Application of Design Data Analytics in Computational Design of Built Environments

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Designers explore multiple design alternatives. They begin with incomplete, imprecise, and changing design goals, and they distill complex emerging design data into meaningful and pragmatic design ideas for finding 'satisficing' solutions. Here, we use 'design data to refer to any input provided by the external agents to be considered in design-decision making or any output data generated in the process and provided as input back to the design. Current design systems lack support for data-informed design exploration: first, they only support a single-state model, forcing designers to work sequentially. Second, they lack features for dealing with large, diverse, dynamic volumes of design data that grow rapidly as alternatives are explored. These drawbacks are interrelated and must be addressed holistically by improved systems to better support working with alternatives. Thus, this proposal addresses the need for devising new computational design tools and workflows for working with alternatives. This need is more emphasized when large volumes of alternatives become an integral part of design exploration. We propose developing novel computational systems to directly support data-informed design exploration by adapting the state-of-art methods from other related disciplines such as visual analytics. We will test and validate our solutions in the context of designing built environments and products. My long-term goal is to develop a methodology for working effectively with alternatives. The mid-term objectives are: (1) develop computational methods for searching large collections of design alternatives assisted by making use of emerging design data; (2) to identify types and sources of data and explore visual data analysis tools that designers can use; (3) develop workflows for data-informed exploration of alternatives. My approach is built on design-based research where the prototype systems and experimental studies incrementally and iteratively reveal new necessary tool features and use cases. In the experiment phase, the prototypes will be used to generate knowledge about the possibilities for novel design exploration systems and data-informed workflows. As an applied research program, this aims to contribute to a deeper understanding of and practical solutions for improved computational design environments. Good design is everywhere a gateway to success and a key component of innovation. With her service-focused economy, Canada can take a strong lead in changing design practice by creating new computational technologies and demonstrating how these technologies can effectively improve outcomes. Although Canada's national competitiveness is high, its design competitiveness in the global market is not at the levels we expect to see. The first step for improving this imbalance is better computational methods in creative industries, including designing built environments.