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SG4228   Advanced Topics in Geographic Information Science (GISci)

Academic year(s): 2018-2019

Key information

SCOTCAT credits : 20

ECTS credits : 10

Level : SCQF level 10

Semester: 1

Availability restrictions: Available every year.

Planned timetable: Lectures - 1.00 pm - 3.00 pm Mon Practicals - 3.00 pm - 5.00 pm Mon

The first part of the module explores advanced GIS and spatial analysis techniques for use with geographic datasets. Students will gain theoretical and applied knowledge in order to study and describe spatial patterns in geographic data. Theoretical understanding will be emphasised through lectures and readings. Labs/practicals will be designed to provide students with hands-on experience applying theory and techniques to datasets spanning human and environmental geography using applications including crime, forestry, health, environmental change, and housing. Focus will be placed on methods for analysing spatial point patterns, spatial autocorrelation, and spatial modelling. In the second part, students will engage in a small research project of their choosing to showcase their new advanced GIS skills. The project will allow students to use GIS and spatial analysis techniques to address a chosen problem in either of the social, physical, or environmental sciences. Students with domain knowledge in human geography, physical geography, or sustainable development will be able to tailor GIS projects to their own applications.

Relationship to other modules

Pre-requisite(s): Before taking this module you must pass GG3201

Learning and teaching methods and delivery

Weekly contact: 2 hours lectures (x 9 weeks), 2-hour practical classes (x 6 weeks), 2-hour IT Lab Help sessions (x 3 weeks)

Scheduled learning hours: 36

Guided independent study hours: 164

Assessment pattern

As used by St Andrews: Coursework project = 100%

As defined by QAA
Written examinations : 0%
Practical examinations : 0%
Coursework: 100%

Re-assessment: No Re-assessment available


Module teaching staff: Dr J Long, Dr U Demsar