Tag

Geostatistics

Geostatistics – III

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this third workshop, we will combine the material we covered in the first two workshops and develop the geostatistical modeling approach. This is mainly a lecture style workshop, but will include an example in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.

Geostatistics – II

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we are covering the basics of Geostatistics. In this second workshop, we will focus on covariance and variogram, and their estimation in the context of geostatistical modeling. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.

Geostatistics – I

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sparsely sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this first workshop we will understand the idea of stationary random fields, positive definite functions, and the fundamental building blocks of Gaussian random fields. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the foundations of Gaussian Process Regression, a commonly used technique in Machine Learning and AI.

Geostatistics – II

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In many situations, we observe a process over a limited number of locations and are interested in estimating its values at nearby unsampled locations. Geostatistical methods provide a powerful tool to do this. In this session, we will build upon the material and intuitions covered in the first workshop. We will focus on the principles, application, and interpretation of results using R, and will cover topics such as variogram modeling, empirical variogram, estimation techniques, and prediction uncertainty.

You should be familiar with R and the basic concepts of GIS data, should have taken an introductory course in statistics, and know the basics of linear algebra.