11-15 March 2024

The course will introduce spatial statistical methods with emphasis on spatial sampling, point pattern analysis, geostatistical analysis, mixed linear and non-linear regression models, and machine learning methods applied to spatial variables. Underlying problems such as handling very large datasets and using large scale satellite imagery will be discussed. Theory will be explained and illustrated by practical applications that can be reproduced by the participants. Exercises will be provided to participants for developing an independent command of the materials.

This course is aimed at higher degree research students and early career researchers working with or with an interest in spatial data and applying spatial statistical methods, with emphasis on the biology and ecology domains. Some familiarity with R, R package sf, and the tidyverse is assumed. Familiarity with basic statistics, linear regression, standard errors, confidence intervals and prediction will be very useful.

 - Understanding of the different spatial statistical data types (point patterns, geostatistical data, lattice data)

- Understanding of spatial dependence, and its role in analysing spatial data

- Hands-on experience with spatial statistical methods and software in R, and a number of R spatial packages

- Understanding of the challenges of using big spatial datasets, and ways to handle them

Daily on-line meetings,  15:00-18:00 CET; offline communication through Slack

Day 1: Introduction to spatial data
Introduction to spatial data, support, coordinate reference systems
Introduction to spatial statistical data types: point patterns, geostatistical data, lattice data
Is spatial dependence a fact? And is it a curse, or a blessing?
Spatial sampling, design-based and model-based inference
Intro to point patterns and point processes, observation window, first and second order properties

Day 2: Point Pattern data
Point patterns, density functions
Interactions of point processes
Simulating point process
Modelling density as a function of external variables

Day 3: Geostatistical data
Stationarity of mean, stationarity of covariance
Estimating spatial covariance and semivariance
Modelling the variogram
Kriging interpolation
Conditional simulation

Day 4: Machine Learning methods applied to spatial data
Data: coverages as predictors
Pitfalls: independence, known predictors, clustered data
Model assessment, cross validation strategies

Day 5: Big spatial datasets
What is big?
Large vector datasets
Large raster datasets, image collections and data cubes
Cloud solutions, cloud platforms, platform lock-in