data Capture & Interpretation

This theme focuses on modern spatial data capture and monitoring approaches, including EO satellite imagery, UAV (drone) data, and spatial sensor networks. Both CDT partner institutions are internationally recognised for research in geodetic positioning, terrestrial EO, airborne and ground-based photogrammetric computer vision and laser scanning. Newcastle University has the UKs largest open Urban Observatory, with over 300+ IoT environmental sensors, CCTV data feeds and social media inputs. Example PhD projects around this theme include: (i) Smart drones for autonomous search and rescue, (ii) 3D intelligent road infrastructure mapping for an autonomous world.

Spatial Statistics & Mathematical Methods

Building on CDT research expertise, this theme will develop Bayesian statistics to analyse and model extreme spatial events, develop robust statistical data assimilation methods for spatial simulation models, develop new tools for the statistical analysis of spatial network robustness and resilience, develop mathematically robust approaches to the analysis of second-order spatiotemporal effects and impacts on grids and network models. PhD projects include: (i) Statistical data assimilation of spatiotemporal social media for improved impact model parameterisation, (ii) Gaussian random temporal fields on manifolds for global environmental models.

Big Data Spatial Analytics

Across the CDT there exists internationally leading research capability in the use of Big Data approaches for geospatial applications, including the utilisation of cloud computing, Big Data management frameworks (e.g., Hadoop, Graph and array databases), edge and IoT computing. Example PhD projects include: (i) Real-time IoT data injestion and analytics for extreme event warning systems, (ii) A federated big data framework for integrated real-time GeoBIM representations of urban neighbourhoods.

Spatial Analysis & Modelling

This theme focuses on the development and application of spatial analysis approaches and modelling, ranging from large scale geodemographic analysis and modelling of population dynamics, coupled multi-scale urban-infrastructure models, advanced agent-based models and the development and application of multi-objective spatial optimisation, evolutionary computing and AI/machine learning. PhD projects include: (i) 3D deep learning for traffic monitoring and analysis, (ii) Spatially enabled resilience and risk in cities through real-time simulation and forecasting using AI.

Visualisation & Decision Support

Spatial visualisation and spatial decision support tools play a critical role in allowing stakeholders to engage with the often complex outputs of geospatial data analytics and models. Significant expertise exists within the CDT on the use of HPC and Big Data frameworks to visualise 3D BIM/city-models, realtime spatial visualisation of infrastructure network simulations, database interfaced spatial decision support tools and the visualisation of environmental data feeds. The N-Lab at the University of Nottingham specialise in the development of analytical decision support tools for industrial and governmental partners, while Newcastle have a state of the art visualisation decision theatre. Building on this leading capability across the CDT, typical PhDs in this theme include: (i) VR and AR immersive spatial decision support and management of interdependent and coupled infrastructure networks, (ii) Visualizing spatial uncertainty using perceptually uniform levels of visual entropy.