This paper aims to explore issues of public urban spaces that have emerged from the gaps found in today’s participatory design. While it is evident that the public are more involved in the design thinking process to produce results that should cater to the community, their opinions are often overlooked as less important remarks from non-professionals. This research attempts to explore public responses in relation to their implicit perceptions and behaviours. The study provides an empirical understanding of users of public spaces in embedding greater value for public voices within participatory design. By applying machine learning methods drawn from Natural Language Processing and the new field of Neuroarchitecture, the traditional methodology of participatory design could incorporate public participation as an implicit feature during the design process. Traditional methods of data collection (e.g., online questionnaires and intercept surveys) are used to extract respondents’ sentiments regarding two popular locations in Singapore: Keong Saik Road and Geylang Road. To obtain users’ subconscious data that can be projected with the polarity scores, participants were invited to experience both sites while wearing eye tracking glasses. The data collected was then visualised as heatmaps and Areas of Interests (AOI), with metrics involving gaze fixations, saccades and graphs depicting peaked interests. Using correlation analysis, the qualitative relationship between the respondents (sentiment analysis) and their subconscious human behaviour (eye-tracking) is made quantifiable. The application of machine learning analysis facilitates the production of valuable evidence that can be implemented for a more collaborative design process. It also extends the versatility of public opinions, to allow communities who are the main users of public spaces to become larger stakeholders. Our neuro-participatory design approach aims to better align the design of cities with implicit humanistic goals.
Immanuel Koh holds a joint appointment as an Assistant Professor in Architecture & Sustainable Design (ASD) and Design & Artificial Intelligence (DAI) at the Singapore University of Technology and Design (SUTD), where he directs Artificial-Architecture. He obtained his PhD at the École polytechnique fédérale de Lausanne (EPFL), while doing transdisciplinary research between the School of Computer Sciences and Institute of Architecture. His doctoral thesis “Architectural Sampling” was nominated for the Best Thesis Prize and Lopez-Loreta Prize. Trained at the Architectural Association (AA) in London and Zaha Hadid Architects, Immanuel has exhibited internationally, including NeurIPS’ AI Art Gallery, V&A Museum and Venice Biennale; and published widely, including International Conference on Computer Vision (ICCV), Architectural Design (AD) and Design Computing & Cognition. He is the co-founder of Neural Architecture Group (NAG) and co-curator of AIArchitects.org. His current funded research projects focus on neuro-participatory urbanism, defence architectural intelligence, creative AI aesthetics, and deep spatial computing.
Elissa Gowika Hartanto is a graduate from class 2021 of MSc. In Urban Science, Policy and Planning from Singapore University of Technology and Design. Prior to her masters, she has also attained a Bachelor of Arts with Honours for Spatial Design, with the best thesis award, and a diploma in Landscape Design. As an urban science graduate, her master’s thesis on perception using AI and Machine Learning was also awarded runner-up in the 3MT competition. Her recent works include a feature in Lee Kuan Yew Centre for Innovative Cities: a research done for Urban Redevelopment Authority in Singapore on Facilitating social mixing in public spaces, receiving praise from Singapore Sports council as well for her inclusion of underground communities. She has a written article in the upcoming URBAN AI Publication, about the use of machines in urban life. Her oral history submission to National Archives Singapore reflected LGBTQ communities face during COVID-19.