The overall aim of this course is to equip health researchers and public health practitioners with existing R experience to build, evaluate, interpret and communicate spatial models and risk maps for health applications.

Course objectives

At the end of the course, students should be able to:

• Prepare, manipulate and visualise spatial health data in R, including vector and raster data.
• Distinguish areal, point-occurrence and continuous-space data structures and relate them to research questions.
• Build and interpret spatial areal models, geostatistical models and ecological niche models.
• Generate predictions from these model types and select evaluation strategies suited to the prediction task.
• Interpret risk and suitability maps, including uncertainty and limitations, and communicate findings for a defined audience.

Who is this course for?

The course is for health researchers, epidemiologists, public health practitioners and postgraduate researchers who already use R and want to develop applied skills in spatial modelling and risk mapping. Participants may work in universities, public health agencies, ministries of health or humanitarian and international organisations. 

Previous GIS experience is helpful but not required however prior practical experience using R is required. Participants should be comfortable importing data, manipulating data frames, writing and running scripts, and fitting and interpreting generalised linear models. Introductory R and GLM materials will be provided as optional pre-reading and will not be taught during the course. Prior spatial analysis experience is not required.

Assessment

There is no formal summative assessment. Formative learning will be supported through practical exercises, discussion of model choices, interpretation of predictions and maps, peer feedback and group presentations.