Ekaterina Chuprikova, Dr.-Ing.

While at the Technical University of Munich, I taught a Geostatistics course to Master’s students of the International Master in Cartography. This course provided a comprehensive introduction to the field of geostatistics, aimed at Master’s students interested in data analysis and spatial data applications. Covering a wide range of topics from the basics to more advanced techniques, the curriculum was designed to equip students with the necessary skills to analyze and interpret spatial data effectively. Through the integration of theoretical knowledge and practical application, particularly with the R programming language, this course prepared students for handling various geospatial data analysis tasks.

Course Topics Breakdown:

1. Introduction to Geostatistics: This part laid the groundwork by introducing the foundational principles of geostatistics. It highlighted the importance of the field and its critical application in the analysis of spatial data.
2. Introduction to R: The course covered the essentials of R programming, emphasizing its importance as a tool for statistical analysis and data visualization, crucial for any data analyst’s toolkit.
3. Exploratory Statistics: The curriculum included techniques for initial data investigation, showcasing how to uncover underlying patterns, detect outliers, and summarize datasets to inform further analysis.
4. Spatial Point Statistics: I taught the methods for examining the distribution and intensity of points within a given space, crucial for understanding spatial relationships and phenomena.
5. Deterministic Models: Instruction was given on various deterministic models, including Thiessen polygons, inverse distance interpolation, trend surface analysis, nearest neighbor, and moving window techniques, demonstrating different approaches to spatial data interpretation.
6. Variography: The course delved into variography, teaching students how to analyze and model spatial dependence, a key concept in preparing for more sophisticated spatial analyses.
7. Local Estimation or Prediction: Detailed instruction on kriging techniques was provided, covering Ordinary Kriging, Declustering, Block-, and Universal Kriging, outlining each method’s application for accurate spatial estimation and prediction.
8. Advanced Kriging Techniques: The curriculum extended to Log-Normal and Indicator Kriging techniques, presenting these as solutions for specific analytical challenges and data types.
9. Cross Validation and Residual Analysis: Finally, the course emphasized the processes of cross-validation and residual analysis, teaching methods to verify the accuracy and effectiveness of spatial models and analyses.