Data availability and computation power offers unprecedented opportunities for scientific questions and practical applications. Being able to handle and analyze different data sets is a well-recognized skill on the labor market not only among ecologists.
During this intensive user-oriented course students will learn key skills and techniques in statistical and geospatial modelling. The course comprises a set of lectures that present modelling concepts and procedures, as well as practical sessions where students can use the software and scripting routines to achieve large scale data analysis and standalone implementation processes. Modelling, management and handling of large quantities of data will be conducted in R.
After the course, students should be able to:
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Use the statistical program R for data analytisc as a GIS modelling environment to apply the treated methods.
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Compare the covered modelling methods and reflect on underlying common principles. Critically judge and communicate the results of a statistical model or spatial analysis.
Main themes of the course (lectures and exercises):
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Statistical modelling: General(ized) linear models, non-parametric regressions (e.g. Regression trees), Ordination and classification techniques, mixed effects models, spatial models.
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Spatial analyses: spatial autocorrelation, spatial interpolation, point and area analyses.
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Spatial data: generation/loading and processing spatial data from different sources.
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Data management: Introduction to programming in R, data storage and computation in R, management manipulation and storage of spatial objects.
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Document your science: Using
R MarkdownandRto document your data analysis process, and report your results.
These topics will be addressed in 13 weeks of classes and one week project.
Alejandro Ordonez with parts of the course being supported by further scientists/experts.