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Spatial Data Analytics (GEOM90006)
Graduate courseworkPoints: 12.5On Campus (Parkville)
To learn more, visit 2023 Course and subject delivery.
About this subject
Contact information
Semester 1
Jagannath Aryal
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
Much of the world’s data relates to processes and objects situated in space. Spatial data is a rich source of insights about patterns, processes, trends and behaviours in space and time. To tap into these insights, specialised statistical, analytical and computational techniques are required.
This subject exposes students to fundamental aspects of spatial analytics. Students are introduced to key techniques and principles for the analysis of point, area, and field data, covering concepts such as point pattern analysis, spatial autocorrelation and geostatistics. As part of putting these techniques and principles into practice, students learn computational thinking approaches and acquire technical software skills in a high-level scripting language (such as Python and R) that enable them to effectively address spatial data science problems across a variety of domains.
The subject partners with other subjects on spatial data management and visualisation and is of particular relevance to people wishing to establish a career in digital infrastructure, spatial information technology, or the quantitative environmental modelling or planning sectors.
The subject delivers underlying and cross-disciplinary concepts of geographic information science (GIS) and spatial analytics in managing environmental and infrastructure data, and the visual representation of spatial and temporal information. Relating these relevant concepts to applications through case study examples from various sectors such as digital infrastructure, spatial information technology, quantitative environmental modelling, urban sustainability, and planning. Defining and realizing a student-driven project employing a modern scripting language and spatial-temporal relationships of the observed data from real world.
Students will be provided with pointers and material to familiarise themselves with the tools used in this subject before the semester starts; this element of preparation is expected for successful participation in the subject. Advice will be provided on LMS.
Intended learning outcomes
On completion of this subject the student is expected to:
- Distinguish and characterise spatial patterns and processes captured in infrastructure and environmental data;
- Design and apply spatial analyses appropriate to given spatial processes, for example, urban sustainability and environmental management;
- Implement and test data structures and analysis procedures for spatial data in a transparent, collaborative, reusable and replicable manner;
- Interpret and critically evaluate a computational implementation of a spatial analysis project.
Generic skills
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 independently a project
- Ability to communicate effectively, with a team and with the community at large
- Ability to manage information and documentation
- Understanding of professional and ethical responsibilities, and commitment to them
- Capacity for lifelong learning and professional development.
Last updated: 31 January 2024