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Unit of study_

ITLS6107: Applied GIS and Spatial Data Analytics

Semester 2, 2020 [Normal evening] - Camperdown/Darlington, Sydney

The world is increasingly filled with systems, devices and sensors collecting large amounts of data on a continual basis. Most of these data are associated with locations that represent everything from the movement of individuals travelling between activities to the flow of goods or transactions along a supply chain and from the location of companies to those of their current and future customers. Taking this spatial context into account transforms analyses, problem-solving and provides a powerful method of visualising the world. This is the essence of Geographic Information Systems (GIS) and this unit. This unit starts by introducing students to the 'building blocks' of GIS systems, including data structures, relational databases, spatial queries and analysis. The focus then moves on to sources of spatial data including Global Positioning System (GPS), operational systems such as smartcard ticketing and transaction data along with web-based sources highlighting both the potential and challenges associated with integrating each data source within a GIS environment. The unit is hands-on involving learning how to use the latest GIS software to analyse several problems of interest using real 'big data' sources and to communicate the results in a powerful and effective way. These include identifying potential demand for new services or infrastructure, creating a delivery and scheduling plan for a delivery firm or examining the behaviour of travellers or consumers over time and locations. This unit is aimed at students interested in the spatial impact of decision-making and on the potential for using large spatial datasets for in-depth multi-faceted analytics.

Unit details and rules

Unit code ITLS6107
Academic unit Transport and Logistics Studies
Credit points 6
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Chinh Ho,
Type Description Weight Due Length
Small test Individual quizzes
20% -
Due date: 23 Oct 2020 at 23:59
4 quizzes x 10 minutes each
Outcomes assessed: LO1 LO4 LO3 LO2
Final exam (Take-home short release) Type D final exam Final exam
Written exam
30% Formal exam period 3 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Individual Project
20% Week 07
Due date: 15 Oct 2020 at 23:59

Closing date: 22 Oct 2020
5 pages
Outcomes assessed: LO2 LO3 LO4 LO5
Presentation group assignment Group presentation
Oral presentation
10% Week 11
Due date: 12 Nov 2020 at 23:59

Closing date: 20 Nov 2020
12 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Group project
Case study
20% Week 12
Due date: 19 Nov 2020 at 23:59

Closing date: 26 Nov 2020
15 pages
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
group assignment = group assignment ?
Type D final exam = Type D final exam ?

Assessment summary

  • Quizzes: Students will complete four quizzes of 10-minute each to consolidate their understanding of key concepts and principles in spatial data analytics. The quizzes will spread across the first half of the semester. 
  • Individual project: Students will complete individually a series of spatial data analyses, using GIS software and time-series data on COVID-19 to develop basic skills of data visualisation and spatial analytics. 
  • Group project: Students, working in a group of 4 or 5, will use realworld data from a national retailer to analyse sales and assess the performance of different locations, identify potential market opportunities and determine the optimal distribution model comprising of distribution centres and vehicle fleet size, as well as a delivery schedule.
  • Group presentation: Students will record a presentation of their group project and upload to Canvas. Each group is required to attend one slot of the presentation in the last workshop(s). 
  • Final exam: The final exam is designed to comprehensively assess students’ ciritical thinking and understanding of both the principles and practice of GIS learnt throughout the course, including assignments.

Detailed information for each assessment can be found on Canvas.

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

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction to GIS 2. The big picture Lecture (1.5 hr) LO1 LO2
1. ArcGIS interface 2. Basic mapping Workshop (1.5 hr) LO1 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 LO2 LO3
1. Theme maps 2. Map design Workshop (1.5 hr) LO1 LO2 LO3
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 LO2 LO3
Week 05 1. Data visualisation: principles and practices 2. Best practice in spatial analytics Lecture (1.5 hr) LO1 LO2 LO3 LO4
Introduction to R programming language Workshop (1.5 hr) LO5
Week 06 1. Data structure 2. Tidy data Lecture (1.5 hr) LO3 LO4 LO5
Data processing and data visualisation with R Workshop (1.5 hr) LO4 LO5
Week 07 1. Introduction to network 2. GS1 and GIS Lecture (1.5 hr) LO3 LO5 LO6
1. Network analysis 2. Vehicle routing Workshop (1.5 hr) LO3 LO4 LO5 LO6
Week 08 Regression: Ordinary Least Squares (OLS) vs. Geographically Weighted Regression (GWR) Lecture (1.5 hr) LO3 LO4 LO5 LO6
1. GWR in ArcGIS 2. Bridging ArcGIS and R Workshop (1.5 hr) LO3 LO4 LO5 LO6
Week 09 1. Data collection 2. introduction to Global Navigation Satellite Systems Lecture (1.5 hr) LO1 LO2 LO3
Spatial analysis and mapping with R Workshop (1.5 hr) LO3 LO4 LO5 LO6
Week 10 1. Open Data and Open Standard 2. Database Lecture (1.5 hr) LO2 LO3 LO5
Database with R Workshop (1.5 hr) LO3 LO4 LO5
Week 11 Dealing with Big Data Lecture (1.5 hr) LO3 LO5 LO6
Parallel processing Workshop (1.5 hr) LO3 LO5 LO6
Week 12 Exam review Lecture (1.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Group presentation Workshop (1.5 hr) LO3 LO4 LO5 LO6

Attendance and class requirements

Lecture recordings: All lectures, workshops, and Q&A are recorded and will be available on Canvas for student use. 

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. Available online at’Reilly. 
    • Chapter 2, 3, 4: Basics
    • Chapter 16: Dealing with dates and time
  2. Lovelace, R., Nowosad, J., & Muenchow J. (2019) Geocomputation with R. Available online at Chapman and Hall/CRC.

    • ​​​​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. explain the underlying theory behind GIS and the challenges in its use
  • LO2. identify the features of GIS and when to apply them
  • LO3. formulate strategies for solving business problems, assess each strategy and recommend and defend a single solution
  • LO4. demonstrate the effective communication of geographic information, to business stakeholders using GIS
  • LO5. describe the processes necessary for the collection, processing and analysis of geographic data
  • LO6. apply the appropriate features of GIS tools to analyse geographic characteristics for the purpose of achieving business objectives.

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

All learning activities, including lectures, workshops, and assignments have been re-designed to facilitate online learning and teaching in response to the COVID-19 pandemic. In particular, considering students' feedback from last year, the coordinator has reduced the workloads required by the assignments, and have created supplementary e-learning resources for student use.


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.