Spatial Data Analytics (GEOM90006)
Graduate courseworkPoints: 12.5On Campus (Parkville)
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.
Please view this video for further information: Spatial Data Analytics
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: 4 March 2025
Eligibility and requirements
Prerequisites
Corequisites
Non-allowed subjects
GEOM90042
Recommended background knowledge
Knowledge of geographic information systems (GIS) such as that gained from the new online modules available in the LMS Commons for example GIS For Engineers: QGIS Module would be beneficial. Fundamental skills in Python or a similar procedural programming language would also be beneficial – however, training is provided during the subject for those with no programming background.
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Assignment 1 Part 1: Programming skills task - Jupyter Lab / Notebook. 5 hours (documented code – no word equivalent). Intended Learning Outcome (ILO) 3 is addressed in this assessment.
| Week 2 | 2% |
Assignment 1 Part 2: Programming skills task - Jupyter Lab /Notebook. 5 hours (documented code – no word equivalent). (ILO) 3 is addressed in this assessment.
| Week 3 | 2% |
Assignment 2: formative task – responses to around five questions representing the fundamentals of Assignment 2 . 5 hours (documented code – ~ 150 words equivalent). (ILOs) 2 and 3 are addressed in this assessment.
| Week 4 | 2% |
Assignment 2: Individual assessment of a spatial data analysis project. 25 hours (an equivalent of 1000 words). (ILOs) 2 and 3 are addressed in this assessment.
| Week 6 | 20% |
Assignment 3: formative task – responses to around five questions representing the fundamentals of Assignment 3. 5 hours (documented code – ~150 words equivalent). (ILOs) 2 and 3 are addressed in this assessment.
| Week 6 | 2% |
Assignment 3: Individual assessment of a spatial data analysis project. 25 hours (an equivalent of 1000 words). (ILOs) 2 and 3 are addressed in this assessment.
| Week 9 | 20% |
Major Project Proposal: Requiring each team member to commit 10 hours (a contribution to the equivalent of 500 words) of work for the group project report, worth 5%. (ILOs) 1-4 are addressed in this assessment.
| Week 9 | 5% |
Major Project formative task – preliminary results on major project submitted early. Requiring each team member to commit 5 hours (a contribution to the equivalent of 150 words) of work for the group report, worth 2%. (ILOs) 1-4 are addressed in this assessment.
| Week 11 | 2% |
Major Project: Major spatial data analysis project requiring each team member to commit 40 hours (a contribution to the group report equivalent of 1500 words) of work for the group report, worth 35%. (ILOs) 1-4 are addressed in this assessment.
| During the examination period | 35% |
A 5-minute video presentation of the group project outcomes and the student's own contributions to the project, worth 10%. (10 Hours; a video equivalent of 500 words). (ILOs) 1-4 are addressed in this assessment.
| During the examination period | 10% |
Last updated: 4 March 2025
Dates & times
- Semester 1
Coordinator Jagannath Aryal Mode of delivery On Campus (Parkville) Contact hours Total time commitment 200 hours Teaching period 3 March 2025 to 1 June 2025 Last self-enrol date 14 March 2025 Census date 31 March 2025 Last date to withdraw without fail 9 May 2025 Assessment period ends 27 June 2025 Semester 1 contact information
Jagannath Aryal
Time commitment details
200 hours
What do these dates mean
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- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 4 March 2025
Further information
- Texts
- Subject notes
LEARNING AND TEACHING METHODS
The subject is based principally on presentations by academic lecturers. In addition each student prepares practical assignments. A computer laboratory equipped with industry-standard software will be used by students to undertake the tutorials.
INDICATIVE KEY LEARNING RESOURCES
Grekousis, G. (2020). Spatial analysis theory and practice: describe - explore - explain through GIS. Cambridge University Press.O'Sullivan, D., & Unwin, D. (2010). Geographic information analysis, second edition. Wiley.Geographic Data Science in Python by Rey, Arribas-Bel and Wolf: https://geographicdata.science/bookCAREERS / INDUSTRY LINKS
Spatial data analytics offers necessary skills to students to work in variety of disciplines such as digital infrastructure, geography, economics, social science, the environmental sciences and statistics.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Environmental Engineering Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Data Science Course Master of Digital Infrastructure Engineering Specialisation (formal) Computational Systems - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
Last updated: 4 March 2025