Syllabus academic year 2011/2012
(Created 2011-09-01.)
Credits: 7,5. Grading scale: TH. Cycle: A (Second Cycle). Main field: Technology. Language of instruction: The course might be given in English. EXTN75 overlaps following cours/es: GISN01. Optional for: L4gi. Course coordinator: Petter Pilesjö,, Department of Physical Geography. Prerequisites: EXTF80/EXTA45 Geographic Information Technology or EXTF01 Geographical Information Systems for Landscape Studies and basic course in programming and in mathematical statistics. The course might be cancelled if the number of applicants is less than 16. The number of participants is limited to 30 Selection criteria: Total number of credits on master level within specialisation of LTH program. Assessment: Assessment takes the form of a written examination. Approved on all exercises and participation on all compulsory activities. Home page:

The course aims to equip students with fundamental theoretical knowledge and practical skills in the methods of spatial analysis.

Knowledge and understanding
For a passing grade the student must

• explain correlations (relationships) within and between geographical data sets

• interpret, discuss, and apply regression methods using geographic data

• explain and apply geostatistics

• explain scaling issues affecting spatial analysis and geographic data at depth

• generally describe analysis approaches for very large data sets (data mining)

• explain the foundations of the theory behind spatial decision support systems.

Skills and abilities
For a passing grade the student must

• independently analyse and intepret the results from regression models, and

• understand and apply particular spatial analytical methods to geographic data.

Judgement and approach
For a passing grade the student must

• to be able to independently relate to both spatial and conventional statistical measures and methods,

• critically appreciate the nature of geographic data and analytical techniques

• assess the reliability of analyses conducted with different methods.

The course consists of 5 modules:

• regression and other basic methods in statistical modelling

• geostatistics

• scaling issues

• analysis of very large data sets (data mining)

• spatial decison support systems.

Course compendium from the department.