Outline

Overview

This unit of study gives an overview of basic spatial data models, and enables students to understand the use of data from a variety of sources within a geographical information system (GIS). The analysis of spatial data, and its manipulation to address questions appropriate to planning or locational applications, will be addressed, as will the development of thematic maps from diverse data layers.

Unit details and rules

Academic unit	Geosciences Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	This unit assumes a sound understanding of scientific principles, HSC level mathematics and understanding of basic statistics
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Kevin Davies, kevin.davies@sydney.edu.au
Lecturer(s)	Kevin Davies, kevin.davies@sydney.edu.au
Tutor(s)	Md. Yousuf Gazi, mdyousuf.gazi@sydney.edu.au

Assessment

The census date for this unit availability is 8 March 2024

Details
Criteria

Type	Description	Weight	Due	Length
Tutorial quiz	Datums and Projections Quiz Multiple choice/short answer quiz in map projections and datums	20%	Week 02	40 minutes
Outcomes assessed:
Tutorial quiz	Content and Concepts Quiz Multiple choice/short answer quiz on concepts and tutorial material	20%	Week 04	40 minutes
Outcomes assessed:
Assignment	GIS Logbook Logbook of in-class GIS laboratory work.	20%	Week 06	Completed over 6 week teaching period
Outcomes assessed:
Assignment	GIS Project Major GIS Project	40%	Week 08	Completed over 6 week teaching period
Outcomes assessed:
= hurdle task ?

Assessment summary

The objective of this project is to develop alternative multiple use plans for a area using Multi-Criteria Analysis in GIS.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Outstanding or exceptional work clearly demonstrating originality and a flair for the subject. Demonstration of initiative and complex understanding in applying spatial analysis and modelling principles. Evidence of independent and extensive reading and use of high-quality peer-reviewed information to support written arguments when required. Arguments clearly articulated, project work is well written, shows clarity of thought and excellent expression of ideas. Very high standard of presentation and interpretation of analytical results.
Distinction	75 - 84	Demonstration of high-level aptitude for the subject area and strong understanding of the material. Evidence of independent thought and critical thinking in applying spatial analysis and modelling principles. Project work is well written, shows clarity of thought and expression of ideas. Very high standard of presentation and interpretation of analytical results.
Credit	65 - 74	Evidence of sound knowledge and understanding of principles and concepts Some evidence of independent thought and critical thinking in applying spatial analysis and modelling principles. Thorough understanding of material presented in practical sessions, lectures and readings. Good written expression and clear interpretation and presentation of analytical results.
Pass	50 - 64	Demonstrate a satisfactory knowledge and understanding of principles and concepts Ability to apply spatial analysis and modelling principles in addressing problems There may be significant analytical errors or omissions Minimal interpretation of analytical results and ideas Meaning apparent but writing style not always fluent or well organised
Fail	0 - 49	Fails to satisfy minimum requirements Demonstrates unsatisfactory or inadequate level of understanding of subject area Demonstrates minimal relevant knowledge or understanding of principles and concepts Limited application of spatial analysis and modelling principles in addressing problems Poor written expression, with numerous typographical and grammatical errors

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to GIS Spatial Analysis, Datums and Map Projections	Lecture and tutorial (6 hr)
Week 02	Spatial Analysis - Vectors	Lecture and tutorial (6 hr)
Week 03	Spatial Analysis - Raster	Lecture and tutorial (6 hr)
Week 04	Spatial Statistics and Interpolation	Lecture and tutorial (6 hr)
Week 05	Cost-Distance Analysis and Network Analysis	Lecture and tutorial (6 hr)
Week 07	Hydrological Modelling and Flood Risk Analysis	Lecture and tutorial (6 hr)

Attendance and class requirements

Attendance at all classes is compulsory.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. understand the conceptual and theoretical basis of spatial analysis and Geographical Information Science (GIS)
LO2. apply GIS as a mechanism for addressing environmental and social problems
LO3. recognise the role of spatial analysis in understanding pattern in social and environmental landscapes
LO4. understand the role of different spatial modelling approaches and relate these models to particular research questions
LO5. undertake geographical enquiry and develop spatial reasoning skills
LO6. critique GIS methods and identify challenges associated with data uncertainty
LO7. reflect on how outcomes of spatial models can inform decision making.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Updates and minor changes have been made since this unit was last offered.

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

GEOG5001: Geographic Information Science A

Semester 1a, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect