Abdul Hadi Asfarangga, The University of Adelaide
Dr Charlotte Jones-Todd, The University of Auckland NZ
This course will focus on the theory and application of modelling spatially referenced data. Students will be introduced to methods for the analysis of geostatistical, lattice, and point pattern data. From summary statistics to model inference & validation this course will introduce students to a wide range of techniques, which will enable them to appropriately analyse spatial data. Much of the course will have a practical element involving using the statistical programming language R; there will be a particular focus on statistical modelling and inference of spatial data from a wide range of research fields (e.g., ecology, physics, international relations, criminology).
There will be a series of two assignments alongside low-stakes tasks and a final take home exam to reinforce the taught material.
No prior knowledge of spatial statistics is required; however, students are expected to be confident in R programming.
Students should be familiar with the Bayesian paradigm, comfortable with the concept of maximum likelihood and OLS, and be aware of the theory of stochastic processes. This course will use the programming language R and fit models using TMB and INLA. Students should bring their own laptop to both lectures and tutorials where possible.
Take this quiz and look at some of the expected foundational skills in this topic