PNS0226 GIS and spatial analysis, 1.0 Credits
Subjects Mathematic Statistics
No Level Indicated
Pass / Failed
The requirements for attaining different grades are described in the course assessment criteria which are contained in a supplement to the course syllabus. Current information on assessment criteria shall be made available at the start of the course.
Admitted to PhD-studies. Basic knowledge of statistics and R
This course is part of the course theme Advanced Statistics in Practice and aims to fill potential knowledge gaps by preparing students to analyze, interpret and report their data using the most up-to-date methods in R. Special attention will be given to discussion of questions springing from the students’ own work and the biological interpretation of data. The course is intended to deepen the students’ understanding of all aspects of ecological inferences, not as a cookbook of "how to".
1-Demonstrate the ability to identify relevant functions and packages in R for analyzing their own datasets.
2-Analyze data using R, including reporting advanced statistics, univariate and multivariate modelling and generation of graphs.
3-Interpret, think critically and draw conclusions on data analysis results.
Unit 2: GIS and spatial analysis. February 15 and 18 2021 .
Teachers: Alistair Auffret (email@example.com) and Mohammad Bahram (firstname.lastname@example.org).
1.Intro: Why GIS in R? Which packages?
2.Vector analysis: Buffer zones, Intersections, Summary statistics etc.
3.Raster analysis: Overlays, Reclassification, Raster calculator
4.Spatial autocorrelation and its effect on your results, strategies and methods for accounting or using spatial autocorrelation
5.Analysing spatially-structured ecological data
The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two half days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.
Formats and requirements for examination
Participating PhD students will be evaluated as passed/not passed based on their overall engagement in classroom activities and fulfilling the assignments.
This course is the second independent course unit of five in the course theme Advanced Statistics in Practice organized by Mohammad Bahram in collaboration with the NJ-faculty research schools ’Ecology-basics and applications’ and ’Focus on Soils and Water’. The course plan for the theme and this course unit was accepted 20200922 by the steering group of the research school Ecology-basics and application.
Course units of the theme Advanced Statistics in Practice 2021
• Time series analysis: Jonas Knape and Örjan Östman, February 1
• GIS and spatial analysis: Alistair Auffret and Mohammad Bahram, Feb. 15 and 18
• Meta-analysis: Julia Koricheva, April 12-13
• Getting more out of community data, Tomas Roslin and Giovanni Strona, May 3 & May 6
• Dealing with complexities of GLMs: Matt Low, May 31 & June 1
Department of Ecology