Urban digital twins – missing pieces and emerging divides

Injecting human behaviour

Machines and equipment can be modelled according to physical formulas and accumulated sensor data that capture real-time and real-world behaviour. The same is true for electricity and water networks, for example. These systems are complicated but can be modelled in theory.

Complicated systems behave in predictable ways. Complex systems, in contrast, will behave differently each time, in part because of human behaviour that can change according to many influences. Most digital twins tend to omit human behaviour, while others treat human behaviour as predictable placeholders – in a way, they mechanise human behaviour. But human behaviour and interactions are of crucial importance in simulating dynamics for digital twins for cities and urban environments – after all, that’s what cities and communities are ultimately created for.

Farzin Lotfi-Jam, assistant professor at Cornell University’s College of Architecture, studies the use of technologies to govern cities. He is the director of Cornell University’s Realtime Urbanism Lab, which “investigates the impacts of new technologies that virtualise cities and populations”.

He says: “The global proliferation of urban digital twin models compels a research agenda that investigates the intertwined social, political and technical dimensions of their development, from design to use in planning and governance. In each of these digital twinning concepts is a concept of what a city is. I noticed, looking at all of these, that there’s no people anywhere in any of these concepts.”

A research field far removed from Lotfi-Jam interests could potentially add guidance in populating digital twins for cities. Tianyi Peng, assistant professor in the decision, risk and operations division at Columbia Business School, is looking at the use of AI for decision making. His research looks at what can be used to generate AI agents that mimic human behaviour, such as that in the context of market decisions like shopping preferences and reactions to product stimuli.

The current use of digital twins for urban environments is limited for the lack of realistic representations of humans and their actions and interactions. It is easy to see how the study of individual behaviour and the simulation of group interactions will find use in city digital twins over time.

Peng’s colleague Olivier Toubia, professor of business at Columbia Business School, who is investigating interactions between AI-generated behaviour and how these interactions affect collective behaviour, “combines methods from social sciences and data science to study human processes such as motivation, choice, and creativity”.

Meanwhile, Lydia Chilton, associate professor of computer science at Columbia University School of Engineering & Applied Sciences, is contributing research into how AI agents in simulated environments can mimic unique behaviours of human interactions that can be unpredictable.

Providing comprehensive data

Mutualistic technologies offer ways looks at the wider set of technologies that interact with each other with impact on digital twins and robotics. The emerging network of mutualistic technologies features AI and sensors as the glue that creates positive feedback loops between these technologies. Data is needed to create realistic representations and relevant interactions between virtual and real world. Many times, real-world data can be difficult or expensive to extract though. Then synthetic data can find use. Synesthetic data can come from simulations in digital twins or from AI-based applications.

Commercial relevance of capturing comprehensive data is substantial, particularly for digital twins for urban environments where data from many interdependent networks require inclusion to realistically mirror activities and interactions of systems in cities. Road networks affect traffic patterns and public transportation impacts how people move through cities and therefore where businesses spring up.

Electricity networks, gas distribution, water and sewage lines crisscross urban maps and affect what neighbourhoods might lose power first during outages or which areas are prone to flooding. And flooding can affect power outages, which then can affect public transportation’s reliability, and so on. A very comprehensive view of urban activities is required to visualise interdependencies.

One of the general hurdles to effective and efficient city management are the silos in which urban networks and services operate. Data cannot easily connect; platform and format issues prevent seamless interfacing between systems, thereby posing genuine hurdles to all-encompassing digital twins that can truly capture and reflect the operational, commercial and social ongoings within cities. Therefore, a first step to creating genuinely beneficial urban digital twins often is a rather mundane, administrative step. Collaboration between administrations and agencies is key and the need for compatible data is crucial.

