Photo of Grove Building entrance
Photo of Richard Fry

Dr Rich Fry

Associate Professor, Health Data Science

Telephone number

+44 (0) 1792 606523

Email address

Research Links

Cellular Office - 302
Third Floor
Data Science Building
Singleton Campus
Available For Postgraduate Supervision


Dr Rich Fry is a Senior Lecturer in GIS and Health Geographies and the Postgraduate Admissions Tutor for the Medical School. Rich graduated with a BSc (Hons) Physical Geography from Swansea before completing an MSc and PhD in GIS and computing at the University of South Wales following some time out in industry. Following a post-doctoral position at the Wales Institute for Socio-Economic Research, Data and Methods (WISERD) he returned to Swansea, joining the Medical School as a Senior Research Officer specialising in GIS and privacy protecting spatial data linkage. Rich currently leads geography and health research for substantive sites including HDR UK Wales and Northern Ireland, ADR Wales and the National Centre for Population Health and Wellbeing. Rich teaches on several courses including GIS (GEGM22), PM-344 Capstone and PMIM 102 Scientific Computing and Health Care. He is module lead for PMIM502 for Health Data Visualisation.

Areas Of Expertise

  • Geography
  • Geographic Information Systems
  • Spatial Modelling
  • Privacy Protecting Data Linkage
  • Accessibility Modelling
  • SAIL Databank
  • Health Data Visualisation
  • Spatial Databases and Computing

Career Highlights

Teaching Interests

Rich teaches on undergraduate and postgraduate courses with a focus on spatial data science, health data science, spatial statistics and health data visualisation. Focus of teaching and supervision is how we can use geography to help improve people lives. Rich is a Fellow of the Higher Education Academy.


The use of geography and spatial data science is crucial in understanding how the environment in which we live influences and impacts on human health. Rich’s research focuses on how to build computational models of the built environment, using geographic information systems, which can be anonymously linked to routinely collected health data to help us understand the interactions between the natural and built environment and population health. The models are designed to capture change over time using administrative, satellite and routinely captured spatially referenced data to gain new insights into population health. Rich is also interested in how the use of novel and cutting-edge techniques in Machine Learning, GPU and distributed computing can be leveraged when building these models.


Rich collaborates with a wide range of academic and non-academic partners both nationally and internationally.

Rich is a member of the International Collaboration Effort (ICE) on Injury Statistics with international colleagues including the Centers for Disease Control and World Health Organisation.