Preface

Acknowledgments

I would like to express my heartfelt gratitude to the following individuals and organisations, without whose support, guidance, and encouragement, this research would not have been possible:

My Principal Supervisor, Professor Ning Gu, for his unwavering support, guidance, and mentorship throughout my candidature. His insightful feedback, constructive criticism, and deep knowledge of the field have been instrumental in shaping my research and improving the quality of my work.

My Co-Supervisors, Dr Alpana Silvam and Dr Jorge Ochoa Paniagua, for their expertise, valuable input, and constructive feedback. Their encouragement and support have been essential in keeping me motivated and on track during my research journey.

My Associate Supervisors, Dr David Kroll and Professor Gabriela Celani, for their insightful comments, constructive criticism, and encouragement throughout my candidature. Their guidance and support have been immensely helpful in shaping my research and strengthening my arguments.

My Industry Advisors, Dr Aida Alfroz and Philippe Naudin, for their industry insights and practical advice. Their contributions have been vital in helping me bridge the gap between academia and industry and ensuring the relevance and impact of my research.

I would also like to acknowledge the financial support provided by the University President’s Scholarship (UPS) provided by the University of South Australia, which made it possible for me to pursue my research and complete this thesis.

I am grateful to all the staff at UniSA Creative for providing me with essential support during my candidature, from administrative assistance to access to resources and facilities.

I would also like to thank Alison-Jane Hunter, a professional editor, for providing proofreading services and improving the readability and coherence of my thesis.

I am also indebted to my academic friends and colleagues from UniSA and UniAdelaide, to mention just a few, Gabriela Dias, Dr Pan Liao, Dr Sahar Soltani, Dr Carlos Koc Batesagui, Zahra Yousefi, Arsham Bassiri, Ayaz Khan, Rosa Teimouri, Elita Nuraeny, Lipon Saha, and many others who have contributed directly or indirectly to my research with their intellectual stimulation, feedback, and friendship.

I am also grateful to those who were crucial in my formation as a Computational Designer and Researcher, and who introduced theories, concepts, and tools to me along my journey. These include, among others, Dr Charles Vincent, Professor Gabi Celani (once again), Ernesto Bueno, Dr Gonçalo Castro Henriques, and Affonso Orciuoli.

I am grateful to the Interview and Focus Group Interview Participants who have generously shared their time, knowledge, and experiences to enrich my research.

Last but not least, I would like to thank my beloved family member: my son and sun, Ravi, my loving wife Juliana, and my family members in Brazil. Their unconditional love, support, and sacrifice have been the driving force behind my pursuit of academic excellence. I could not have made it this far without their unwavering support and company all these years.

Thank you all for your contributions and support. I am deeply grateful for everything you have done for me.

Abstract

Cities are complex systems that face challenges related to rapid urbanisation, climate change, and resource scarcity. One approach to address these issues is the concept of “smart cities”, which can be interpreted in various ways. The 3RC Framework categorises smart city approaches into restrictive, reflective, rationalistic, and critical schools of thought, each with their own emphasis on different relationships between technology and its social dimension. Despite these differences, the primary goal of smart cities is to use information and communication technologies (ICTs) to enhance cultural, social, and urban development. Despite the current favourable conditions of smart infrastructure, data science methods, and availability of open data in urban design, there is a lack of systematisation and integration of computational data-driven tools. Current tools tend to use proprietary software, limiting their accessibility, community development, and sharing of improvements, which are considered crucial features for empowering citizens as active users and decision-makers in the development of future smart cities.

Hence, from the premise that systematisation, integration, and awareness of the socio-political implications of the development and use of computational data-driven tools are needed to assist urban design processes and support the development of future smart cities, this research aims to develop an integrated data-driven approach to assisting computational urban design processes, following a Design Science Research (DSR) methodology. DSR is appropriate as it focuses on creating artefacts that solve real-world problems, and this study aims to develop a conceptual framework and a computational toolkit prototype as research artefacts that compose the approach. The research is divided into three phases: the Conceptual Development Phase, the Computational Development Phase, and the Validation Phase, each aligned with the six steps of DSR and research objectives. The first phase involves understanding current data-driven urban design processes and developing a framework for systematising the approach. The second phase focuses on developing a computational toolkit prototype based on the framework and demonstrating the proposed approach through a scenario. The third phase involves validating the approach among designers.

During the Conceptual Development Phase, the research aimed to understand the current data-driven urban design processes and their enabling computational smart technologies. The literature review identified a lack of integrative approaches between design processes and data procedures, and the need for a holistic understanding of data-driven urban design tools. Semi-structured interviews refined this understanding with perspectives from traditional and data-driven designers, and a thematic analysis was conducted to develop the conceptual framework.

In the Computational Development Phase, a computational toolkit prototype was developed to assist data-driven urban design processes. The prototype was built as a plugin for Sverchok, a visual programming environment of Blender 3D, and underwent reflective practice consisting of feedback loops of proposing, building, testing, and retesting, to assess its feasibility and validity. The developed tools were demonstrated in a real-world scenario to showcase their potential usability.

The Validation Phase aimed to validate the integrated data-driven approach among designers using Focus Group Interviews. The approach, including the framework and computational toolkit, was analysed qualitatively through constant comparison between two groups of participants, traditional and data-driven designers.

The findings unfolded the integration of the design process, data procedures, and computational technologies, contributing to enhance data-driven urban design processes. The developed integrated approach provides an understanding of data-driven urban design and a practical environment for testing and applying the approach in different urban design scenarios. The findings of the study suggest that interoperability between multiple datasets is crucial for successful data-driven urban design processes. Additionally, the use of free and open-source technologies in the development of the computational toolkit prototype promotes awareness and potential expansion of holistic development of data-driven design computational tools in other fields of the architecture, engineering, and construction industries. The proposed approach and computational implementation have the potential to contribute to raising awareness of the implications of technology use by citizens in future smart city scenarios.

Covid-19 Impact Statement

This thesis was conducted, in its majority, during the Covid-19 pandemic and, as such, had suffered impact, both direct and indirect, from it. The following detail the challenges related to the Covid-19 restrictions.

Even when lockdowns were being removed, strict social distancing restrictions were still in place and mandatory. Also, because of the continuing impact and uncertainty, stakeholders had other priorities from their businesses and were less inclined to participate in interviews.

Declaration

This thesis presents work carried out by myself and does not incorporate without acknowledgment any material previously submitted for a degree or diploma in any university. To the best of my knowledge, it does not contain any materials previously published or written by another person except where due reference is made in the text; and all substantive contributions by others to the work presented, including jointly authored publications, are clearly acknowledged.