FOR50020 GIS and Spatial Modelling

Academic Year 2019/2020

This is an advanced geospatial biological modelling module. The objective of the module is to integrate GIS and geospatial biololgical modelling.
Topics covered may include: A review of the fundamental equation and assumptions of linear and nonlinear regression analysis. Hypothesis testing and biological interpretation of model parameters. The extra sums of squares principle and partial F tests. Model building strategies. Asymptotic precision of the model parameters.
Matrix formulation of the analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). Integral and differential forms of nonlinear models including the simple exponential, monomolecular, Logistic, von Bertallanffy, Chapman-Richards, Richards and Weibull models. Biological interpretation of nonlinear sustained yield parameters.
Introduction to spatial forecasting and geographically weighted regression. Parameter estimation of linear and nonlinear models using R and Mathematica. Transparent and independent analysis, interpretation and reporting of geospatial biological models. Literature review of geopatial biological models. Application of geospatgial biological modelling to Coillte-COFORD permanent sample plot data from long-term forest experiments.
This 10 credit GIS and Biological Modelling module is offered as a postgraduate elective to all MSc, Structured PhD programme students, interested post-doctorates, academic staff and professionals registered in Continuous Professional Development module. This module is designed for researchers and professional required to undertake research and analysis of GIS and biological modelling data.
Postgraduates are encouraged to work on their own geospatial and biological data.
Upon completion of the module the 10 credits will appear on your UCD transcript. There is no formal end-of-trimester examination. Digital scientific papers on each exercise will account for 100% of the examination. The entire assessment is open-book, digital and encourages student's self-improvement of work. All examination files will be submitted as Winzip files, *.zip, through Blackboard. Grades will based be on the criteria specified at the following URL: http://www.ucd.ie/registry/assessment/

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Curricular information is subject to change

Learning Outcomes:

On completion of this GIS and geospatial biological modelling module postgraduates and professionals should be able to demonstrate their understanding and competencies under the following themes:
1. Understanding geospatial linear models within ArcGIS 10.1
2. Understanding geospatial nonlinear models within ArcGIS 10.1
3. Understanding geospatial forecasting models within ArcGIS 10.1
4. Understanding geospatial biological modelling within ArcGIS 10.1
5. Understanding parameter estimation using Mathematica 9 and R 6 within ArcGIS 10.1.
6. Understanding the creation and improvement of a portfolio of error-free scientific papers
7. Understanding digital submission of all examination Winzip files saved through Blackboard

This module may be taken as a directed study by mutual agreement between the postgraduate, the postgraduate Supervisor and the Lecturer. The credits from this module may be accumulated, along with the credits from other related modules, towards a Masters or PhD Degree which incorporates GIS.

Student Effort Hours: 
Student Effort Type Hours
Lectures

30
Computer Aided Lab

30
Specified Learning Activities

60
Autonomous Student Learning

60
Total

180
Approaches to Teaching and Learning:
Not yet recorded 
Requirements, Exclusions and Recommendations
Learning Requirements:

The ideal background for this module would be:
FOR 20100 Applied Biostatistics,
FOR 30310 GIS and Remote Sensing.
FOR 30360 GIS and Forest Sampling,
FOR 40080 GIS and Forest Inventory.
FOR 40120 GIS and Experimental Design,
FOR 50010 GIS and Remote Sensing 2.
or equivalent modules.

This is not an introductory module to ArcGIS 10.1 or to the
analysis of variance (ANOVA) of elementary univariate sampling or experimental design data.

Learning Recommendations:

The recommended texts for this module include:

Braun, John W. and Duncan J. Murdoch. 2007. A fisrt course in statistical programming with R. Cambridge University Press. 162p. ISBN 978-051-87265-2 (hardback). www.cambrigge.org/9780521872652

Crawley, Michael J. 2007. The R Book. John Wiley & Sons Ltd. Chichester, UK. 942p. ISBN-13:978-0-470-51024-7. www.wiley.com.

de Smith, Michael J., Michael F. Goodchild and Paul A. Langley. 2007. Geospatial Analysis. A Comprehensive Guide to Principles, Techniques and Software Tools. Matador, Winchelsea Press. 394p. ISBN 10: 1-905886-60-8. Email: books@troubador.co.uk www.troubador.co.uk/matador

Diggle, Peter, J. and Paulo G. Ribeiro Jr. 2007. Model-based Geostatistics. Springer Science+Business Media, LLC. 228p. ISBN 10-0-387-32907-2. www.springer.com


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: 1. Understanding geospatial linear models within ArcGIS 10.1 Week 2 n/a Not yet recorded No

20
Assignment: 5. Submission of improved zip files to Blackboard each Friday by 1500 Varies over the Trimester n/a Graded No

5
Assignment: 4. Understanding geospatial biological modelling within ArcGIS 10.1 Week 8 n/a Graded No

20
Assignment: 3. Understanding geospatial forecasting models within ArcGIS 10.1 Week 6 n/a Graded No

25
Assignment: 6. Submission of feedback, self assessment and time management Varies over the Trimester n/a Graded No

5
Assignment: 2. Understanding geospatial nonlinear models within ArcGIS 10.1 Week 4 n/a Graded No

25

Carry forward of passed components
Not yet recorded
 

Not yet recorded
Please see Student Jargon Buster for more information about remediation types and timing. 
Not yet recorded