This story is part of  Planning's Disruptors series, a year-long look at the trends, challenges, and opportunities driving change in our communities.
Whether it is a new vaccine, the next must-have smartphone app, or even a plant-based burger, scientists and developers take advantage of the ability to refine a product in the lab before testing it in the real world and, ultimately, selling it to the customer. In IT, developers work in virtual testing environments called sandboxes to experiment and evaluate software programs and identify potential issues, like threats to cybersecurity, before they arise.
In fact, when most industries set out to develop a new product or solve a problem, they rely on using laboratories, testbeds, or other workspaces to refine and optimize their ideas. But that kind of experimentation and testing is simply not a part of standard Planning processes. Could planners use a sandbox in our toolbox? Might smart city digital twins (SCDT) be the answer?
The conversation about experimentation in the planning world can get a little complicated. Sticking to the familiar way of doing things is often the path of least resistance as we balance the needs of residents, stakeholders, and local officials. Yet many plans and policies would benefit from undergoing a test stage before implementation. Up until now the options to do so have been fairly limited.
The concept of using predictive technology in planning is not new. Planners already run traffic simulations to understand transportation demand or factor in energy use patterns to predict future greenhouse gas emissions. But these are often disjointed attempts that don't account for the fact that cities are complex, meaning policy decisions in one area might end up impacting the built environment of another area. Planners need a more integrated way of simulating the complex potential futures of their cities.
Especially since the dawn of the smart city era, planners have been using our own communities as labs, piloting projects and programs on select city streets or in certain districts. But those urban labs aren't actually laboratories — a controlled space for experimenting — at all. They are often actual neighborhoods where real people live, work, play, and are vulnerable. When planners experiment in these labs, there are real-world consequences.
Prototyping ideas, then learning and adjusting can greatly improve outcomes. A recent edition of PAS QuickNotes on design thinking illustrates this idea in the planning field. When done thoughtfully, it can expand public engagement, facilitate community acceptance, and reduce financial risks. Yet as planners, we haven't had a space where we can holistically experiment within the context of an integrated urban system without having to face real-world consequences.
Smart city digital twins promise to change that. SCDT allow planners to explore new solutions to urban problems, improve planning activities such as public engagement and zoning, and address complex issues like climate resilience — all in a controlled environment that mimics the real city — while also offering a viable tool for telling a city's stories and communicating plans, policies, and outcomes.
The term "digital twin" has existed since the early 2000s. Beginning with applications in manufacturing and construction, various industries have since come to define the term in their own contexts. According to IBM, a digital twin "is a virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning and reasoning." Siemens adds to that the ability of a digital twin "to simulate, predict, and optimize the product and production system before investing in physical prototypes and assets."
In the planning context, we are mainly talking about digital twins of entire cities.
