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Unit outline_

PLAN9075: Urban Data and Science of Cities

Semester 1, 2023 [Normal day] - Remote

The discipline of Science of Cities examines relationships between the physical form of cities and the social, cultural, economic, technological and spatial processes that give rise to this form. As technology evolves and changes, so do the ways in which we make and think about our cities. In this era of unprecedented and fast-accelerating changes, digital technologies are reshaping the ways in which we measure, sense, conceive of, design and plan for our cities. As a result, we collect and store large amounts of data on every aspect of the urban environment, but it is as yet unclear how this data can be used to inform evidence based planning and urban management. In particular, it is unclear how these quantitative methods and data driven frameworks may be best leveraged for planning and designing just, equitable, sustainable, liveable, and affordable cities. This unit of study will introduce the principles of science of cities and the tools, methods, algorithms and techniques on big urban data that enable transformative ways of thinking about, designing and planning for a fast urbanizing world. Fundamentals of programming with big urban data will be introduced through the Python programming language (Jupyter Notebooks) and open source Geographic Information Systems (GIS) software. Emphasis will be placed on developing understanding of urban structure and fast and slow dynamics shaping this structure, and on the use of data to develop performance indicators for cities, in particular targeting the spatial and temporal measurement accessibility, affordability, segregation, displacement, social exclusion, and disadvantage. This transdisciplinary unit of study will be relevant for designers, planners, engineers, geographers, economists, physicists and data scientists interested in modelling urban systems.

Unit details and rules

Academic unit Urban and Regional Planning and Policy
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Basic mathematics and statistics; all required programming and mathematics needed for the unit will be taught from the basics

Available to study abroad and exchange students

No

Teaching staff

Coordinator Somwrita Sarkar, somwrita.sarkar@sydney.edu.au
Type Description Weight Due Length
Assignment group assignment Urban Indicator Development
Students will develop in teams of 3-4, an urban performance indicator
40% Week 13 Due Week 13
Outcomes assessed: LO1 LO2 LO3 LO4
Online task Python Programming and GIS for Urban Data Science
Students will complete Python Programming and GIS Tasks, via quizzes.
60% Weekly 12 weeks
Outcomes assessed: LO1 LO2 LO3 LO4
group assignment = group assignment ?

Assessment summary

Three assessments and online weekly submissions

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

Work of outstanding quality, demonstrating mastery of the learning outcomes
assessed. The work shows significant innovation, experimentation, critical
analysis, synthesis, insight, creativity, and/or exceptional skill.

Distinction

75 - 84

Work of excellent quality, demonstrating a sound grasp of the learning outcomes
assessed. The work shows innovation, experimentation, critical analysis,
synthesis, insight, creativity, and/or superior skill.

Credit

65 - 74

Work of good quality, demonstrating more than satisfactory achievement of the
learning outcomes assessed, or work of excellent quality for a majority of the
learning outcomes assessed.

Pass

50 - 64

Work demonstrating satisfactory achievement of the learning outcomes
assessed.

Fail

0 - 49

Work that does not demonstrate satisfactory achievement of one or more of the
learning outcomes assessed.

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:

Standard policy

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.

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 Cities and Urban Science Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 02 Size and Scale of Cities: Zipf's Law Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 03 Size and Scale of Cities: Urban Allometry Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 04 Flows: Spatial Interaction and Gravity Models Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 05 Networks Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 06 Urban Economic Structure Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 07 Cities and Inequalities: Segregation, Displacement, Exclusion Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 08 Cities and Inequalities: Polycentricity, Accessibility, Jobs-Housing Balances Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 09 Cities and Inequalities: Diversity and Specialisation Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 10 Urban indicator development: International Perspectives Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 11 Remote Sensing, Satellite Imagery, and Urban Growth Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 12 How to define city boundaries: Open questions Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4
Week 13 No Lecture or Tutorial - Final Submission Only Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4

Attendance and class requirements

Please refer to the Resolutions of the University School: http://sydney.edu.au/handbooks/architecture/rules/faculty_resolutions.shtml

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

Batty, M. (2016) The New Science of Cities, MIT Press. 

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. Map attributes of data into measurement of different aspects of cities.
  • LO2. Identify appropriate ways to represent and process complex and abstract data in order to make inferences on the spatial organization and fast and slow evolution of cities.
  • LO3. Understand traditional and new methods of data collection and the various sources of availability of data related to urban systems.
  • LO4. Apply standard algorithms and methods of analysis to data in order to understand the development of indicators for planning, design, and policy outcomes.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

GIS Tutorials have been further developed in response to students saying they would like this specific skill set developed.

More information can be found on Canvas

Site visit guidelines

There are no site visit guidelines for this unit.

Work, health and safety

There are no specific WHS requirements for this unit.

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.