GS41256 – Geo-statistics in GIS
Course Outline
Course |
: GS 41256 – Geo-Statistics in GIS |
Core/Specialization |
: Specialization in Geographic Information System |
Programme |
: Bachelor of Science Honours in Surveying Science |
Department |
: RSGIS |
Faculty |
: Faculty of Geomatics |
Contact Hours |
: 100 |
Year |
: IV |
Semester |
: I |
Lecturer |
: Dr DR Welikanna |
Room No. |
: |
Telephone No. |
: 0453453071 |
|
: drw@geo.sab.ac.lk |
Synopsis
The course provides a complete overview of Geo-statistics, in order to students who wish to apply spatial and geo statistical computing in research and consulting projects. The main objective is to equip the students to continue learning and applying geo-statistical techniques to own problems.
Contents
Geo-statistical computing, Exploring and visualizing spatial data, Modelling spatial structure from point samples, Spatial analysis, Spatial prediction from point samples, Assessing the quality of spatial predictions, Spatial sampling, Interfacing R spatial with GIS, Point pattern analysis
Practical Tasks
Computational statistics with the R environment and the R Commander GUI, univariate descriptive statistics, and univariate exploratory data analysis, Statistical techniques to discover the relation between variables, Exploring and visualizing spatial data, Modelling spatial structure from point samples, Spatial data analysis, both graphical and numerical to find evidence of spatial structure, both over the whole area and locally, Spatial prediction from point samples (use the results of the spatial data analysis to predict over an interpolation grid by different methods), The concept of indicator variable
Learning Outcomes
By the end of the course, students should be able to: |
|||
No. |
Course Learning Outcome |
Programme Outcome |
Assessment Methods |
1. |
Select and apply appropriate visualization and numerical techniques to explore the structure of spatial data set |
P01 |
Final Exam/ Assignment /Lab Practical |
2. |
Model the structure of a spatial data set |
P01, P04 |
Final Exam/Lab Practical/ Assignment |
3. |
Select and apply appropriate procedures to predict data values at unvisited locations, using parametric and non-parametric models |
P01, P04 |
Final Exam/Lab Practical/ Assignment |
4. |
Deign a sampling strategy to reveal or account for spatial structure |
P01 |
Final Exam/ Assignment |
5. |
Use the R environment for statistical computing at an intermediate level |
P01, P02 |
Assignment / Lab Practical |
Student Learning Time (SLT)
Teaching and Learning Activities |
Student Learning Time (hours) |
Directed Learning |
|
|
12 |
|
40 |
Independent Learning |
|
|
14 |
|
10 |
|
08 |
|
07 |
Assessment |
|
|
03 |
|
05 |
|
01 |
TOTAL (SLT) |
100 |
Teaching Methodology
Lectures, and individual assignments, and individual or group practical |
References
- de Gruijter, J., Brus, D. J., Bierkens, M. F. P., & Knotters, M. (2006). Sampling for Natural Resource Monitoring: Springer.
- C. V. Deutsch, 2002, Geostatistical Reservoir Modeling, Oxford Univeristy Press, 376 pp.
- Bivand, R. S., Pebesma, E. J., and G´omez-Rubio, V. (2008). Applied Spatial Data Analysis with R. Springer.
- Diggle, P. J. and Ribeiro, P. J. (2007). Model-based Geostatistics. Springer.
- Schreuder, H. T., Ernst, R., & Ramirez-Maldonado, H. (2004). Statistical techniques for sampling and monitoring natural resources. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Gen. Tech. Rep. RMRS-GTR-126. http://www.fs.fed.us/rm/pubs/rmrs_gtr126.html
- Stein, A., & Ettema, C. (2003). An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons. Agriculture, Ecosystems & environment, 94(1), 31-47.
Grading
Assignments (x3) |
20% |
Practical Assignment (x2) |
30% |
Final Examination |
50% |
Total |
100% |