Urban Transport MSc
Big Data, AI & Urban Analytics URBAN5125
- Academic Session: 2025-26
- School: School of Social and Political Sciences
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
Short Description
This course provides a critical introduction to debates about the role of big data in understanding urban systems, and supporting urban planning and management. It covers different forms which big data take, discusses legal and ethical issues, and reviews important examples and applications.
Timetable
Classes to run in Semester 1, delivered in 3-hour blocks, once per week, over 6 consecutive weeks.
Excluded Courses
None
Co-requisites
None
Assessment
Assessment will be through a single essay (2000 words) which gives the student scope to demonstrate their knowledge through its application to a specific urban analytics case study.
Course Aims
The aims of this course are to:
• explore critically the claims made for the big data paradigm, identifying the differences with more traditional data sources, as well as the strengths and weaknesses of these new data.
• identify the ways in which data-driven analytical approaches, including those based on AI methods shape how we understand urban problems or challenges compared with theory-driven approaches, and identify the methodological issues involved in using each.
• critically examine specific examples of urban analysis using big data and data-driven analytical approaches, reviewing strengths and weaknesses, as well as the transparency in the presentation of results.
• set out the legal and ethical issues around the use of big data and data-driven analytical methods for research or urban analytics
Intended Learning Outcomes of Course
By the end of this course, students will be able to:
■ Critically assess the claims for the big data paradigm and its merits relative to more traditional approaches to urban research and analysis.
■ Identify the different kinds of big data used in urban analytics, and provide a critical assessment of their strengths and weaknesses for a range of applications.
■ Provide a critical assessment of existing urban analyses based on big data or urban analytics methods, looking in particular at methodological rigour, validity and transparency.
■ Demonstrate a critical awareness of the legal and ethical issues raised by diverse data sources and methodological approaches, and identify appropriate responses to these.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.
Minimum requirement for award of credit for students on MSc City Planning is D3 or above.
University standard regulations apply to students on other qualifications.