Course Title: GIS Analytics

Part A: Course Overview

Course Title: GIS Analytics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


Sem 1 2017,
Sem 1 2019,
Sem 1 2020

Course Coordinator: Dr Gang-Jun Liu

Course Coordinator Phone: +61 3 9925 2425

Course Coordinator Email:

Course Coordinator Location: 14.07.05

Course Coordinator Availability: by appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities


You are required to have successfully completed the course GEOM 1159 GIS Fundamentals and GEOM1163 GIS Principles or equivalent courses covering:

  1. the key components and functions of a geographical information system (GIS);
  2. understanding of spatial data models and databases and coordinate systems;
  3. ability to perform basic GIS operations including spatial and attribute data input, editing, and querying; and
  4. ability to produce cartographically sound digital maps using a GIS;
  5. knowledge of the principles of spatial data analysis using GIS;
  6. ability to select and apply suitable GIS operations for vector and raster data;
  7. ability to design and implement suitable GIS-based network analysis and suitability analysis.

Course Description

This course extends your understanding of geographic information sciences, focuses on quantitative methods of spatial pattern analytics applicable to different types of geographical data (points, lines, areas, and surfaces), and 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: the structure of geographical data and issues of geographical data integration; statistical measures and methods for spatial pattern and relationship analysis; spatial interpolation; and surface analysis.

Objectives/Learning Outcomes/Capability Development


This course contributes to the development of the following Program Learning Outcomes in 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 will be able to:

  1. Outline the geographical concepts of distance, adjacency, interaction, and neighbourhood, and demonstrate an advanced critical understanding of the role of these concepts;
  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 for describing spatial patterns, quantifying spatial relationships, and identifying spatial clusters;
  4. Outline the concept of spatial interpolation as spatial prediction or estimation based on point samples and the importance of first law of geography in interpolation, and critical assess the different conceptions of near, distance or neighbourhood, identifying how they result in different interpolation methods that produce different field representations with the same set of point samples;
  5. Design and implement suitable GIS-based spatial data analysis procedures for set tasks of surface analysis.

Overview of Learning Activities

 The learning activities you will be involved in are:

  • Frequent participation in lectures, where syllabus material will be presented and explained, and key concepts and procedures will be explained and illustrated with examples;
  • Active engagement in laboratory sessions, where practical tutorials and projects will be demonstrated and discussed, and feedback on your progress will be provided, guiding you to develop competencies in applying GIS operations critically and solving practical problems creatively;
  • Timely completion of written assignments consisting of problem solving tasks requiring integrated understanding of the subject matter.

Total study hours:

Teacher Guided Hours: 48 per semester

Learner Directed Hours: 48 per semester

Overview of Learning Resources

You will be able to access course information and learning materials through electronic distribution and will be provided with copies of additional materials in class. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided. You will also be able to use supported GIS software and computer laboratories for practical tutorials and projects and written assignments.

Overview of Assessment

The assessment for this course comprises written submissions to set tasks, a portfolio summarising assigned practical projects completed during the semester, and a written class test during the semester. During the semester you will also be required to give a class presentation, an assigned essay / project topics and act as a peer assessor of other students. Written assignments and the presentations will be used to provide feedback on your progress in the course during the semester.

This course has no hurdle requirements.

Assessment Task 1: Class Test
Weighting 20%
This assessment task supports CLOs 1, 2, 3, 4 and 5

Assessment Task 2: Laboratory Reports 1-4
Weighting 40%
This assessment task supports CLOs 3, 4 and 5

Assessment Task 3: Project presentation
Weighting 10%
This assessment task supports CLOs 3, 4 and 5

Assessment Task 4: Project Submission
Weighting 30% 
This assessment supports CLOs 1, 2, 3, 4 and 5