Study

Distance course in

Geostatistics and Open-Source Statistical Computing

Almost all the interesting data collected in geographical studies are from known locations on, in or over the Earth’s surface. These data usually have a spatial structure that can be visualised and modelled. The resulting models can be used to map by interpolation, for example, using kriging or trend surfaces. Often, conventional statistical methods are not valid when there is spatial covariation, so specific geostatistical methods must be used to make inferences. The model of spatial covariation can also be used to design sampling plans.

Statistical computing is becoming routine among geoscientists. In addition, extensive computing using actual datasets is the best way to learn statistics. The R Project for Statistical Computing is the leading open-source environment for exploratory, introductory and advanced computational statistics. R includes several add-in geostatistical packages. This course will use mostly the GSTAT package, although students may elect to use any software that is available in their organisation for a data analysis project.

For whom is the course relevant?

This course is aimed at postgraduate students and working professionals who wish to apply spatial statistics and geostatistical computing in research and consulting projects.

What will be achieved?

On completion of this course, participants should be able to:

• select and apply appropriate visualisation and numerical techniques to explore the structure of a spatial dataset
• select and apply appropriate procedures to model the structure of a spatial dataset
• select and apply appropriate procedures to predict data values at unvisited locations, using parametric and non-parametric models
• design a sampling strategy to reveal or account for spatial structure
• use the R environment for statistical computing at an intermediate level and be able to improve their skills by self-study and experimentation.

Learning from a distance, how is it like?

The general approach of the course is task based learning which blends theory and practice. The study load is 20 to 24 hours per week. All materials including (most of) the software will be provided online in ITC's digital learning environment Blackboard. For convenient offline study most materials will be sent on a CD-Rom. We will use email for individual communication and a discussion board in Blackboard for group communication.

Admission requirements

Academic level and background
Applicants for the Distance programme should have a Bachelor degree or equivalent from a recognised university in a discipline related to the course, preferably combined with working experience in a relevant field.

As all courses are given in English, proficiency in the English language is a prerequisite. Participants in Distance programme are exempted from an English language test. However, ITC expects their proficiency in the English language to meet the minimum requirements mentioned below.

Applicants for the Distance education programme must have elementary computer experience, and internet, e-mail and bulletin board skills.

Distance courses require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.

Software requirements

• Internet Explorer 5.0 (6.0 or higher recommended)
• Adobe Acrobat Reader
• Course specific software

Hardware requirements

ITC expects the hardware to meet the minimum requirements mentioned below.

What more there is to know?

This distance course is based on a module of one of ITC's degree courses, successful completion will lead to exemption for that module in the degree course.