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SPA402 Spatial Statistics (8)

Abstract

This subject provides specialised skills in spatial science by focusing on the application of statistical techniques in spatial data analysis. The fundamental approach used will be to highlight the practical value of using modern techniques instead of previous compromises. To this end, case studies from several discipline areas will be used together with a brief historical perspective. A feature of this subject will be the practical component involving data analysis and interpretation for various problems using computer packages and algorithms.

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Continuing students should consult the SAL for current offering details prior to contacting their course coordinator: SPA402
Where differences exist between the handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Computing and Mathematics

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to develop specialist skills in the use of smoothing and interpolation in order to produce maps and estimate problems;
  • be able to sample spatial features, test their patterns, and estimate their densities;
  • be able to apply their specialist skills and abilities in order to interpret the results of an analysis of a mapped point pattern;
  • be able to demonstrate they can apply their knowledge and skills about statistical techniques in spatial data analysis in order to make high level, sound independent judgments regarding the most effective technique to use across a range of applications
  • be able to demonstrate they can apply their knowledge and skills about statistical techniques in spatial data analysis in order to make high level, sound independent judgments regarding the computer facilities needed to execute spatial analysis and interpretation appropriately.

Syllabus

The subject will cover the following topics:
  • An overview of history of the subject and areas of application via case studies
  • Sampling methods for 2D data
  • Smoothing techniques and their role in other analyses
  • Graphical methods for interpretation
  • Packages and computer skills;
  • Spatial patterns
  • Sampling methods in 2D
  • Smoothing and interpolation
  • Use of the variogram and autocorrelation
  • Kriging and cokriging
  • Case studies in ecology, geostatistics and field experiments.

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The information contained in the 2015 CSU Handbook was accurate at the date of publication: 01 October 2015. The University reserves the right to vary the information at any time without notice.