2022/23 Taught Postgraduate Module Catalogue
GEOG5404M Analytics for Urban Policy
30 creditsClass Size: 50
Module manager: Ed Manley
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2022/23
This module is not approved as an Elective
Module summaryThis module will provide students with a foundation in the application of data science in urban policymaking. It will expose students to the latest uses of data science for understanding cities and setting urban policy, it will explore some of the 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. Lectures will cover research and policy advances in the use of data science for urban policymaking across five major themes. Workshops will introduce students to data science challenges in each theme area, requiring them to conduct and interpret analyses that could support policy. Fieldwork will be conducted with an aim of exposing students to the impact of different urban policy within diverse environments and context.
ObjectivesThis 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.
1. Understand recent applications of data science and artificial intelligence in different areas of urban policy, including the common data and methods appropriate to application in theme areas
2. Understand 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.
3. Understand the urban policymaking and planning processes, including governance structures, decision-making, and the role of evidence and data analytics in the process.
4. Apply data science methods to analysing relevant urban policy challenges.
5. Understand the relationship between data science, policy, and implications for urban spaces.
The classroom components of the course will provide an overview of the application of data science in urban policymaking, across five themes:
• Mobility and Transport,
• Environment and Sustainability,
• Economic Development,
• Health and Wellbeing.
Teaching will describe the latest research and ‘real-world’ applications of data science in each policy area. This will furthermore embed themes relating to the critical application of these approaches – including:
• Policy design and implementation (e.g., processes, consultation, trials, implementation)
• Bias, ethics and limits in using analytics and AI
• Impact evaluation.
|Delivery type||Number||Length hours||Student hours|
|Private study hours||227.00|
|Total Contact hours||98.00|
|Total hours (100hr per 10 credits)||325.00|
Private studyReading on urban policy and research applications
Independent coding and practical work relating to data science and analysis on urban themes
Reading to prepare for field trip
Design, data collection, and preparation of assessment materials
Opportunities for Formative FeedbackFormative feedback will be provided through submission of five 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
|Assessment type||Notes||% of formal assessment|
|Report||Policy briefing (2000 words) and data science notebook||75.00|
|Portfolio||Analysis workflow proposals (500 words)||25.00|
|Total percentage (Assessment Coursework)||100.00|
The main piece of coursework will be the preparation of a policy report and data science notebook, that describes a real-world policy challenge, the potential policy, and uses data science methods to either further analyse policy issues in retrospect (e.g., through regression or clustering) or predicts prospective scenarios (e.g., classification or regression). The notebook should critically assess the impact these methods can have on real-world policy, including the limitations and any inherent biases. Following each biweekly workshop, students will also be asked to submit a short, proposed analysis plan within the theme area. The plan will describe a challenge area, the dataset, and the proposed methodology. Portfolio Work set wks 1,3,5,7,9 Work due wks 3,5,7,9,11
Reading listThe reading list is available from the Library website
Last updated: 06/05/2022
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