Spatial Statistics

Course content The course covers the following topics and practical examples will be done in R and QGIS:

• The Need for Spatial Analysis
• Types of Spatial Data
• Autocorrelation-Concept and Elementary Measures
• Autocorrelation Functions
• The Effects of Autocorrelation on Statistical Inference
• Random, Aggregated, and Regular Patterns
• Binomial and Poisson Processes
• Testing for Complete Spatial Randomness
• Second-Order
• Properties of Point Patterns
• The Inhomogeneous Poisson Process
• Marked and Multivariate Point Patterns
• Point Process Models
• Semi-variogram and Co-variogram
• Covariance and Semi-variogram Models
• Estimating the Semivariogram;
• Spatial Prediction and Kriging: Optimal Prediction in Random Fields
• Linear Prediction-Simple and Ordinary Kriging
• Linear Prediction with a Spatially Varying Mean
• Kriging in Practice
• Estimating Covariance Parameters
• Model- and Design-based spatial sampling
• Testing for similarity of spatial data sets.

Accreditation: 

SACNASP CPD Points