Welcome! This organization hosts the course materials, group assignments, and submission repositories for Applied Spatial Analysis for Sustainable Urban Development (ARFW0501), taught by Dr. C. Forgaci and Dr. D. Cannatella.

ARFW0501 brings MSc Urbanism, Landscape Architecture, and Geomatics students together in an intradisciplinary course to work on an integrated, end-to-end spatial analysis assignment. You'll build scalable, automated, and reproducible analytical workflows, combining methods such as spatial analysis, network analysis, and climate/landscape analysis, applied to a real-world urban development challenge, and learn to communicate your findings through data visualization.

• R / RStudio — for data wrangling, spatial analysis, and reproducible reports
• QGIS — for spatial visualization and cartographic output
• Git & GitHub — for version control and submitting your work

This year's practicals will be structured around a real case study:

• PAST-FORWARD — develops climate adaptation strategies and a design support system for adaptive Historic Urban Landscape (HUL) planning, combining geospatial analysis, participatory planning, and adaptive urban landscape design, tested through living labs in Delft and Rotterdam (NL) and Xi'an and Guangzhou (CN).
• Potential case study area: Rotterdam (to be confirmed) — exercises may draw on Rotterdam as the primary study area; this will be finalized before the course starts, so treat it as provisional for now.

You're welcome to use AI tools (ChatGPT, Claude, GitHub Copilot, etc.) as part of your workflow for this course, within these guidelines:

• Use AI to support your thinking, not replace it. AI tools can help you debug R code, understand a method, or get unstuck, but the analytical choices, interpretation, and final writing need to be your own.
• Disclose how you used it. Add a short note in your report (e.g. in an appendix or methods section) describing which tools you used and for what - for example, "used Claude to debug a sf join error" or "used ChatGPT to draft an initial outline, which we then rewrote."
• You are responsible for correctness. AI-generated code, statistics, or text can be wrong, outdated, or subtly misleading, especially with spatial data. You must be able to explain and justify every method and result in your submission, AI-assisted or not.
• Don't submit AI output as your analysis. Generating your maps, statistical interpretation, or conclusions wholesale from a prompt defeats the purpose of the assignment and will be treated as you would treat any other misrepresentation of authorship.
• When in doubt, ask. If you're unsure whether a particular use is appropriate, check with your TA or the course coordinator before submitting.

This policy follows TU Delft's general guidance on the responsible use of AI in education. If in doubt, that guidance takes precedence.

• Multi-Scale Spatial Analysis for Nature-Based Urban Heat Island Mitigation (Group D) — Artemi Kurski, Niranjan Pradeep, Darius Floroiu, Henryk Gujda (MSc Geomatics)

• Ecological Connectivity Analysis in Delta Cities — A Case Study in Rotterdam and Guangzhou (Group A) — Faried Nurmohamed (MSc Urbanism / Geomatics), Karlijn Huissen, Austėja Tarvydaitė, Marta Zolnowska (MSc Geomatics)

• Spatial Justice through Urban Green Space Connectivity (Group E) — Roxanne Vuijk (MSc Urbanism), Belina Aileen, Daman Dogra (MSc Geomatics)

If you're in the Urbanism or Landscape Architecture track, remember that ARFW0501 is designed to be taken together with Integrated Metabolic Design for Sustainable Neighborhoods, Cities and Regions (ARFW1001, 10 EC). The two are not offered separately for your track, but as a combined 5+10 EC package. The analytical workflows and spatial data skills you build here feed directly into the metabolic design work in ARFW1001.

Reach out to us or to the ARFW1001 team if you want advice on sequencing the two courses or balancing the combined workload.

Good luck, and welcome to the course!