Credit points: 15
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
Subject Co-ordinatorAndriy Olenko
Available to Study Abroad StudentsYes
Subject year levelYear Level 4 - UG/Hons/1st Yr PG
Prerequisites STA3AS or STA4AS and (STA3SI or STM3SI) or (STA4SI or STM4SI) or enrolment into SMDS
Special conditions A sufficient background in probability and statistics is required to undertake this subject.
|Resource Type||Title||Resource Requirement||Author and Year||Publisher|
|Readings||Analysing spatial point patterns in R.||Recommended||Baddeley, A. 2008||WORKSHOP NOTES, VERSION 3.|
|Readings||Applied spatial analysis with R||Recommended||Bivand, R.S., Pebesma, E. J., Gomez-Rubio, V. 2008||SPRINGER.|
|Readings||Statistics for spatial data||Recommended||Cressie, N.A.C 1993||WILEY|
|Readings||Online learning materials (written specifically for this subject)||Prescribed||2016||La 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.
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Melbourne, 2019, Semester 1, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorAndriy Olenko
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."
|Four assignments||40||01, 02, 03, 04|
|one 3-hour examination||60||02, 03, 04|