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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

E-mail

:  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

  1. Lecturers and Student-Centered Learning (SCL)

12

  1. Lab Practical

40

Independent Learning

  1. Student centered  learning activities

14

  1. Lab Practical activities

10

  1. Self-Learning (Library & Internet)

08

  1. Revision

07

Assessment

  1. Assignments

03

  1. Lab Practical Assignment

05

  1. Final Examination

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%