Credit points: 15

Subject outline

The subject surveys the theory of random fields, spatial processes, spatial statistics models, and their applications to a wide range of areas, including image analysis and GIS (geographic information system). The subject will cover the methodology and modern developments for spatial-temporal modelling, estimation and prediction, and spectral analysis of spatial processes. All the methods presented will be introduced in the context of specific datasets with GRASS and R software.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorAndriy Olenko

Available to Study Abroad StudentsYes

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites STA3AS or STA4AS and (STA3SI or STM3SI) or (STA4SI or STM4SI) or enrolment into SMDS


Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditions A sufficient background in probability and statistics is required to undertake this subject.


Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsAnalysing spatial point patterns in R.RecommendedBaddeley, A. 2008WORKSHOP NOTES, VERSION 3.
ReadingsApplied spatial analysis with RRecommendedBivand, R.S., Pebesma, E. J., Gomez-Rubio, V. 2008SPRINGER.
ReadingsStatistics for spatial dataRecommendedCressie, N.A.C 1993WILEY
ReadingsOnline learning materials (written specifically for this subject)Prescribed2016La Trobe University

Graduate capabilities & intended learning outcomes

01. Formulate purposeful questions to explore new statistical ideas and subsequently design valid statistical experiments.

Students will be given examples of practical problems in GIS, geosciences and environmental sciences. They will learn new statistical techniques to model raster and vector data.

02. Present clear, well structured proofs of important theoretical statistical model results.

Students will be given examples of proofs of some key results about theoretical properties of spatial statistical models. Based on information provided in lectures they repeat proofs in details or modify proofs for similar models.

03. Creatively find solutions to real world problems consistent with those commonly faced by practicing statisticians.

Students will be introduced to practical analysis of spatial data using R and GRASS software and examples of different real spatial data sets.

04. Professionally defend or question the validity of existing statistical analyses and associated evidence-based conclusions that are derived via application of sound spatial statistical methodology.

Students will be given examples of analyses and interpretations of spatial information. Based on information provided in lectures/computer labs they repeat analyses for similar data.

Subject options

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Start date between: and    Key dates

Melbourne, 2018, Semester 1, Day


Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAndriy Olenko

Class requirements

Lecture/Practical Week: 10 - 22
One 2.0 hours lecture/practical per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
"Two contact hours per week."


Assessment elementComments% ILO*
Four assignments40 01, 02, 03, 04
one 3-hour examination60 02, 03, 04