Postgraduate Course: Data, Mobility and Infrastructure (EFIE11542)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Cities across the world are increasingly required to define policies aimed at meeting targets for a sustainable future for all (UN-Habitat 2009). This course will focus on how data can help supporting policy makers in developing a sustainable transport system centred on active travel.
Low- or non-emitting vehicular transport modes alone are known to be insufficient to reach this ambition hence the importance to encourage active travel. The course explores what active travel means and how understanding and analysing data can provide the tools to make resilient policy tools and impact to deliver this agenda.
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| Course description |
Policy makers have made commitments to decarbonise and transform our cities and there is now an expectation that such commitments are implemented in the immediate future. In this context, data is a vital asset as objective evidence to influence policy decisions. This course will focus on leveraging data to support the planning of sustainable transportation systems built around the concept of active travel.
The course will provide knowledge on sustainable transportation systems and active travel and how it relates to well-being and the environment. It will explore the use of both traditional and new forms of data to investigate human mobility and assess existing transportation infrastructure. Furthermore, it will introduce students to geographic data science approaches and open-source tools for transportation planning.
Students will work individually and in groups, to investigate how data can underpin and support decision making for sustainable cities. They will access a wide range of digital learning environments, such as livestreamed sessions, interactive coding sessions, and other tools for sharing materials and discuss with peers.
Edinburgh Futures Institute (EFI) - Hybrid Course Delivery Information:
The Edinburgh Futures Institute delivers many of its courses in hybrid mode. This means that you may have some online students joining sessions for this course. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities.
Students should be aware that:
- Classrooms used in this course will have additional technology in place: in some cases, students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- All presentations, and whole class discussions will be recorded (see the Lecture Recording and Virtual Classroom policies for more details).
You will need access to a personal computing device for this course. Most activities will take place in a web browser, unless otherwise stated. We recommend using a device with a screen, a physical keyboard, and internet access.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2026/27, Available to all students (SV1)
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Quota: 30 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 4,
Seminar/Tutorial Hours 6,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The course will be assessed by means of the following components:
1) Reproducible Notebook (30%)
A fully reproducible notebook documenting the complete data analysis workflow.
Learning Outcomes Assessed by Component: 1, 2
2) Written Report (70%)
A 2,500-word report presenting and critically discussing the data analysis findings.
Learning Outcomes Assessed by Component: 1, 2, 3, 4 |
| Feedback |
Feedback on any formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The Course Organiser will decide which format is most appropriate in relation to the nature of the assessment.
Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.
Feedback on the summative assessment(s) w/ill be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
Formative Feedback Opportunity:
Formative feedback is ongoing feedback which monitors learning and is intended to improve performance in the same course, in future courses, and also beyond study.
Proposals of the data analysis work will be presented in class. Verbal comments from educator form the basis of the formative feedback. This will feed-forward the analysis work.
Educator will provide written formative feedback on a short abstract of the essay and on the final essay. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Develop and interpret metrics to evaluate active travel infrastructure and sustainable mobility systems.
- Extract, investigate and report on case study data using advanced geographic data science methodologies.
- Demonstrate the ability to think critically about data usage in relation to sustainable transportation.
- Acquire the ability to independently develop data analysis and visualisation and effectively communicate findings.
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Reading List
Essential Reading:
Paul A. Chatterton, Unlocking Sustainable Cities, 2019, Chapter 1 The car-free city
Carolina Criado Perez, Invisible women, 2019, Chapter 1 Can snow-clearing be sexist?
Pereira, R., Schwanen, T. & Banister, D. (2017). Distributive justice and equity in transportation. Transport Reviews 37, 170-191.
Banister (2008). The sustainable mobility paradigm.
Lovelace R. Geocomputation with R Chapter 13 accessible at https://geocompr.robinlovelace.net/transport.html
OECD 2005 Handbook on Constructing Composite Indicators. Chapter 1, accessible at https://www.oecd.org/sdd/42495745.pdf
Alexiou, A., Singleton, A., & Longley, P. A. (2016). A classification of multidimensional open data for urban morphology. Built Environment, 42(3), 382-395.
Shashank, A., & Schuurman, N. (2019). Unpacking walkability indices and their inherent assumptions. Health & place, 55, 145-154.
Arellana, J., Saltarín, M., Larrañaga, A. M., González, V. I., & Henao, C. A. (2020). Developing an urban bikeability index for different types of cyclists as a tool to prioritise bicycle infrastructure investments. Transportation Research Part A: Policy and Practice, 139, 310-334.
Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., & Woodcock, J. (2017). The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal of transport and land use, 10(1), 505-528.
Recommended Reading:
Michael Szell Keynote Video accessible at ATI-website (this will be available soon) - https://nerds.itu.dk/event/keynote-talk-michael-szell-at-alan-turing-institute/ |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | PG,EFI,Level 11,Sustainability,Sustainable Lands and Cities,Data,Mobility,Infrastructure |
Contacts
| Course organiser | Dr Alessia Calafiore
Tel:
Email: acalafio@ed.ac.uk |
Course secretary | Miss Yasmine Lewis
Tel:
Email: yasmine.lewis@ed.ac.uk |
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