Handbook home
Spatial Data Analytics and Geoprocessing (ERTH90060)
Graduate courseworkPoints: 6.25Dual-Delivery (Parkville)
From 2023 most subjects will be taught on campus only with flexible options limited to a select number of postgraduate programs and individual subjects.
To learn more, visit COVID-19 course and subject delivery.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
October
Overview
Availability | October - Dual-Delivery |
---|---|
Fees | Look up fees |
This subject introduces the fundamentals of spatial data analytics and geoprocessing. By using hands-on exercises with real-life geological datasets, the students learn how to handle data in relational databases, query data with simple SQL statements, cleaning, formatting and exporting geospatial datasets and geo-processing the data in GIS software packages. At the start the subject will focus on looking at the basics of database structures, data analytics and data querying. In the second part of the course the students will create GIS projects, plot spatial data, start analysing and geoprocessing geospatial data, creating interpolated heatmaps, rasterizing point clouds and combining and standardizing disperse datasets. We will also practice data extraction, such as how-to geo-reference a map in Google Earth and in GIS, extract data locations from a geo-referenced image and how to create a final GIS project, including legend and map. Finally, the course concludes with bringing all the data together and creating a final GIS project visualizing all pre-analysed data.
Intended learning outcomes
At the completion of this subject, students should be able to:
- Critically examine and assess the feasibility of creating big data projects;
- Have an advanced understanding of how to analyse data in GIS and create own technical advanced projects;
- Gain unique skills in geoprocessing of spatial data and interpretation of the outcomes; and
- Gain insights into the potential of machine learning and artificial intelligence use in exploration workflows.
Generic skills
Upon completion of this subject, students should be able to:
- Handle large datasets in digital format;
- Exercise critical judgement;
- Undertake rigorous and independent thinking;
- Adopt a problem-solving approach to new and unfamiliar tasks;
- Develop high-level written report and/or oral presentation skills;
- Interrogate, synthesise and interpret the published literature; and
- Work as part of a team.
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
None
Corequisites
None
Non-allowed subjects
None
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: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
Completion of a final data and GIS project based on provided dataset and individual tasks. The final mark will be assessed by means of a 15 – 20 min presentation by each student. | Due one week after the classroom teaching has concluded. | 50% |
Submission of a technical report (~2000 words) describing steps of data processing, GIS interpretations and final summary
| Due one week after the classroom teaching has concluded. | 50% |
Last updated: 31 January 2024
Dates & times
- October
Principal coordinator Ralf Haese Coordinator Fabian Kohlmann Mode of delivery Dual-Delivery (Parkville) Contact hours Total of 40 contact hours over 5 days: x4hrs lectures & x4hrs practicals per day Total time commitment 75 hours Teaching period 3 October 2022 to 7 October 2022 Last self-enrol date 4 October 2022 Census date 4 October 2022 Last date to withdraw without fail 6 October 2022 Assessment period ends 7 October 2022 October contact information
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Links to additional information
This subject is taught through the Victorian Institute of Earth and Planetary Sciences: https://vieps.earthsci.unimelb.edu.au/.
Last updated: 31 January 2024