Big Data and Urban Analytics URBAN5125
- Academic Session: 2019-20
- School: School of Social and Political Sciences
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
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.
Classes to run in Semester 1, delivered in 3-hour blocks, once per week, over 5 consecutive weeks.
Requirements of Entry
Mandatory Entry Requirements:
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.
The aims of this course are to:
■ explore critically the claims made for the big data paradigm, identifying the differences with more traditional analytical approaches, as well as the strengths and weaknesses of these new techniques.
■ identify the range of data types available for understanding urban problems or challenges, and identify the methodological issues involved in using each.
■ examine specific examples of urban analysis using big data, reviewing strengths and weaknesses of data and analytical approaches employed, as well as the transparency in the presentation of results.
■ set out the legal and ethical issues around re-use of data for research or urban analytics.
■ examine the factors which influence whether urban analyses are likely to be used by planners and policy makers in practice.
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 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, 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 identify appropriate responses to these.
■ Identify factors which might prevent a given piece of urban analysis being used in practice, and devise a strategy for maximising chances of uptake.
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.