SPA414 Critical Review of GIS and Geocomputation (8)

This subject investigates a range advanced applications associated with spatial science and develops academic reporting skills. The discipline of geographic information science is critically appraised to consider its strengths and weaknesses, particularly with respect to time, uncertainty, multimedia and multiscale. The conversation leads to geocomputation, a term describing advanced techniques in spatial science that generally require significant computing power. Four major areas of geocomputation are discussed: optimisation, pattern recognition & classification, modelling & simulation and measuring & analysing. This subject  has no residential school. Students completing this subject are able to make sound independent judgements about the application of a range of advanced spatial analysis techniques including those that have led to a much wider use of this technology in science, government, business, and industry.
 

No offerings have been identified for this subject in 2020.

Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System

HD/FL

Duration

One session

School

School of Environmental Sciences

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to describe strengths and limitation of geographic information science and explain recent development of the field of GIS and spatial data dissemination;
  • be able to use the specialised data models that currently underlie spatial information management, and appreciate the priorities for future data model development in GIS
  • be able to make sound independent judgements regarding the use of advanced spatial science methodologies in the field of geocomputation

Syllabus

This subject will cover the following topics:
  • Society and web mapping;
  • Online decision support systems and stakeholder GIS;
  • Metadata and infrastructures for data sharing;
  • Spatial uncertainty;
  • Spatiotemporal GIS;
  • Multiscale GIS;
  • Integrating multimedia with geolibraries;
  • Object oriented GIS;
  • Cellular automata and agent-based modelling;
  • Spatial data mining and geo-visualisation;
  • Expert systems;
  • Fuzzy logic;
  • Fractals;
  • Neural networks;
  • Genetic algorithms;
  • Heuristic search.

Special Resources

Students are expected to be able to gain access for a significant amount of the session to an IBM PC or one of its many variants.

The information contained in the CSU Handbook was accurate at the date of publication: January 2020. The University reserves the right to vary the information at any time without notice.

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