AI for urban history

Course Description

This course explores how recent developments in artificial intelligence, particularly large language models (LLMs), that can be used to investigate the long-term historical evolution of cities. Through a combination of theoretical grounding and hands-on experimentation, students will learn how to critically use AI tools to analyze historical urban data.

The course is situated within the broader framework of urban systems, where the city is understood as the product of multiple interacting variables such as social, spatial, economic, and political that evolve across time and space. Urban systems analysis emphasizes the dynamic and layered nature of urban change: how housing, mobility, commerce, infrastructure, and land use co-evolve, and how such transformations can be reconstructed from historical sources. The course encourages students to think of cities not only as physical objects, but also as temporal systems shaped by flows, processes, and memory.

Students will work with historical datasets derived from city directories, cadastral sources, civil registries, and historical maps, covering the period 1700-Now. In some selected cases, datasets extend over even longer timeframes. The cities studied include Lausanne, Paris, Venice, Amsterdam, London, New York, Geneva, and selected U.S. cities. These sources allow for diverse questions related to urban morphology, socio-economic change, demography, commercial activity, and mobility.

The first part of the course introduces foundational concepts in both urban history and AI methods, with particular attention to the functioning and epistemological implications of LLMs. The second part is project-based: students will work on their own research question using LLMs and/or statistical tools to analyze the datasets provided.

Each week combines:

• Theoretical framing (2h): urban history concepts, source criticism, and technical background on LLMs and AI methods

• Practical session (2h): guided exercises using Python, NLP/statistical tools, dataset exploration, and project prototyping

Course Structure

• Weeks 1-4

  • Introduction to urban history and historical sources

  • Technical overview of LLMs and their architecture

  • Epistemological discussions on the use of AI in the humanities

• Weeks 5-6

  • Introduction to datasets and toolkits

  • Group formation and project design

• Weeks 7-13

  • Project development with technical guidance

  • Peer feedback and interim presentations

• Week 14

  • Final presentations and critical discussion

     

Expected Outcomes

By the end of the course, each group of students will produce:

1. A Web-Based Exploratory Interface

An online prototype that showcases the historical analysis developed during the project. It may include:

• Interactive visualizations (maps, timelines, network graphs)

• Interfaces for navigating or querying historical data

• Comparative views between cities or time periods

This interface should demonstrate the students' ability to communicate a research narrative using both AI-generated insights and critical historical interpretation.

2. A Project Booklet / Infolio

A printable portfolio (PDF or printed) that documents:

• The research question and urban context

• The dataset(s) used and historical sources involved

• The methods applied (LLM prompting, data cleaning, statistical analysis)

• A critical reflection on the results and methodological choices

• A visual narrative of the process (sketches, diagrams, maps, screenshots)