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Semester 2 - Dual-Delivery
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In this subject students will learn about the foundations of spatial data and their analysis. Emphasis will be placed on learning how to investigate the patterns that arise as a result of processes that may be operating in space. For example, students will learn to identify geographic clusters of disease cases, or hotspots of crime. A variety of scientific tools including probability theory, combinatorics, descriptive statistics, distributions and matrix algebra will be taught. Students will learn essential skills that are fundamental for all applications of geographic information.
The subject partners with other subjects on spatial data management and visualization, and is of particular relevance to people wishing to establish a career in the spatial information industry, the environmental or planning industry. Spatial Analysis builds on the fundamental knowledge of probability and statistics, mathematics, as well as computer literacy to write simple algorithms, and the preparation and management of data for sophisticated analysis software.
Spatial autocorrelation, spatial data structures and algorithms, point patterns, measures of dispersion, measures of arrangements, line and network analysis, patterns of areas and in fields, and the role of spatial scale and spatial aggregation problems.
Intended learning outcomes
On completion of this subject the student is expected to:
- Describe and discuss data structures and analysis procedures to analyse spatial data
- Design and run a spatial analysis appropriate to a given phenomenon
- Distinguish and characterise patterns and processes in geographic space
- Apply GIS software for spatial analysis, and interpret the results.
On successful completion students should have the:
- Ability to apply knowledge of science and engineering fundamentals
- Ability to undertake problem identification, formulation, and solution
- Ability to conduct an engineering project
- Ability to communicate effectively, with the engineering team and with the community at large
- Ability to manage information and documentation
Last updated: 8 May 2021