Spatial Computing

Societal challenges such as climate change, urbanization, housing shortages, and social inequality demand timely and well-informed spatial decisions to improve the quality of our built environment. Yet the complexity of these challenges often exceed the capacity of traditional design and planning approaches. These decisions involve navigating complex, multi-disciplinary design problems in which multiple objectives, constraints, and stakeholders must be considered simultaneously. A central question in this context is not only what to design, but how design decisions can be supported, evaluated, and justified. Can we explore design alternatives, assess their spatial and environmental consequences, and compare trade-offs in a transparent and reproducible way? How can sustainability and livability goals be translated into quantifiable criteria that inform design choices?

Spatial Computing provides a structured, computational approach to design-related decision-making. By integrating spatial data, models, and algorithms, spatial computing enables designers and planners to formalize design intent, encode constraints and objectives, and systematically evaluate through simulations, the implications of different spatial configurations. Rather than treating analysis and design as separate phases, spatial computing links them in integrated workflows and iterative design-feedback loops where analysis actively guides design exploration.

The first quarter of the minor focuses on urban-scale spatial computing through geospatial digital twinning, where Geographic Information Systems, spatial analysis, AI and spatial optimization methods are used to support urban design and planning decisions. The second quarter focuses on building-scale spatial computing through parametric and generative design – using methods borrowed from game design – where computational design methods are used to generate, modify, and evaluate architectural design alternatives. The provided tools and workflows enable the evaluation of alternative spatial strategies, assessment of urban and building performance indicators, and comparison of scenarios in terms of sustainability and livability.

The minor is offered by the Digital Technologies section in the Department of Architectural Engineering & Technology, in collaboration with the Computer Graphics and Visualization group and the Discrete Mathematics and Optimization group of the Faculty of Electrical Engineering, Mathematics and Computer Science.

• Distinguish and identify data modelling, analysis, simulation, evaluation and optimization approaches and methods in relation to spatial decisions.

• Understand the complexity of spatial problems and the role of Spatial Decision Support Systems.

• Acquire and process geospatial data and create 3D models of existing parts of the real world

• Use Multi-criteria Spatial Decision Support System when making decisions about the (mutual) impact of a design and its environment.

• Compare design or decision alternatives analytically and procedurally, considering the mutual impacts of the design-environment and the evaluation criteria to formulate measurable sustainable development goals for spatial interventions.

• Apply computational models/methods in architectural design and spatial planning.

• Students can model real-world problems, in particular those related to spatial optimization, as linear and integer programming problems.

• Students can model real-world spatial optimization and infrastructural problems as graphs and reason about their structural properties (e.g. degree, connectivity, planarity, coloring, centrality).

• The students will be able to apply aspects of procedural content generation and computational simulations, which might be employed in architectural design and urban planning. They will learn how to code simple prototypes to verify their knowledge.

The course makes use of software that requires a capable laptop, e.g. it needs to have a dedicated GPU with support for OpenCL/CUDA. Laptops with only integrated graphics are strongly discouraged. Alternatively the student has a desktop PC at home.

This minor is suitable for those interested in rational and collaborative approaches to decision-making for sustainable development in surveying, design, and planning. It has a mathematics & programming-oriented approach, however, prior knowledge is not considered a prerequisite.

Lectures, workshops, and seminars. The assessment is based on the group and individual deliverables, specified in the syllabi or course description of each of the courses.

Check the detailed overview of courses, learning activities and study load at https://www.studyguide.tudelft.nl/