Panel 1: Science-Based Approach to Future Scenario Planning

Date: 5 October, Thursday
Time: 9.00am – 11.00am


With increasingly unpredictable dynamics of cities and their development trajectory, planning cities can be a challenge. A systems level science-based approach can help reduce uncertainty by identifying the key underlying drivers of the urban system and its emergent behaviours, grasp the co-evolution of different systems, and simulate plausible future scenarios. By combining scientific urban science concepts such as circular causality and complexity sciences with advanced computational modelling and data, emergent patterns and trends can be detected at various temporal and spatial scales, helping to plan for the future of cities.


About the Speakers


Dr Long Ying resized

Dr Long Ying
Associate Professor
School of Architecture, Tsinghua University
Director and Founder, Beijing City Lab

Dr Ying Long, PhD is now working in the School of Architecture, Tsinghua University, China as a tenured associate professor. His research focuses urban science, including applied urban modelling, urban big data analytics & visualisation, quantitative urban studies, planning support systems, data augmented design and future cities. He has an education background in both environmental engineering and city planning. Before joining Tsinghua University, he has worked for Beijing Institute of City Planning as a senior planner for eleven years.

Familiar with planning practices in China and versed in international literature, Dr Long’s academic studies creatively integrate international methods and experiences with local planning practices. He has published almost two hundred papers and led over twenty research/planning projects. His funded projects range from international organisations like World Bank, World Health Organization, World Resource Institute and NRDC, and Wellcome Trust, internet companies like Alibaba, Baidu, Jingdong, Tencent, Didi, Mobike and Gudong, local governments like Beijing, Chengdu, Qingdao, Hefei, Zunyi, Rongcheng and Laizhou, to central governments like NDRC and MOHURD, and the NSFC. Dr Long is also the founder of Beijing City Lab (BCL:, an open research network for quantitative urban studies.


Topic: UrbanSense: Empowering Communities through Active Sensing for Sustainable Urban Development

Accurate monitoring of urban environments and their dynamics is essential for achieving the Sustainable Development Goals (SDGs) set by the United Nations. However, traditional sensing methods face challenges in meeting the needs of urban monitoring, including difficulties in balancing spatial and temporal granularity, high human and material costs, and a mismatch in study scope due to data-driven rather than demand-driven approaches. In recent years, active urban sensing methods have emerged as more flexible approaches that can adapt to varying demands. Three sensing paradigms — stationary sensing, mobile sensing and collaborative sensing — have been practiced in research.

This paper proposes a framework for an active urban sensing approach: firstly, it categorises and aggregates literature on active urban sensing techniques, refines monitoring objects and sensing paradigms, and forms an evidence-based metrics library for active urban sensing. Secondly, in order to conclude the application conditions of different sensing paradigms, the metrics are further clustered according to volatility, spatial resolution and spatio-temporal coverage, and five application scenarios are further summarised to form a decision tree for sensing paradigm selection. This framework serves as a valuable reference for data refinement in less developed areas with missing or untimely data updates, as well as developed areas with insufficient data coverage and density, enabling active urban sensing to be applied in a wider range of demand scenarios and contribute to the achievement of SDGs in community research contexts.


Dr John Sweeney resized

Dr John A. Sweeney
United Nations Educational, Scientific and Cultural Organization (UNESCO) Chair for Futures Studies in Anticipatory Governance and Sustainable Policymaking
Westminster International University

Dr John A. Sweeney is the UNESCO Chair for Futures Studies in Anticipatory Governance and Sustainable Policymaking at Westminster International University in Tashkent, Uzbekistan. He also currently serves as Co-Editor-in-Chief of World Futures Review: A Journal of Strategic Foresight. As a practitioner, consultant, and educator, John has organised, managed, and facilitated workshops and seminars, multi-stakeholder projects, and foresight games and simulations in 50 countries on five continents in-person and online with participants from all over the world.


Topic: Re-imagining Urban Futures: How Cities Can Leverage Strategic & Transformative Foresight

In recent years, cities have turned towards forward-looking approaches aimed at creating more resilient policies, plans, and strategies. This talk provides a framework and case studies centered on the usage of foresight within urban contexts with a particular focus on participatory futures tools and methods. From Mexico City to Płock, there are numerous case studies that demonstrate the value and impact of foresight, specifically how to include more citizens and residents in decision-making. Additionally, this talk puts forward a framework for distinguishing between strategic and transformative foresight, which situates the latter as focused more inward with an emphasis on perceptions, hopes, and fears. Designed for an audience with little or no exposure to foresight, this talk offers an introduction to forward-looking approaches within the context of urban planning, policy, and strategy development, design, and delivery. 


Dr Chenyi Cai resized

Chenyi Cai
Postdoctoral Researcher, Semantic Urban Elements team
Future Cities Laboratory (FCL) Global
Singapore-ETH Centre

Dr Chenyi Cai is a Postdoctoral Researcher at the Singapore-ETH Centre’s Future Cities Laboratory Global, Semantic Urban Elements team. Her research interests lie in the interface of computation, AI, and urban design. Her research focuses on computational design, aiming to leverage the interactions between machines and humans for understanding city complexity and creativity in design. At FCL Global, she contributes to computational urban design approaches by formal definitions, based on multi-domain data. She completed her Ph.D. at the Institute of Architectural Algorithms & Applications, Southeast University (China). She was a visiting Ph.D. at the Institute of Technology in Architecture, ETH Zurich. 


