ENVS1134 Statistical methods for analyzing environmental data (5 cr)
Study level:
Advanced studies
Grading scale:
0-5
Language:
English, Finnish
Responsible organisation:
Department of Biological and Environmental Science
Curriculum periods:
2017-2018, 2018-2019, 2019-2020
Description
Content
An advanced course related to analyzing environmental and spatial data with R statistics software. Main topics include multivariate methods for modeling and data analysis, time series analyses, community composition analyses, and analysis methods for spatial data, including marked spatial point patterns, animal movement analyses, spatial autocorrelation, spatial modeling, species distribution models, and spatial interpolation.
Completion methods
Lectures 12 x 2 h, computer exercises 12 x 4 h, seminar, exercise work.
Assessment details
Exercise work report 50%, seminar presentation 40%, activity in exercises 10%.
Learning outcomes
After passing the course students should know and be able to describe the basic concepts, some simple methods and assumptions of: classification, clustering, and projection methods, time series analysis methods (both time domain and frequency domain), community composition analysis methods, spatial point pattern analyses, spatial autocorrelation estimation methods, spatial modeling methods, and spatial interpolation, especially kriging, methods. The students should also know how to perform statistical analyses and how to create maps with the R Statistics software, and be able to apply the analysis methods to new data.
Description of prerequisites
TILP2500, TILP2600 (Basics of statistics), and BENA4033 Basics of geoinformatics.
Literature
- Hastie T., Tibshirani R. & Friedman J. 2009. The Elements of Statistical Learning, 2nd Edition. Springer.; ISBN: 978-0-387-84857-0
- Zuur A., Ieno E.N., Walker N., Saveliev A.A. & Smith G.M. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer.; ISBN: 978-0-387-87457-9
- Cryer J. & Chan K.-S. 2008. Time Series Analysis with Applications in R, 2nd Edition. Springer.; ISBN: 978-0-387-75958-6
- Bivand R., Pebesma E. & Gomez-Rubio V. 2013. Applied Spatial Data Analysis with R, 2nd Edition. Springer.; ISBN: 978-1-4614-7617-7
Completion methods
Method 1
Select all marked parts
Parts of the completion methods
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Teaching (5 cr)
Type:
Participation in teaching
Grading scale:
0-5
Language:
English, Finnish