Introduction to Geostatistics

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Duration : 1.0 day(s)

:: Course Summary

This 1-day course covers the latest methods in geostatistics for the exploration, analysis and interpretation of spatial data. It gives an overview of techniques like semivariograms and kriging through a series of examples and case studies.

:: Learning Objectives

Upon completion of this course, participants will be able to:

Gain an understanding of data exploration tools as a first step in spatial data analysis

Understand the nature and calculation of variograms

Learn the different linear kriging methods including ordinary and universal kriging

Learn statistical methods for validating models and how model performation is assessed using cross-validation techniques

:: Target Audience

People for which spatial location is important in analyzing data. The course is especially aimed at practitioners - business, government or academia - in the field of data analysis who want to come up to speed rapidly on these important set of tools, as well as people already familiar with spatial statistics who want to learn about the current state of the art.

:: Prerequisite

This course in spatial statistics assumes participants have a basic knowledge of the field as well as descriptive and inferential statistics.

:: Topics Covered

Exploratory Data Analysis
- location, spread, shape and empirical histograms
- box and stem plots and lattice plots
- cumulative distribution function, Q-Q and P-P plots
Estimating the empirical semivariogram
Median polishing
Loess trend analysis
Problems of anisotrophy
Methods of Kriging (why Kriging?)
Cross-validation procedures
Simulating geostatistical data (conditional & non-conditional)

:: Course Content

Analysis and modeling of spatial data will be an integral part of the course with a strong accent on the “hands-on” application of data utilizing spatial statistical techniques. Within the lecture presentations, there will be class demonstrations where a discussion of applications and problem solving procedures used in spatial statistics will be presented. In addition there will be a series of in-class sessions where the participants will work on different spatial data sets, discuss the application of geostatistical tools from start to finish in real-life scenarios and practice interpreting results provided by statistical software output.

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