Spatial Statistics with R course materials

Materials used in the course Spatial Statistics with R, held Mar 11-15, 2024, online.

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

License

Materials found here are distributed under CC-BY-SA