Geostatistics and Open-Source Statistical Computing

Event Organisers: 

Faculty of Geo-Information Science and Earth Observation


21/01/2013 to 28/02/2013

Registration Deadline: 

Tuesday, January 1, 2013

Language of event: 



Distance course in

Geostatistics and Open-Source Statistical Computing


Certification Location Start Duration ECTuition fee Registration deadline NFP registration deadline Register
Certificate Distance 21 Jan 2013 6 weeks 5 EUR 1000 / 500 Registration is closed 02 Oct 2012 n.a.


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 visualized 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 organization 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 visualization 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.

English language

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.

English language tests: minimum requirements

TOEFL Paper-based Test (PBT) 550
TOEFL Internet-based Test 79-80
British Council / IELTS 6.0
Cambridge CPE/CAE

Computer skills

Applicants for the Distance education programme must have elementary computer experience, regular access to internet and e-mail. For some courses additional computer skills are required (see description of specific course).

GIS and remote sensing

Most distance courses, except for the introductory course, require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.

Candidates are asked to provide proof of identity during the registration process.

Hardware and software requirements

  • Any modern web browser compatible with BlackBoard (ITC's electronic learning environment).
  • Any reasonable laptop/desktop running Windows XP or later, Mac OS/X 10.5 or later, or Linux.
  • For those using OS/X or Linux, a Windows emulator such as Wine to run one Windows program.
  • Good internet connectivity.

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.

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