Designing cities is a complex task. Thousands of elements need to be configured and multiple requirements must be met. Parametric design enables to efficiently create and evaluate multiple urban planning variants. By the creation of algorithmic urban models the design process becomes more transparent and therefore more accessible to multiple stakeholders (e.g. in participative processes). Furthermore, the exploration of a multitude of design variants helps to systematically search for optimal solutions (e.g. trade-offs between conflicting design goals).

In a master thesis you can explore the potentials of parametric modeling for solving urban design problems using a case study (e.g. a contemporary urban design competition). Therefore you should create parametric models for different urban aspects (e.g. street network, parcellation, public spaces, neighborhood configuration) and an analysis framework for testing the resulting designs (e.g. energy, daylight, visibility, accessibility). Using optimization approaches (e.g. evolutionary strategy) you can systematically search for good design solutions. Or, you can test the feasibility of your parametric models using statistical analysis (How well do the design variants of the parametric model perform? How robust is the parametric model?).