LCE5735 - Introduction to Spatial Statistics

Credit hours

Instructor
Deoclecio Jardim Amorim

Objective
To train students in the use of techniques for the analysis of spatial and geostatistical data. To present
the theoretical foundations and methods of exploratory analysis. To identify and apply these analytical
methodologies to practical situations in agricultural sciences, based on both computational and
theoretical aspects.

Content
1. Preliminary Concepts of Spatial Analysis: a) Spatial statistics and geostatistics; b) Notation; c)
Motivating examples. 2. Exploratory spatial data analysis: a) Univariate and bivariate description; b)
Spatial description; d) Spatial continuity analysis. 3. Data Interpolation Methods: a) Basic concepts; b)
Global and local methods; c) Categorical data interpolation; d) Statistical limitations. 4. Theory of Spatial
Random Fields: a) Valid covariance functions; b) Stationarity hypotheses; c) Parametric families of
semivariograms. 5. Inferential Theory of Kriging: a) The kriging paradigm as BLUP (Best Linear Unbiased
Predictor); b) Simple kriging; c) Ordinary kriging; d) Kriging with a trend; e) Interpolation variance; f)

Non-linear geostatistics; g) Validation methods. 6. Co-Kriging and stochastic simulation: a) The Co-
Kriging paradigm; b) Spatial stochastic simulation. 7. Model-Based geostatistics and hierarchical

Approach: a) Likelihood-based inference; b) Model-based geostatistical generalized linear models; c)
Hierarchical Bayesian geostatistics. 8. Integration with data science: a) Spatial machine learning; b)
Spatial cross-validation; c) Hybrid classic-modern models. All methods and applications will be
performed using data analysis software (R and/or Python).

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