03 Self-Organizing Space

Architectural design requires the negotiation of a wide variety of often conflicting constraints and conditions. This puts a tremendous burden on designers to understand and evaluate all the design and site parameters in the conceptual phase of the project. Design methodologies that utilize conventional means of representation such as site diagrams, maps, or other orthographic projections may not always be adequate to produce truly integrative design solutions. They often simplify conditions for user clarity or eliminate volumetric and temporal data entirely. As computational design tools develop, however, and the mapping of georeferenced urban data becomes more commonplace, it becomes possible to integrate spatial information into design strategies and evaluate various relationships more effectively.

Taking clues from medical imaging, Self-Organizing Space uses voxel data to represent volumetric gradients in material properties and densities of spatial conditions. This method can be used to generate morphogenic spatial analysis of an existing site. The project explores how self-organizing programmatic agents can use this analysis and embedded behaviors to visualize performative schematic design scenarios. These agents, which represent a variety of functional spaces, programmatic requirements, design constraints, and value sets, can negotiate the myriad of environmental and socio-economic site conditions as well as interact with other adaptive programmatic spaces.