Outline

Overview

Enterprises can access increasing volumes of spatial data (associated with time and space) drawn from a variety of sources including the internet of things, sensors, mobile phone locations and other diverse and unlinked data sets. Managing these data to create useful management insights is a demanding task, and spatial data analysis presents a unique set of challenges and opportunities. Effective management and analysis of spatial data provides strategic value for organisations, across logistics, transport, marketing and other business functions, allowing enterprises to manage strategic challenges in sustainability and resilience. This unit uses real-world data and problem-based learning to develop hands-on experience with managing, processing and modelling spatial data and ultimately drawing insights for business decisions linked to both distribution and supply chain interactions. Students develop highly marketable skills in spatial data analytics that are transferable across a broad range of industries and sectors. These skills include the ability to generate a range of outputs, including decision support systems, maps and visualisations that effectively communicate complex information to support strategic, tactical and operational decision making. This unit utilises a widely-used spatial software package and introduces Geographic Information Systems (GIS), spatial databases and structured query language (SQL).

Unit details and rules

Academic unit	Transport and Logistics Studies
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ITLS6107 or TPTM6180
Assumed knowledge ?	Basic knowledge of Excel is assumed
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Stephen Greaves, stephen.greaves@sydney.edu.au
Demonstrator(s)	Aitan Medeiros Militao, aitan.militao@sydney.edu.au
Demonstrator(s)	Ashikur Rahman, ashikur.rahman@sydney.edu.au
Lecturer(s)	Stephen Greaves, stephen.greaves@sydney.edu.au
Lecturer(s)	Chinh Ho, quoc.ho@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam Written exam	30%	Formal exam period	2 hours
Outcomes assessed:
Small test	Online quizzes n/a	15%	Multiple weeks	4 quizzes, 10 minutes each
Outcomes assessed:
Assignment	Individual report n/a	25%	Week 06 Due date: 05 Sep 2023 at 23:59 Closing date: 15 Sep 2023	4 pages (Title page and 3 maps)
Outcomes assessed:
Assignment	Group Project n/a	30%	Week 12 Due date: 27 Oct 2023 at 23:59	10 minutes presentation, 15 pages report
Outcomes assessed:
= group assignment ?

Assessment summary

Online quizzes: MCQ on-line/in-class quizzes. Students can do a weekly quiz at the time of their choice during that week.

Individual report: Spatial analytics and visual design, submitted work is in pptx format, each page containing one map that communicates the results of the data analysis.

Group project: Students work in groups of 4 or 5 (max) to solve a real-world spatial logisic problem to choose the best locations for warehouse, retailing shops, and fleet size.

Final exam: A 2-hour comprehensive examination on theories and application covered in the unit.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction to the unit and GIS 2. The big picture	Lecture (1.5 hr)
Week 01	1. ArcGIS interface 2. Basic mapping	Workshop (1.5 hr)
Week 02	1. Spatial data 2. Spatial relationship 3. Introduction to SQL	Lecture (1.5 hr)
Week 02	1. ArcGIS query 2. Joining/merging data using ArcGIS	Workshop (1.5 hr)
Week 03	Data visualisation: general guidelines	Lecture (1.5 hr)
Week 03	1. Theme maps 2. Map design	Workshop (1.5 hr)
Week 04	1. Coordinate reference systems 2. Spatial analysis	Lecture (1.5 hr)
Week 04	1. Spatial analytics with ArcGIS	Workshop (1.5 hr)
Week 05	1. Data visualisation: principles and practices 2. Best practice in spatial analytics	Lecture (1.5 hr)
Week 05	Introduction to R programming language	Workshop (1.5 hr)
Week 06	1. Data structure 2. Tidy data	Lecture (1.5 hr)
Week 06	Data processing and data visualisation with R	Workshop (1.5 hr)
Week 07	Introduction to network	Lecture (1.5 hr)
Week 07	1. Network analysis 2. Vehicle routing	Workshop (1.5 hr)
Week 08	Regression: Ordinary Least Squares (OLS) vs. Geographically Weighted Regression (GWR)	Lecture (1.5 hr)
Week 08	1. GWR in ArcGIS 2. Bridging ArcGIS and R	Workshop (1.5 hr)
Week 09	Collecting and harvesting data	Lecture (1.5 hr)
Week 09	Spatial analysis and mapping with R	Workshop (1.5 hr)
Week 10	1. Open Data and Open Standard 2. Database	Lecture (1.5 hr)
Week 10	Database with R	Workshop (1.5 hr)
Week 11	Dealing with Big Data	Lecture (1.5 hr)
Week 11	Parallel processing	Workshop (1.5 hr)
Week 12	Exam review	Lecture (1.5 hr)
Week 12	Collecting and Harvesting data	Workshop (1.5 hr)
Week 13	Group work presentation	Workshop (3 hr)

Attendance and class requirements

No attendance is required. However, it is highly recommended that students stay on top of each week learning activities because lectures are built on top of each other, so are assignments.

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.

Required readings

[1] Grolemund G., & Wickham H. (2017) R for Data Science. O’Reilly. Available online at https://r4ds.had.co.nz/workflow-basics.html.

Chapter 2, 3, 4: Basics
Chapter 16: Dealing with dates and time

[2] Lovelace, R., Nowosad, J., & Muenchow J. (2019) Geocomputation with R. Chapman and Hall/CRC. Available online at https://geocompr.robinlovelace.net/index.html.

Chapter 2: Geographic data in R
Chapter 8: Making maps with R

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. Develop appropriate methodologies to analyse spatial data to effectively address real world problems
LO2. Explain the key concepts and process of spatial analytics as well as the value of spatial analytics to enterprises and other organisations
LO3. Evaluate alternative approaches to spatial data analysis and recommending appropriate solutions to business problems
LO4. Communicate persuasively using maps and visualisations to support organisational 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.

Last year when this unit was first offered, it received overwhelming positive comments on the quality and relevance of the lectures, assignments, and the way in which the unit is structured to support student learning. So this year, we will keep most of the learning activities but we will have more time to interact and engage with peers via workshops.

Additional information

Business School Policies

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

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ITLS6111: Spatial Analytics

Semester 2, 2023 [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

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect