/sswr

Spatial Statistics with R course materials

Spatial Statistics with R course materials

Materials used in the course Spatial Statistics with R, held Mar 11-15, 2024, and again on Nov 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
  • MaxEnt

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: Lattice data, big spatial datasets

  • Analysing lattice data: neighbours, weights, models
  • 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