2024/25 Taught Postgraduate Module Catalogue

GEOG5404M Analytics for Urban Policy

30 creditsClass Size: 50

Module manager: Ed Manley
Email: e.j.manley@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

This module will expose students to the latest uses of data science for understanding cities and setting urban policy, explore technical, procedural and ethical challenges in using data science in urban policymaking, and address how urban policy results in tangible change in real-world cities. The module will be delivered through a mix of lectures, workshops, and fieldwork which will expose students to the impact of different urban policy within diverse environments.

Objectives

This module will provide students with an understanding of where data science is applied in urban policymaking. It will explore the key considerations in using data science across a range of urban policy areas, including contemporary challenges around data collection, interpretation, and limitations. Through fieldwork, students will consider the extent and limits to which data collection and analysis reflects urban life, and the subsequent challenges of integrating data science and policy.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:

1. Understand the urban policymaking and planning processes, including governance structures, decision-making, and the role of evidence and data analytics in the process.
2. Describe recent applications of data science and artificial intelligence in different areas of urban policy, including the common data and methods appropriate to application in real-world scenarios.
3. Evaluate the wider implications and limits of data science for urban policymaking, including issues of bias and ethics in data generation and interpretation, and practical constraints informed data science in cities.
4. Apply and appraise data science methods to analysing relevant urban policy challenges.


Skills Learning Outcomes

On successful completion of the module students will have demonstrated the following skills learning outcomes:

1. Engage technical proficiency through the study, selection, and application of programming and data analytics skills;
2. Develop academic writing skills to communicate data and analytical processes and implications to a policy audience;
3. Expand critical thinking skills to gather information from a range of sources, analyse, and interpret data to aid understanding and anticipate problems.

Syllabus

Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lectures	5	2.00	10.00
seminars	5	1.00	15.00
Practicals	5	3.00	15.00
Fieldwork	6	8.00	48.00
Private study hours			212.00
Total Contact hours			88.00
Total hours (100hr per 10 credits)			300.00

Opportunities for Formative Feedback

Formative feedback will be provided through submission of three short analysis plans, aligned to each workshop. These will require students to propose a policy area (within the theme area that week), a dataset, and method for analysis. These plans will be 500-words in length, and be submitted prior to the next workshop (two weeks later). The plans will test the student’s ability to establish a credible and thoughtful plan for policy analysis through application of appropriate data science methods. The assessments will furthermore support development of critical thinking and analytical skills needed for the summative assessment.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Coursework	70.00
Assignment	Coursework	30.00
Total percentage (Assessment Coursework)		100.00

Portfolio resit will be an individual summary of code from the seminars, with a reflection on the students contribution

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:14:37

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