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Spatial Information Programming (GEOM90042)
Graduate courseworkPoints: 12.5Online
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Overview
Availability | Semester 1 - Online |
---|---|
Fees | Look up fees |
AIMS
Much of the world’s data relate to processes and objects situated in space. This spatial dimension of the data requires special representation and analytical approaches. Therefore, application problems such as the analysis, monitoring and simulation of Smart cities and smart environments cannot be handled by standard programming approaches and require specialist knowledge.
Using case studies in the domains of smart environments and smart cities, this subject will enable students to learn the necessary computational thinking approaches and acquire technical software development skills to address specific spatial information problems enabling them to effectively address Spatial Data Science problems.
The subject will focus on the application of state-of-the art programming techniques and applications of spatial analytics to solve a series of spatial data science use cases, in particular in the urban informatics domain. The course projects will also introduce the principal aspects of software development life cycle relevant for a data scientist.
This subject assumes students are familiar with elementary spatial information data and the varied ways these are used by various stakeholders. Fundamental understanding of a programming language is assumed, with the first few weeks of the semester providing an ability to acquire these skills (using Python).
Students who successfully complete this subject will have a distinct competitive advantage in the smart environment, smart cities, and urban analytics practices, with the ability to support consultancy work requiring computational data handling, analysis, and the development of software tools for spatial analysts beyond the traditional spatial information industry.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to
- Use an object oriented programming language to design, implement and test spatial programming solutions
- Design and generate an algorithmic solution to a specified spatial information problem
- Be able to understand, critically evaluate and explain software code developed by others
- Document and maintain complex, re-useable software programs.
- Use varied spatial and non-spatial data, including dynamically changing web content in these solutions
Generic skills
The following generic skills will be strengthened as a result of this course of study:
- Ability to apply knowledge of science and engineering fundamentals
- Ability to undertake problem identification, formulation, and solution
- Ability to communicate effectively, with the engineering 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
Eligibility and requirements
Prerequisites
None
Corequisites
None
Non-allowed subjects
GEOM30010 Programming Geomatics Applications
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 |
---|---|---|
Short, introductory assignment introducing GIS and spatial information programming
| Week | 0% |
Two individual written programs, solving realistic use-cases of application of spatial information programming and spatial data science, and the relevant documentation to support the program (25 hours each, 20% each, 40% in total). ILOs 1 to 5.
| Mid semester | 50% |
A team assignment (teams of 2-3) with a collaborative component addressing a realistic use-case of replicable analysis in spatial data science and/or of a re-useable extension of a GIS environment. ILOs 1 to 5.
| End of semester | 50% |
Last updated: 31 January 2024
Dates & times
- Semester 1 - Online
Principal coordinator Martin Tomko Mode of delivery Online Contact hours 48 hours (Lectures: 2 hours per week; Tutorials/Practicals: 2 hours per week - tutorials and practicals in computer lab) Total time commitment 200 hours Teaching period 1 March 2021 to 30 May 2021 Last self-enrol date 12 March 2021 Census date 31 March 2021 Last date to withdraw without fail 7 May 2021 Assessment period ends 25 June 2021 Semester 1 contact information
Time commitment details
200 hours
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
Recommended texts and other resources
- Jennings, N., 2011. A Python Primer for ArcGIS. CreateSpace Independent Publishing Platform
- Cogliati, J. 2005, Non-Programmer's Tutorial for Python
- Downey, A.B., Elkner, J. and Meyers, C., 2008. Think Python: How to Think Like a Computer Scientist. O’Reilly Media
- Mark Lutz: Learning Python (3rd ed.), O'Reilly Media
- van Rossum, G. and Drake, F.L. Jr. (Editor), 2003. The Python Language Reference Manual, (Version 3.2) Network Theory Ltd
- Beazley, D.M., 2006. Python Essential Reference (4th ed.), Addison-Wesley
- Subject notes
LEARNING AND TEACHING METHODS
There will be lectures covering the addressed topics. Additionally, there will be computer labs, which will allow students to apply previously learnt concepts, methods and approaches. Students will also have time to work on the practical assignments. Labs start in week 1 and then run until the end of the semester.
INDICATIVE KEY LEARNING RESOURCES
- Jennings, N., 2011. A Python Primer for ArcGIS, CreateSpace Independent Publishing Platform
- Cogliati, J., 2005, Non-Programmer's Tutorial for Python
- Downey, A.B., Elkner, J. and Meyers, C., 2008. Think Python: How to Think Like a Computer Scientist. O’Reilly Media
- Mark Lutz: Learning Python (3rd ed.). O'Reilly Media
- van Rossum, G. and Drake, F.L. Jr.(Editor), 2003. The Python Language Reference Manual, (Version 3.2) Network Theory Ltd
- Beazley, D.M., 2006. Python Essential Reference (4th ed.), Addison-Wesley
CAREERS / INDUSTRY LINKS
Spatial information services have grown into a major sector. Being able to combine a deep understanding of the fundamentals of spatial information with the ability to develop custom-made tools and analysis methods is a significant advantage in many areas of the spatial industry. Thus, successfully participating in this subject increases students’ attractiveness for employers and broadens their career opportunities.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Doctor of Philosophy - Engineering Course Ph.D.- Engineering Course Master of Philosophy - Engineering Specialisation (formal) Spatial - 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.
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
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 31 January 2024