Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2016:Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 48 hours (Lectures: 2 hours per week; Laboratory Sessions: 2 hours per week) |
Total Time Commitment:
Successful completion of the following is required to enrol in this subject:
Study Period Commencement:
|Recommended Background Knowledge:||None|
|Non Allowed Subjects:||None|
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorDr Mohsen Kalantari Soltanieh
Dr Mohsen Kalantari Soltanieh
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 (ILO)
On completion of this subject the student is expected to:
O'Sullivan, D. and Unwin, D.J. (2002). Geographic Information Analysis. Hoboken, NJ: John Wiley & Sons
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On successful completion students should have the:
LEARNING AND TEACHING METHODS
The subject is based principally on presentations by academic lecturers. In addition each student prepares four practical assignment reports. A computer laboratory will be used by students to undertake the tutorials.
INDICATIVE KEY LEARNING RESOURCES
Major text book: O'Sullivan, D. and Unwin, D.J. (2002). Geographic Information Analysis. Hoboken, NJ: John Wiley & Sons
CAREERS / INDUSTRY LINKS
Spatial data analysis offers necessary skills to students to work in variety of disciplines such as geomatics, geography, economics, social science, the environmental sciences and statistics.
Doctor of Philosophy - Engineering |
Master of Geographic Information Technology
Master of Information Technology
Master of Information Technology
Master of Philosophy - Engineering
Master of Spatial Information Science
Energy Studies |
MIT Spatial Specialisation
Master of Engineering (Spatial)
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