Course Title: Spatial Information Science Analytics

Part A: Course Overview

Course Title: Spatial Information Science Analytics

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

GEOM1057

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016

GEOM1057

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021

GEOM2133

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016

GEOM2133

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 1 2018,
Sem 1 2019

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: 12.12.17

Course Coordinator Availability: by appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Prerequisite:

GEOM1044 SIS Principles (for Undergraduates taking GEOM1057)

Required prior study:

GEOM1159 GIS Fundamentals and GEOM1163 GIS Principles (for Postgraduates taking GEOM2133) and GEOM1033 SIS Fundamentals (for Undergraduates taking GEOM1057).


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.

 

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.)

The WAM web page link:

http://www1.rmit.edu.au/browse;ID=eyj5c0mo77631


Objectives/Learning Outcomes/Capability Development

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

1. Define the geographical concepts of distance, adjacency, interaction, and neighbourhood and show how these can be recorded using matrix representations;
2. Identify and outline 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 demonstrate how different conceptions of near, distance or neighbourhood 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.


This course contributes to the development of the following Program Learning Outcomes in BH117 Bachelor of Science (Geospatial Science) (Honours):

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


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: http://rmit.libguides.com/geospatial  


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 20%

 

Assessment Task 2: Laboratory Reports 

  • Supporting CLOs 3, 4 and 5 
  • Weighting 40%

 

Assessment Task 3: Project consultation and presentation 

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

 

Assessment Task 4: Project Submission 

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