Ready to harness the power of geostatistics for your data? In this Geospatial Specialization Course #1: Basic Principles of Geostatistical Geospatial Modeling course, you鈥檒l learn how to identify key variables, address outliers and missing data, and apply univariate and bivariate analyses鈥攁ll within the versatile R programming environment. You鈥檒l quickly master correlation and covariance matrices, construct dynamic visualizations (histograms, boxplots, crossplots), and uncover insights hidden in your spatial datasets. Next, you鈥檒l dive into advanced geostatistical techniques, such as building omnidirectional and directional variograms, developing nested models, and performing kriging and co-kriging for precise spatial predictions. You鈥檒l even explore conditional simulation to capture the full range of possible outcomes. Rigorous post-processing methods鈥攊ncluding cross-validation, error variance mapping, and isoprobability analyses鈥攍et you confidently validate and refine your models. Whether you鈥檙e tackling environmental or mining data (or anything in between), you鈥檒l finish the course with a powerful geostatistical toolbox and the know-how to apply it. Join us and discover how R-powered geostatistical modeling can transform raw data into actionable intelligence!
Introduction to Kriging
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Skills You'll Learn
Geostatistics, Data Analysis, Data Validation, Box Plots, Geospatial Mapping, Descriptive Statistics, Regression Analysis, Probability & Statistics, Spatial Data Analysis, Histogram, Correlation Analysis, Simulation and Simulation Software, Spatial Analysis, Exploratory Data Analysis, Geospatial Information and Technology, Statistical Modeling, Statistical Analysis, Statistical Methods, R Programming
From the lesson
Kriging
Taught By
Jeffrey Yarus
Research Professor