Topic: A Case-based Search Engine for Mapping Urban Patterns & Cases Integrating Street View Imagery

A case-based search engine has been developed to collect urban semantic, spatial, and image data, to extract geometry and image features, and with unsupervised learning, the urban patterns can be elucidated. The engine provides users with a city map with general urban patterns and various related urban cases, and can help architects and urban designers with decision-making. Presenter will be using Nanjing as a case study to demonstrate the engine’s applications.


Mr Winston Yap resized

Mr Winston Yap
PhD researcher
National University of Singapore

Winston is a PhD candidate at the National University of Singapore’s Urban Analytics Lab. Before the PhD, he worked as research associate at the Lee Kuan Yew Centre for Innovative Cities, where he researched on citizen urban science in Southeast Asian Megacities. Currently, his research focuses on open-source software, urban machine learning, urban complexity, and network-based analytics. Through his research, he aims to understand how planning technologies can help to support evidence-based planning and design. 


Topic: Urbanity: Automated Modelling and Analysis of Multidimensional Networks in Cities

A network-based Python package, Urbanity, has been developed to automate the construction of feature-rich urban networks anywhere and at any geographical scale. The presentation will discuss data sources, features of the software, and a set of data representing the networks of five major cities around the world. A test of its usefulness was also conducted by classifying different types of connections within a single network. Findings extend accumulated knowledge about how spaces and flows within city networks work, and affirm the importance of contextual features for analysing city networks.


Dr Heiko Aydt resized

Dr Heiko Aydt
Cooling Singapore Head, SEC Digital Twin Lab

Dr Heiko Aydt is the Head of the Singapore-ETH Centre (SEC) Digital Twin Lab and a Lead Investigator in the Cooling Singapore project, focusing on building a digital urban climate twin for Singapore. He holds a PhD in Computer Science and an MSc in Software Engineering of Distributed Systems. He has extensive experience in modelling and simulation, high-performance computing and cloud computing. At SEC, he coordinated the Responsive Cities scenario and played a vital role in the Cooling Singapore project since its inception in 2017. He previously worked at NTU and TUMCREATE, specialising in simulation-based optimisation and agent-based traffic simulation. 


Topic: A Digital Urban Climate Twin of Singapore to analyse Green Plan 2030 scenarios

Demonstrating the DUCT climate model, which integrates anthropogenic heat emission meso-scale data (from time-resolved building, traffic, industry and power plants) with 16 local climate zones (based on vegetation and land use). Contribution of different factors to the urban heat is evaluated, how these are translated into SG Green Plan scenarios, and thus the impact of these scenarios on the urban heat.


Mr Dake Wu resized

Mr Dake Wu
Master Researcher, Physics Department
National University of Singapore (NUS)

Dake Wu is a master researcher from the physics department at the National University of Singapore. His interest centers around complex systems, particularly complex network research. With a solid knowledge base in physics and an interdisciplinary outlook, Dake Wu aims to contribute fresh perspectives on the real-world complex networks. His work on urban traffic delves into the dynamics driving the propagation of traffic congestion on the urban road networks, and is promising in systemically facilitating urban planning and enhancing traffic management strategies.  

Topic: Analysing Systemic Traffic Conditions in Singapore through Epidemic Spreading Models

The empirical dynamics of traffic jams are hypothesised to be similar to epidemic spread spatial-temporal patterns, where slow vehicle flow in a road segment is an ‘infected’ node, while a high-speed flow road segment is a ‘susceptible’ node. The fit of traffic jam spread dynamics to the conventional Susceptible-Infected-Recovered (SIR) was compared with the modified Susceptible-Infected-Susceptible (SIS) model, where the interaction between an emerging jam ‘infection’ and an existing jam infection is accounted for. It was found that existing traffic dynamics in Singapore operate well below the ‘percolation threshold’, reflecting the current situation where no traffic jams have emerged over a large area. Traffic jams here fit better to the SIS model, suggesting that traffic jam dynamics evolve constantly from preceding network topology. If combined with land use and geo-tagged population data, such a systemic approach could be used as a framework to inform potential systemic traffic conditions, thereby benefitting urban land usage planning scenarios.  


Dr Sam Conrad Joyce resized

Dr Sam Joyce
Assistant Professor
Singapore University of Technology and Design (SUTD)

Dr Sam Conrad Joyce is the head of Meta Design Lab, a interdisciplinary research group based in Singapore working on AI and informatics applied to urban and architectural design. Focusing on leveraging real world data to help understand the present and plan for the mid to long term future. Specialising on methods involving, analytics, machine learning, novel senor information, data visualisation, and generative design. He also acts as Associate Professor at the Singapore University of Technology and Design with a join appointment in the Design AI and Architecture and Sustainable Design Pillar. 


Topic: Understanding Active Mobility using Computer Vision and Data Visualisation

Machine learning over video captures and spatiotemporal data visualisation techniques was deployed to understand how different user groups behave and interact at PCNs. The impacts of PCN design on their behaviour was also elucidated. Compliance of road markings was found to be higher in cyclists than pedestrians, and more dangerous PMD behaviour was linked to times of low pedestrian density. Advantages, challenges, and limits of computer vision technology in understanding spatiotemporal use patterns and identifying potential areas of conflicts will be discussed.