This course is delivered in-person and online over 5 days. The course combines short lectures and live demonstrations with guided R practicals, participant-led discussion of participants' own research questions and datasets. There will also be group model comparison, map critique and a final map communication activity. Participants will work with prepared health and environmental datasets; sharing their own data is not required.

Day 1

Spatial data and GIS in R, including spatial data structures, CRS and projections, joins, buffers, distances, raster and vector data, and exploratory mapping.

Day 2

Student research questions and datasets; areal data, neighbourhood structures, spatial autocorrelation, baseline areal models, spatial random effects and model interpretation.

Day 3

Geostatistical models and spatial dependence, followed by ecological niche modelling using occurrence and environmental data.

Day 4

Prediction from areal, geostatistical and ENM approaches; predictive evaluation and comparison across model types.

Day 5

Interpreting risk and suitability maps, uncertainty and limitations, map communication workshop and student group presentations.