Education

We recommend the following educational resources.

Learning resources

Textbooks

Video lectures

  • Júlio Hoffimann - Video lectures with the GeoStats.jl framework.

  • Edward Isaaks - Video lectures on variography, Kriging and related concepts.

  • Jef Caers - Video lectures on two-point and multiple-point methods.

Workshop material

  • UFMG 2024 [Portuguese] - Geociência de Dados na Mineração, UFMG 2024

  • JuliaEO 2024 [English] - Global Workshop on Earth Observation, AIRCentre 2024

  • UFMG 2023 [Portuguese] - Geociência de Dados na Mineração, UFMG 2023

  • JuliaEO 2023 [English] - Global Workshop on Earth Observation, AIRCentre 2023

  • CBMina 2021 [Portuguese] - Introução à Geoestatística, CBMina 2021

  • UFMG 2021 [Portuguese] - Introdução à Geoestatística, UFMG 2021

GaussianProcesses.jl

GaussianProcesses.jl - Gaussian process regression and Simple Kriging are essentially two names for the same concept. The derivation of Kriging estimators, however; does not require distributional assumptions. It is a beautiful coincidence that for multivariate Gaussian distributions, Simple Kriging gives the conditional expectation.

KernelFunctions.jl

KernelFunctions.jl - Spatial structure can be represented in many different forms: covariance, variogram, correlogram, etc. Variograms are more general than covariance kernels according to the intrinsic stationary property. This means that there are variogram models with no covariance counterpart. Furthermore, empirical variograms can be easily estimated from the data (in various directions) with an efficient procedure. GeoStats.jl treats variograms as first-class objects.

Interpolations.jl

Interpolations.jl - Kriging and spline interpolation have different purposes, yet these two methods are sometimes listed as competing alternatives. Kriging estimation is about minimizing variance (or estimation error), whereas spline interpolation is about deriving smooth estimators for computer visualization. Kriging is a generalization of splines in which one has the freedom to customize geospatial structure based on data. Besides the estimate itself, Kriging also provides the variance map as a function of point patterns.

ScikitLearn.jl

ScikitLearn.jl - Traditional statistical learning relies on core assumptions that do not hold in geospatial settings (fixed support, i.i.d. samples, ...). Geostatistical learning has been introduced recently as an attempt to push the frontiers of statistical learning with geospatial data.