Computational methods in urban studies

Summary

CMUS will focus on acquiring insights into, engaging with, and modeling the processes of spatial transformation in contemporary societies. It will integrate statistical techniques and critical urban theory to promote socially and environmentally fair policies against the climate crisis.

Content

Keywords

• Computational social science
• Urban studies, planning, and sociology
• Transportation infrastructures
• Mobility practices and behavior
• Quantification processes and data collection techniques
• Social and spatial inequalities

Learning Prerequisites

Recommended courses

A fundamental comprehension of computational thinking as well as previous experience with coding (R) are recommended, although all the materials related to coding will be made available to students for reproduction.

Learning Outcomes

By the end of the course, the student must be able to:

• Derive concepts from urban studies to describe and analyze contemporary cities and peripheries.
• Apply advanced statistical techniques to the field of urban studies.
• Quantify and model sociological processes and mechanisms that underlie urban mobility and its impact on urban systems.
• Assemble empirical evidence to support sustainable urban planning.
• Estimate social and spatial inequalities to reduce their negative impact in urban policies.

Transversal skills

• Set objectives and design an action plan to reach those objectives.
• Use a work methodology appropriate to the task.
• Take account of the social and human dimensions of the engineering profession.
• Take responsibility for environmental impacts of her/ his actions and decisions.
• Demonstrate the capacity for critical thinking
• Access and evaluate appropriate sources of information.
• Collect data.
• Write a literature review which assesses the state of the art.

Teaching methods

The course will comprise lectures presenting significant theoretical elements and concepts related to urban and/or mobility studies, as well as the way in which statistical techniques can incorporate such theories and concepts to yield empirical results. This will be illustrated through open-access and reproducible codes, which will be made available to students.

During the semester, students will have the opportunity to work in groups to advance their project under the guidance of the teaching team. A designated period will be devoted to the presentation and discussion of each group's research question, methodological design and empirical results in the context of the existing literature.

Expected student activities

Over the course of the semester, students will engage in a group project comprising three distinct phases: formulation of the research question, development of the methodological design, and presentation of the empirical results. Each group will be required to present their work twice during the semester to receive feedback and to submit a scientific report, including a literature review, a contribution to the state of the art and operational guidelines, by the end of the semester.

The students are free to select their research question, determine whether they wish to gather new data or utilize existing data, and choose the computation techniques they will employ. A list of recommended literature, in the form of scientific papers, will be made available on the Moodle platform.

Assessment methods

The evaluation will primarily be based on the group exercise completed throughout the semester, with the oral presentations accounting for 50% of the total grade (individual assessment based on the restitution of concepts and methods learned in class) and the final written report for 30% (collective assessment based on the structure and demonstration related to the research question). The remaining 20% will consist of a two-page written assignment in which each student will critically elaborate on one concept presented and discussed in class, considering how it was introduced, how useful it was and how it could be applied in non-academic contexts.

Supervision

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

The following books will be referenced in class (additional resources will be provided on Moodle):

• Banister D. (2018), Inequality in Transport, Alexandrine Press.
• Bonneuil C. & Fressoz J.B. (2016), The Shock of the Anthropocene: The Earth, History and Us, Verso Books.
• Cidell J. & Prytherch D. (Eds.) (2015), Transport, Mobility, and the Production of Urban Space, Routledge.
• Desrosières A. (2014), Prouver et gouverner : une analyse politique des statistiques publiques, La Découverte.
• Forgarty B. (2023), Quantitative Social Science Data with R: An Introduction, SAGE.
• Illich I. (1974), Energy and Equity, Calder & Boyard.
• Meadows D.H. (2008), Thinking in Systems: A Primer, Chelsea Green Publishing.
• Ohnmacht T. et al. (2016), Mobilities and Inequality, Routledge.
• Rosa H. (2013), Social acceleration: a new theory of modernity, Columbia University Press.
• Urry J. (2000), Sociology beyond societies: mobilities for the twenty-first century, Routledge.
• Wacquant L. (2022), Bourdieu in the City: Challenging Urban Theory, Wiley.

Ressources en bibliothèque

• Find the references at the Library

Moodle Link

• https://go.epfl.ch/URB-404