Generative landscape modeling in urban open space design: An experimental approach

This paper aims to explore the algorithmic design thinking for the landscape through a generative modeling approach to urban open space. Focusing on dynamic interactions between spatial dispersion of hard-soft surfaces, shadow elements like tree locations and their impacts such as micro-climatic condition changes with human behaviors, were the primary inputs of the process. Using a case study from Turkey/Istanbul-Kadıköy, the reciprocal relations between social (human movement), physical (hard-soft structures) and ecological (surface radiation and microclimate analysis) parameters were studied, and how these relations formed the design was shown. The modeling process was defined in 4 algorithmically associated stages: firstly, field observations were conducted to collect data on vegetation and user behaviors, for site digitalization. After that, algorithmic parameters were defined in the second phase; and design constraints, as the first initiator of the interactive process, were identified. At the last stage, by the evaluation of all parameters with constraints, final design set-up was originated via a Quadtree algorithm. During these phases, user simulation data, surface radiation and outdoor mi-croclimate analysis findings were shown for comparison. Therefore, this study underlines the im-portance of the data for landscape design, and its process, rather than the final design solution.