Course Title: GIS Analytics

Part A: Course Overview

Course Title: GIS Analytics

Credit Points: 12.00

Important Information:

 

 


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

GEOM2152

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2017,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022,
Sem 1 2023

Course Coordinator: Dr Gang-Jun Liu

Course Coordinator Phone: +61 3 9925 2425

Course Coordinator Email: gang-jun.liu@rmit.edu.au

Course Coordinator Location: 12.11.17

Course Coordinator Availability: by appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

You should have satisfactorily completed the following courses before you commence this course.

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.


Course Description

This course extends your understanding of geographic information science and focuses on quantitative methods of spatial pattern analytics applicable to different types of geographical data (points, lines, areas, and surfaces). It emphasises spatial statistical and numerical techniques for describing, analysing, and comparing spatial patterns so that spatial relationships among relevant geographical phenomena can be characterised, modelled, predicted, or optimised. Topics covered in this course include statistical measures and GIS-based methods for spatial interpolation, spatial pattern analysis, and regression analysis using spatial data.  


Objectives/Learning Outcomes/Capability Development

This course contributes to the development of the following Program Learning Outcomes for MC265 Master of Geospatial Science:  

1.2 Demonstrate in-depth understanding of the spatial models and mathematical methods used in contemporary practice.  

1.3 Understand specialist bodies of knowledge in geospatial science.  

2.1 Apply standard and advanced techniques to solve a range of measurement and data management problems.  

2.3 Be proficient in the recording, storage, management and reporting of spatial information.  

3.2 Interpret and critically analyse results and make informed judgments on the appropriateness of solutions.  

3.3 Apply critical and analytical skills in a scientific and professional manner.  

4.1 Communicate effectively by means of oral, written, and graphical presentations to peers and a wider audience.  

6.2 Work with others and contribute in a constructive manner to group and team activities. 


On successful completion of this course, you should be able to:  

  1. Critically review the geographical concepts of distance, adjacency, interaction, and neighbourhood, show how these can be recorded using matrix representations, and demonstrate these concepts in the context of spatial data analytics.
  2. Identify and critically assess problems in the statistical analysis of spatial data associated with spatial autocorrelation, modifiable areal units, scale, and non-uniformity of geographical space.
  3. Select and apply suitable statistical /quantitative / numerical measures/ methods and GIS-based operations for completing set tasks of describing spatial patterns, quantifying spatial relationships, and identifying spatial clusters.
  4. Demonstrate application of geographical information science principles that underpin GIS-based spatial visualisation and analytical techniques for describing spatial patterns, identifying spatial clusters, and quantifying spatial relationships using spatial data.
  5. Demonstrate improved capability in design and implement suitable GIS-based spatial data analytical and visualisation techniques and workflows for solving specific real-work geographical problems.  


Overview of Learning Activities

The learning activities you will be involved in include:  

  • Participating the timetabled learning activities, such as listening to pre recorded lectures, attending lectorials,  tutorials, lab-based practicals, and class presentations, as well as Canvas-based course discussions.
  • Studying course materials and clarifying any course related questions in a timely manner.
  • Completing and submitting set assignment tasks according to published deadlines and rubrics. 

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Assessment Tasks

Assessment Task 1: Project Proposal
Weighting 10% 
This assessment task supports CLOs 2 & 3

Assessment Task 2: Lab Practical Reports (3)
Weighting 30% 
This assessment task supports CLOs 3 & 4

Assessment Task 3: Final Timed Tests (3)
Weighting 30% 
This assessment task supports CLOs 1, 3 & 4 

Assessment Task 4: Project Consultation and Submissions (Project Report, Project Database, Project Journal)
Weighting 30%
This assessment task supports CLOs 4 & 5

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.