https://github.com/edzer/sswr

Spatial Statistics with R course materials
https://github.com/edzer/sswr

r spatial statistics-course

Last synced: 3 months ago
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Spatial Statistics with R course materials

• Host: GitHub
• URL: https://github.com/edzer/sswr
• Owner: edzer
• Created: 2024-03-01T20:55:27.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-03-26T15:20:05.000Z (9 months ago)
• Last Synced: 2024-05-09T10:30:29.584Z (8 months ago)
• Topics: r, spatial, statistics-course
• Homepage: https://edzer.github.io/sswr/
• Size: 14.3 MB
• Stars: 16
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Spatial Statistics with R course materials

Materials used in the course [Spatial Statistics with

R](https://www.physalia-courses.org/courses-workshops/spatial-statistics/),

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](https://creativecommons.org/licenses/by-sa/4.0/)