15 contact hours + 10 hours of independent work (self-study)
Dates: 26th January – 13th February
Chiara Garau (DICAAR, University of Cagliari) – Course coordinator (PhD course)
Pierre Frankhauser (University of Franche-Comté)
Tazyeen Alam (DICAAR, University of Cagliari)
Reza Askarizad (DICAAR, University of Cagliari)
Vito Garramone (IUAV, University of Venice)
To register for this PhD course, send an email to Professor Chiara Garau: cgarau@unica.it
Course Brief: This 15-hour intensive course introduces PhD students to the theoretical foundations and practical applications of Dynamic Urban Modelling (DUM) in spatial and urban planning, with a strong focus on data-driven analysis and synoptic visualisation. The course combines complementary modelling traditions (urban complexity and scaling, including fractal approaches, network and graph-based reasoning, space syntax analysis, and GIS-based spatial workflows) to decode how urban systems are structured, how they evolve, and how evidence can be translated into planning-oriented insights.
The course begins with key conceptual building blocks for interpreting urban form and spatial organisation, then moves quickly into applied work using open-source tools (e.g., QGIS, depthmapX, and reproducible scripts). Students will work with urban datasets (e.g., street networks, building footprints, land-use layers) to compute and interpret indicators related to accessibility and movement potentials, spatial configuration/centrality, resilience and redundancy, and selected morphological scaling metrics.
Hands-on sessions are based on real-world case studies and culminate in an individual final project in which each student will develop and present their own concept and application idea of Dynamic Urban Modelling (DUM) in spatial and urban planning. The course aims to provide an integrated synthesis of theory, tools, and applied analysis, enabling participants to incorporate DUM approaches into their ongoing research.
PhD Course language: English
Duration: January 2026-February 2026
Objectives of the Course
- To introduce students to Dynamic Urban Modelling (DUM) as a paradigm for understanding spatial complexity in cities.
- To build a working knowledge of fractal models and understand the basics of graph theory and their relevance in urban planning and design.
- To enable students to use quantitative and computational tools (e.g., QGIS, depthmapX, or custom scripts) to analyse urban networks.
- To foster interdisciplinary thinking by integrating insights from urban theory, complexity science, and spatial analytics.
- To introduce principles of data visualization and the construction of a dynamic dashboard (using Shiny, an R package that makes it easy to build interactive web applications-apps)
Possibilities of Applications
- Urban Accessibility & Mobility Planning: Using space syntax and graph-based centrality to evaluate pedestrian/cyclist access and public transport integration.
- Resilient Urban Design: Applying fractal analysis to assess redundancy, robustness, and the scaling properties of urban infrastructure.
- Land Use and Zoning Optimization: Identifying emergent spatial patterns that inform better land allocation strategies.
- Comparative Urban Studies: Enabling comparative morphological studies between cities based on fractal dimensions and network logic.
- Smart Cities & Digital Twins: Laying foundational knowledge for integration with real-time data systems and simulations.
- Policy and Scenario Planning: Using dynamic models to forecast outcomes of zoning changes, infrastructure investments, or population shifts.
- Participatory processes, public awareness, and information: Using data visualization to build participatory processes and shape informed public opinion, to disseminate and discuss collective issues, planning actions, or public policies.
Course Structure: 6 sessions organised as follow:
Session 1 (2 hrs) 10:00 – 12:00
26th January: General concepts on the Course
Topics Covered: Introduction to the course and teaching team; course structure, datasets and expected outputs; overview of Dynamic Urban Modelling (DUM) approaches (urban complexity, network/graph thinking, fractal/scaling, space syntax), tools (QGIS, depthmapX, dashboards) and planning applications.
Delivered by: C. Garau
Session 2 (3 hrs) 16:00 – 19:00
27th January: Fractal Analysis in Urban Planning
Topics Covered: Fractal models: foundations & relevance for planning (why fractals matter; key concepts). Urban fractal analysis: mono vs multi-fractal; scaling/sprawl analysis; interpretation and communication of results; links to network representations and morphological indicators.
Delivered by: C. Garau; T. Alam
Session 3 (2 hrs) 11:00 – 13:00
28th January: Fractalopolis. A multiscale approach for sustainable metropolitan planning
Topics Covered: Fractalopolis framework and workflow; multi-scale reading of metropolitan structure; interpreting spatial hierarchies and patterns for sustainability-oriented planning; discussion of case-study logic and transferable insights.
Delivered by: C. Garau; P. Frankhauser
Session 4 (3 hrs) 16:00 – 19:00
29th January: Space Syntax in Urban Planning and Design
Topics Covered: Space syntax concepts and core measures; movement potential and spatial configuration; applied workflow with depthmapX; interpreting outputs for urban design and planning decisions; integration with GIS layers and other indicators.
Delivered by: C. Garau; R. Askarizad
Session 5 (4 hrs) 9:00 – 13:00
30th January: Data Viz & Dashboards for Planning Decision Support Systems
Topics Covered: Planning Decision Support Systems (PDSS): goals and structure; data preparation and visualization choices; designing effective visual narratives for planning; building a simple interactive dashboard (e.g., Shiny) to communicate indicators, maps, and scenarios.
Delivered by: V. Garramone
Session 6 (1 hr) 11:00 – 12:00
13th February: Individual final presentations and discussion + peer feedback; course wrap-up
Topics Covered: Individual final presentations (methods, indicators, maps, interpretation); peer feedback and discussion; lessons learned; how to transfer DUM approaches into PhD research; course wrap-up.
Delivered by: C. Garau