Course Title: Spatial Information Science Analytics

Part A: Course Overview

Course Title: Spatial Information Science Analytics

Credit Points: 12.00


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

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

Course Coordinator: Dr Gang-Jun Liu

Course Coordinator Phone: +61 3 9925 2425

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


Pre-requisite Courses and Assumed Knowledge and Capabilities

GEOM1159 GIS Fundamentals and GEOM1163 GIS Principles (for Postgraduates taking GEOM2133) and GEOM1033 SIS Fundamentals and GEOM1044 SIS Principles (for Undergraduates taking GEOM1057) 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;
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 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 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 for describing spatial patterns, quantifying spatial autocorrelation, 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:


• Frequent participation in lectures, where syllabus material will be presented and explained and key concepts and procedures investigated 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.
 

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.
A library subject guide is available at: http://rmit.libguides.com/geospatial


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 written class tests during the semester. During the semester you will also be required to give class presentations, on as 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.


Early assessment task 1:  Class Test 1
Weighting 25%
This assessment task supports CLOs 1, 2 and 3

Assessment Task 2:  Class Test 2
Weighting 25%
This assessment task supports CLOs 4 and 5

Assessment Task 3: Laboratory Reports 1-4
Weighting 20%
This assessment task supports CLOs 3, 4 and 5

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