A New Short Course in Environmetrics

An Introduction to Spatial Statistics

E nvironmetrics Australia is pleased to announce a new short course in Spatial Statistics.

Many traditional methods of statistical analysis assume independence in sampled data.

In an environmental context this is unlikely to be the case. Conventional practice is to

either (a) proceed as if the spatial dependency didn’t exist; (b) use physical or statistical

means of controlling or reducing the effects of spatial dependency; or (c) use a more

sophisticated approach to model the spatial dependency. This short course is intended

to provide the participant with an introduction to the last of these: modelling spatial

dependency.

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As with all courses offered by the Australian Centre for

Environmetrics, the course in Spatial Statistics will be

‘hands-on’. Participants will be introduced to important

statistical concepts in an easily-grasped manner and the

learning will be consolidated by working through a series

of computer-based exercises. The course is divided into two components: Part I deals

with the characterisation and modelling of spatial dependency for quantitative data (eg.

soil moisture properties, PM10, nutrients in a waterbody) while Part II looks at

statistical models for spatial point processes

associated with qualitative data (eg. spatial

distribution of diseased trees, sightings of a rare

or threatened species, distribution of

contaminated sites).

Environmetrics Australia E: info@environmetrics.net.au W: http://www.environmetrics.net.au

Course Outline Part I : Modelling Spatial Dependency

• Characterising spatial dependency – the variogram • Estimating the variogram • Variogram model fitting • Spatial interpolation

- Triangulated irregular networks (TINs) - Inverse distance weighting - Kriging 2-D variogram surface

• Ordinary Kriging • Block Kriging • Indicator Kriging

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Part II : Spatial Point Processes

• Spatial point processes and patterns • Complete Spatial Randomness (CSR) • Homogeneous Poisson processes • Heterogeneous Poisson processes • Basic statistics for describing spatial point patterns • Nearest-neighbour distribution functions • The K-function • Modelling spatial point processes

Software Tools Used

• R version 2.3.0 (comprehensive statistical software package)

• VarioWin (variogram estimation and modelling)

• SGeMS (Stanford Geostatistical Modelling Software)

• Spatstat (R library for statistical analysis of point pattern data)

• FIELDS (R library for modelling spatial data)