Course Title: GIS Analytics

Part A: Course Overview

Course Title: GIS Analytics

Credit Points: 12.00

Important Information:

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT:

Please read the Student website for additional requirements of in-person attendance: 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


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

Course Coordinator: Dr Gang-Jun Liu

Course Coordinator Phone: +61 3 9925 2425

Course Coordinator Email:

Course Coordinator Location: 12.11.17

Course Coordinator Availability: by appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

GEOM1059 GIS Fundamentals and GEOM1163 GIS Principles or equivalent courses covering:

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

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: the structure of geographical data and issues of geographical data integration; statistical measures and methods for spatial pattern and regression 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: 

  • Studying course materials, where syllabus material including key concepts and procedures will be presented, explained, investigated, and illustrated with examples; 
  • conducting lab-based practical / project works, 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; 
  • participating group-lecture consultation / feedback sessions, group discussions, and class presentation; 
  • timely completing written assignments (lab and project reports, class practical exercises) consisting of problem-solving tasks requiring integrated understanding and application of the subject matter.  

Overview of Learning Resources

You will be able to access course information and learning materials (including lists of relevant reference texts and useful internet sites) through electronic distribution on Canvas. You will also be able to use supported GIS software (including ArcGIS) and computer laboratories for practical tutorials and projects and written assignments. 

A library subject guide is available at:  

Overview of Assessment

This course has no hurdle requirements.

The assessment for this course comprises written submissions to set tasks, including a portfolio summarising an approved practical project and timed written class practical exercises, completed on time. During the semester you will also be required to give a class presentation on the approved practical project and act as a peer assessor of other students. The written assignments and the class presentation will be used for providing formative feedback on your progress in the course during the semester. 

Assessment Task 1: Class Practical Exercises 

  • Supporting CLOs 1, 2, 3, 4 and 5 
  • Weighting 30% 

Assessment Task 2: Laboratory Reports 1-4 

  • Supporting CLOs 3, 4 and 5 
  • Weighting 30% 

Assessment Task 3: Project Presentation 

  • Supporting CLOs 3, 4 and 5 
  • Weighting 10% 

Assessment Task 4: Project Consultation and Submission 

  • Supporting CLOs 1, 2, 3, 4 and 5 
  • Weighting 30%