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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)
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