EAE5053 · Advanced spatial analysis and modelling

Overview

This unit aims to develop advanced knowledge and skills in spatial analysis and modelling within GIS. It covers spatial regression models, and the application of modern machine learning and deep learning algorithms to solve real-world geospatial problems . It also introduces methods and techniques for integrating advanced analysis and modelling tools with GIS or extending GIS functionality using the Python programming language to build new tools and custom GIS solutions. The unit will equip you with highly valued spatial analysis and modelling skills by the geospatial industry and research community.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Build a strong foundation in modern spatial analysis and modelling techniques;
2. Understand the assumptions and limitations of spatial analysis techniques;
3. Grasp skills in selecting appropriate spatial analysis and modelling techniques for a specific problem;
4. Gain practical experience in developing and applying a range of spatial analysis and modelling methods in a GIS;
5. Interpret, evaluate and communicate the results from spatial analysis and modelling.

Workload

• 1-hour lecture videos; • 2-hour workshops; • 3-hour practicals/projects and • 6-hour independent study per week

Student experience, not official rules. Nothing here changes what the Handbook says.