Education

We recommend the following educational resources.

Learning resources

Textbooks

Hoffimann, J. 2023. Geospatial Data Science with Julia - A fresh approach to data science with geospatial data and the Julia programming language.
Isaaks, E. and Srivastava, R. 1990. An Introduction to Applied Geostatistics - An introductory book on geostatistics that covers important concepts with very simple language.
Chilès, JP and Delfiner, P. 2012. Geostatistics: Modeling Spatial Uncertainty - An incredible book for those with good mathematical background.
Webster, R and Oliver, MA. 2007. Geostatistics for Environmental Scientists - A great book with good balance between theory and practice.
Journel, A. and Huijbregts, Ch. 2003. Mining Geostatistics - A great book with both theoretical and practical developments.

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 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 - 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 spatial 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.