GG4328 Advanced Topics in Geographic Information Science (GISci)
Academic year
2024 to 2025 Semester 1
Curricular information may be subject to change
Further information on which modules are specific to your programme.
Key module information
SCOTCAT credits
30
SCQF level
SCQF level 10
Planned timetable
Mon: 1-5pm
Module coordinator
Dr U Demsar
Module Staff
Dr Urska Demsar
Module description
Students will learn how to use advanced GIScience methods for applications in human and environmental geography, including spatial statistics methods, methods for analysis of movement or other contemporary developments in GIScience. The module is structured around two types of methods, each of which has a specific application purpose and is assessed in a small pre-defined lab project. Each part also has a related seminar, where students find and present applications of the method. The final part of the module consists of an independent research project, where each student selects their own topics, find data and runs appropriate spatial analyses. We further run an annual career event, GIS Day, where we invite professionals from industry and academia to come and present how they use GIS and spatial analysis in their work. The module will use Free and Open Source Software (e.g. QGIS, R, Python, GeoDA), to be compatible across the Win/Mac divide.
Relationship to other modules
Pre-requisites
BEFORE TAKING THIS MODULE YOU MUST PASS GG3209
Anti-requisites
YOU CANNOT TAKE THIS MODULE IF YOU PASS GG4228 OR TAKE GG4228
Assessment pattern
100% coursework
Re-assessment
100% coursework
Learning and teaching methods and delivery
Weekly contact
2 lectures (X9 weeks), 2 seminars (X5 weeks), 2 practical labs (X10 weeks)
Scheduled learning hours
48
Guided independent study hours
250
Intended learning outcomes
- Display familiarity with two different groups of advanced spatial analysis methods,
- Analyse complex spatial data independently,
- Display problem solving and troubleshooting skills applicable to spatial research projects,
- Define and execute their own research project, and
- Display confidence in the use GIS and spatial data science software.