Using Machine Learning in Decision-making to Augment Beauty, Resilience, and Sustainability Outcomes in Urban Planning
Lead Research Organisation:
Queen Mary University of London
Department Name: Sch of Mathematical Sciences
Abstract
The proposed research seeks to answer the question of how machine learning may be used in decision-making to augment beauty, resilience, and sustainability outcomes in urban planning, design, and engineering in the UK.
Background
By 2050, 6.7 billion or 68% of the world's population is expected to live in cities (UN,2019), which means that both developing and developed worlds are faced with enormous challenges and critical and strategic decisions that must be made today. To ensure that this phenomenon of urbanisation and the other enduring global megatrend of climate change do not result in environmental catastrophes or a poor quality of life in these cities, governments and city leaders, globally, are grappling with decision-making for "complex situations in complex environments" (Bennett and Bennett,2008). Vast amounts of data generated through the amplified digital connectivity of the Fourth Industrial Revolution and concurrent growth in data analytics capabilities have permitted good support systems for decision and policy-making, yet, many instances of policy or strategy as well as design and engineering as applicable to the urban, environmental or sustainability domains remain the legacy of authoritarian decisions, creating ineffective, depoliticised solutions from standardized options and checklists (Jordan and
Turnpenny,2015).
Cities continue to evolve and change in a potentially irreversible manner, so, acknowledging that the challenges of the urban built environment are extraordinarily complex, understanding this complexity in the contexts of climate change, technological advancements, terror threats, cyberattacks, biological warfare, etc, understanding the problem(s) (which in itself may be an iterative process) for which solutions are sought, and understanding the human limitations are important first steps, and will help with identifying where and what types of decisions are required to augment the performance of cities, and where and how technology could help. Literature is limited in these areas.
To be successful, McKinsey's broad recommendations are that city leaders adopt a strategic approach, plan for change, integrate environmental thinking, and base the value proposition of their cities on opportunities for all. These recommendations demand that decision-making by
stakeholders leading urban planning, design, and engineering (UPDE) today is not only innovative, collaborative, and creative but also based on an ability to learn from the past at one level and see and shape the future at another. Machine Learning - a subset of Artificial Intelligence - with its well-documented ability to classify trends and patterns as well as ability to deal with multi-dimensional, multi-variable big data, is well-placed to:
1. help improve one's understanding of this situation's complexity
2. transform how new towns, cities, and large-scale mixed-use urban developments (LMUDs)
are conceived, developed, and maintained, and
3. augment the performance outcomes of new towns, cities, and LMUDs.
Background
By 2050, 6.7 billion or 68% of the world's population is expected to live in cities (UN,2019), which means that both developing and developed worlds are faced with enormous challenges and critical and strategic decisions that must be made today. To ensure that this phenomenon of urbanisation and the other enduring global megatrend of climate change do not result in environmental catastrophes or a poor quality of life in these cities, governments and city leaders, globally, are grappling with decision-making for "complex situations in complex environments" (Bennett and Bennett,2008). Vast amounts of data generated through the amplified digital connectivity of the Fourth Industrial Revolution and concurrent growth in data analytics capabilities have permitted good support systems for decision and policy-making, yet, many instances of policy or strategy as well as design and engineering as applicable to the urban, environmental or sustainability domains remain the legacy of authoritarian decisions, creating ineffective, depoliticised solutions from standardized options and checklists (Jordan and
Turnpenny,2015).
Cities continue to evolve and change in a potentially irreversible manner, so, acknowledging that the challenges of the urban built environment are extraordinarily complex, understanding this complexity in the contexts of climate change, technological advancements, terror threats, cyberattacks, biological warfare, etc, understanding the problem(s) (which in itself may be an iterative process) for which solutions are sought, and understanding the human limitations are important first steps, and will help with identifying where and what types of decisions are required to augment the performance of cities, and where and how technology could help. Literature is limited in these areas.
To be successful, McKinsey's broad recommendations are that city leaders adopt a strategic approach, plan for change, integrate environmental thinking, and base the value proposition of their cities on opportunities for all. These recommendations demand that decision-making by
stakeholders leading urban planning, design, and engineering (UPDE) today is not only innovative, collaborative, and creative but also based on an ability to learn from the past at one level and see and shape the future at another. Machine Learning - a subset of Artificial Intelligence - with its well-documented ability to classify trends and patterns as well as ability to deal with multi-dimensional, multi-variable big data, is well-placed to:
1. help improve one's understanding of this situation's complexity
2. transform how new towns, cities, and large-scale mixed-use urban developments (LMUDs)
are conceived, developed, and maintained, and
3. augment the performance outcomes of new towns, cities, and LMUDs.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/V519935/1 | 01/10/2020 | 30/04/2028 | |||
| 2496675 | Studentship | EP/V519935/1 | 01/01/2021 | 30/09/2026 | David Carun |