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Unit of study_

ITLS6111: Spatial Analytics

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).


Academic unit Transport and Logistics Studies
Unit code ITLS6111
Unit name Spatial Analytics
Session, year
Semester 2, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

ITLS6107 or TPTM6180
Assumed knowledge

Basic knowledge of Excel is assumed.

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Chinh Ho,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final Exam
Written exam
30% Formal exam period 3 hours
Outcomes assessed: LO2 LO3
Small test Online quizzes
15% Multiple weeks 4 quizzes, 10 minutes each
Outcomes assessed: LO2 LO3
Assignment Individual report
25% Week 07 5 pages
Outcomes assessed: LO1 LO4 LO3 LO2
Presentation Group Project
30% Week 12 10 minutes presentation, 15 pages report
Outcomes assessed: LO1 LO2 LO3 LO4
Type D final exam = Type D final exam ?

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


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. 


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.


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.


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. 


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction to the unit and GIS 2. The big picture Lecture (1.5 hr) LO2
1. ArcGIS interface 2. Basic mapping Workshop (1.5 hr) LO2
Week 02 1. Spatial data 2. Spatial relationship 3. Introduction to SQL Lecture (1.5 hr) LO1 LO2
1. ArcGIS query 2. Joining/merging data using ArcGIS Workshop (1.5 hr) LO1 LO2
Week 03 Data visualisation: general guidelines Lecture (1.5 hr) LO1 LO4
1. Theme maps 2. Map design Workshop (1.5 hr) LO1 LO4
Week 04 1. Coordinate reference systems 2. Spatial analysis Lecture (1.5 hr) LO1 LO2 LO3
1. Spatial analytics with ArcGIS Workshop (1.5 hr) LO1 LO3
Week 05 1. Data visualisation: principles and practices 2. Best practice in spatial analytics Lecture (1.5 hr) LO3 LO4
Introduction to R programming language Workshop (1.5 hr) LO1
Week 06 1. Data structure 2. Tidy data Lecture (1.5 hr) LO1 LO2
Data processing and data visualisation with R Workshop (1.5 hr) LO1 LO4
Week 07 Introduction to network Lecture (1.5 hr) LO1 LO2
1. Network analysis 2. Vehicle routing Workshop (1.5 hr) LO1 LO3
Week 08 Regression: Ordinary Least Squares (OLS) vs. Geographically Weighted Regression (GWR) Lecture (1.5 hr) LO1 LO3
1. GWR in ArcGIS 2. Bridging ArcGIS and R Workshop (1.5 hr) LO1 LO2 LO3
Week 09 Collecting and harvesting data Lecture (1.5 hr) LO1 LO2
Spatial analysis and mapping with R Workshop (1.5 hr) LO1 LO3 LO4
Week 10 1. Open Data and Open Standard 2. Database Lecture (1.5 hr) LO1 LO2
Database with R Workshop (1.5 hr) LO1 LO2 LO3
Week 11 Dealing with Big Data Lecture (1.5 hr) LO1 LO3
Parallel processing Workshop (1.5 hr) LO1 LO2 LO3
Week 12 Exam review Lecture (1.5 hr) LO1 LO2 LO3 LO4
Group presentation Workshop (1.5 hr) LO2 LO3 LO4

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.    

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

  • 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

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

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.

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
This is the first time this unit has been offered. Predecessor unit is ITLS6107 which is replaced by this unit from 2021 onward.


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

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