The city of San Diego’s managers realised the importance of such collaboration and created a partnership between the San Diego Association of Governments; the San Diego Regional Economic Development Corporation; San Diego State University; University of California, San Diego; and industrial partners. Interconnected dynamics and challenges in urban environments require connected, relatable data and digital twins that can represent resulting complexities – the collaboration of city administrations and network users is the first step.

Cautioning against developing communal divides

Digital twins will transform the way we plan, design, operate and maintain equipment, networks and urban environments. AI will accelerate their development, improve their performance and enhance their usability. But on the road to ubiquitous use, hurdles and issues need overcoming – some considerations generally associated with virtualisation technologies and use, others relate to AI, which currently is experiencing almost unchecked excitement and investment.

The Brookings Institution recently highlighted the emerging industrial and geographic unevenness of implementing and leveraging AI. The diffusion of AI will occur on different timelines in various industrial sectors. Spending on AI will depend on productivity and economic growth that companies and industries will expect or experience. Available investment capital and shareholder agreement will also play a role.

While it is natural that technology-related companies and finance, logistics and manufacturers firms already foresee substantial changes to their operations, agriculture, mining, personal services (including some aspects of healthcare) and many government services will see less immediate application opportunities. AI’s use for digital twins of cities will therefore initially create uneven representation in various sectors of urban planning and management.

Existing disparities between countries and regions will create geographical unevenness in the use of AI, and therefore in the adoption and diffusion of AI-empowered digital twins. Research by the Brookings Institution states: “Artificial intelligence is transforming the US economy, yet regional disparities in talent development, research capacity and enterprise adoption are stark and not yet fully understood.”

The digital divide emerged as a major concern at the end of the 1990s. Although the effects did not pan out as dramatic as some observers initially warned, the Covid pandemic from 2020 and following years highlighted unevenness in the way individuals, regions and entire countries were able to move personal and commercial activities online. Geographical laggards could develop in which AI implementation is slow, leaving other regions to charge ahead.

The report continues: “Such gaps and deficits may result in unrealised opportunities for productivity growth across disparate industries, and limit discovery and dissemination of the full range of AI use cases. For that matter, disparities in AI readiness may leave some communities to fall behind or slump into ‘development traps’. Imbalances in AI talent, innovation infrastructure and business adoption very well could decide which people and places will prosper in the future – and which will not.”

Internationally, such gaps can lead to “geo-algorithmic inequality”. Digital twins that replicate commercial activities, urban environments, entire ecosystems and eventually even economies as a whole will support the development of climate-resilience strategies, affect investment flows and establish the foundation for regional development plans in developed countries and metropolises.

“By contrast, much of Africa, the Caribbean and parts of Southeast Asia remain invisible in major digital twin ecosystems,” says the report. Data availability is spotty, often non-existent. Therefore, “decisions around infrastructure aid, disaster prevention or carbon offsetting are made with incomplete information – or without them in mind at all”.

Thinking globally, acting locally

The impact on these regions can be dramatic. Geo-algorithmic inequality results in “the uneven inclusion of countries and communities in the simulations that shape global policy, investment, and resilience planning”, according to Brookings Institution.

The effect can cascade towards digital twins that attempt to simulate the global ecosystem. In such virtualisations of the entire planet, structural bias can encode misrepresentations in digital twins and therefore distort resulting applications.

Potential approaches to alleviate such concerns exist. The Gaia-X initiative is looking to establish digital sovereignty by establishing “an ecosystem, whereby data is shared and made available in a trustworthy environment”. And the World Avatar effort is working on an “ecosystem of tools and services that can be used to create an individual digital twin, or network of connected digital twins, to provide a platform of data and model interoperability”.

Data silos of networks or country initiatives can then easily connect to each other. Although laudable, a concerted policy framework is needed to create incentives for corporations and organisations to buy into and fully embrace such efforts.

Martin Schwirn is the author of Small data, big disruptions: How to spot signals of change and manage uncertainty (ISBN 9781632651921) on foresight and horizon scanning. He is a strategy and innovation consultant for Global 2000 companies.