Section: Module 7 - Capstone project | Cities Spatial Model Training | Short Courses

Main course page

  • General

    • About the course

      This online training course provides a practical, in-depth introduction to the Cities Spatial Model, designed for policymakers, urban practitioners, and analysts working on city planning and economic development. The course builds a clear intuition for how the model works and why spatial and equilibrium effects matter for urban policy.

      Participants will learn what data the model requires, where to find reliable sources, and how to clean, structure, and manipulate datasets into the formats needed for analysis using QGIS and R. Step-by-step guidance is provided on running the model in R, interpreting outputs, and using visual tools to explore results. The course also focuses on how to communicate findings clearly and effectively, translating technical outputs into policy-relevant insights that can inform real-world decisions. There is also an option to use the Shiny app interface - enabling users to upload data and run the basic model without any coding whatsoever. 

      By the end of the course, participants will be equipped with both the conceptual understanding and practical skills needed to apply the Cities Spatial Model to real urban challenges, supporting better-informed, evidence-based planning and investment decisions. Please note that there is no certificate provided on completion of the course, it is designed for practical application and knowledge sharing.

      While researchers and students are welcome to participate, the emphasis will be on practical applications of the model rather than its technical details. For those in search of a more in-depth understanding of quantitative spatial models, we encourage you to engage further here: https://www.quantitativeurbanmodels.com/toolkits 

    • Meet your instructors

      Nick Tsivanidis - Associate Professor of Economics, UC Berkeley and IGC Cities Research Programme Director

      Nick Tsivanidis

      Maria Del Mar Gomez - Research Analyst, Cities Spatial Model

      Maria Del Mar Gomez - Research Analyst on the Cities Spatial Model

      Daniel Ruiz Palomo - Research Analyst, Cities Spatial Model

      Daniel Ruiz Palomo

    • Join the virtual office hours

      5 June 2026: https://lse.zoom.us/meeting/tZYqf-qprDsiG90wJssVW5PLKNsduteWVEU8/calendar/google/add?meetingMasterEventId=DkQJckhmQ7uSs8EjJ2Ou7Q

      3 July 2026: https://lse.zoom.us/meeting/tZUrf-GppjstEtCU1YxhKm_JUZClk4cdvJR-/calendar/google/add?meetingMasterEventId=a8Gzh8rLTiyvn5UzHuAT2Q

      Register your interest and submit questions here.

Module 6 - Analysing the results
Feedback

Module 7 - Capstone project

  • Module 7 - Capstone project

    • Objectives

      • Formulate a clear urban policy or shock question and translate it into a well defined counterfactual using the Cities Spatial Model.
      • Run and compare baseline and policy scenarios, applying the full workflow learned in the course
      • Analyse and visualize model results to identify spatial, distributional, and welfare impacts across the city
      • Interpret outcomes in general equilibrium terms, explaining how labor markets, land markets, and amenities jointly adjust
    • Interactive content SCORM package

    • Using the data pack, prepare the required model inputs by working with the provided shapefiles and travel time matrices, applying the GIS operations learned in Module 4 to generate travel time matrices for the baseline and the three transport scenarios S1, S2, and S3. If you prefer, you may use the pre computed travel time matrices available in the subfolder TravelTimeMatrix.

      Once your characteristics file and travel time matrix are correctly formatted, run the Cities Spatial Model for the baseline and counterfactual scenarios using either the Shiny app or the CRAN package in R. Compare the results across scenarios, focusing on changes in wages, rents, employment, population, and aggregate welfare, and produce at least one map that clearly illustrates the spatial distribution of impacts to support your policy recommendation.

      Finally, using the model intuition developed in Modules 1 and 2, explain the economic mechanisms behind your results. Discuss how changes in travel times affect accessibility, productivity, location choices, housing demand, wages, rents, and welfare, and clarify why some locations gain while others may lose.

      Disclaimer

      The datasets provided in this capstone are entirely synthetic. They were generated for teaching purposes and do not represent real administrative records or any factual characteristics of a real city.

Module 6 - Analysing the results
Feedback