Seventy percent of the world’s population will live in cities by 2050, and this large number makes urban planning more difficult. As a result, planners have turned to technology, most recently generative artificial intelligence, to help design, analyze and develop dense areas.
Enthusiasts envision urban planners using AI to review development proposals, analyze proposed zoning changes, and develop new city master plans or improve existing ones.
In one recent test case, Virginia Tech professors used generative AI to determine the walkability of an area using AI tools to analyze images of built environment features such as benches, streetlights, and sidewalks. To the extent that AI can take on such simple, but labor-intensive, tasks, urban planners may have increased bandwidth to work on more complex problems facing cities — problems like affordable housing, climate change, and the decline of the office sector.
However, incorporating generative AI into the digitization of urban planning, also known as “PlanTech,” is not without challenges, and the question remains: Can AI provide enough value to justify its use?
The cost of building and operating AI infrastructure is enormous, both in monetary and environmental terms. If generative AI can only solve small problems, not large ones, municipalities may wonder whether the expense is worth it. Also, in light of their field’s long and tangled history when it comes to inequality, urban planners may be particularly sensitive to concerns about biased training data leading to biased generative AI models.
Have past technological developments improved cities?
Despite PlanTech’s enormous efficiency gains, it is sometimes viewed as part of a constellation of “cool” but eccentric applications that improve certain aspects of urban life but fail to solve real problems, such as public health crises and rising housing costs.
One of the first large-scale attempts to integrate cutting-edge technologies into modern urban planning was the emergence of “smart cities” in the early 2000s. Smart cities use information and communications technology (ICT), such as 3D imaging and information modeling, to improve the quality of urban services. San Francisco, for example, has implemented a smart waste management system that uses sensors and Internet-connected devices to improve waste collection and disposal.
While smart cities’ use of technology has led to efficiency gains, it is not clear that this translates into improved quality of life for their citizens. After the Covid-19 pandemic, academics wanted to know whether the smartest cities were doing better at managing the pandemic. they I looked at the municipalities that ranked highly in the “smart city” indicators. Such as environment, mobility, urban planning, and transportation, and they concluded that higher-ranking cities were not necessarily able to manage the pandemic better.
There are also concerns that smart cities’ focus on modeling and algorithms may harm aspects of urban life that are not easy to quantify.
A more recent wave of technological innovation in urban planning involves a concept called “digital twins,” which are real-time virtual models of urban areas, ranging from a building to an entire city. Just like how NASA uses digital spacecraft simulators to train astronauts and mission control crews, these dual digital simulations allow urban planners to test their designs and land use plans before implementing them.
Municipalities can use digital twins to explore the impact of natural disasters, such as 100-year floods or extreme heat events, and develop a response. Using a digital twin, it is possible to design new buildings or areas and test them under many different scenarios before creating the actual development.
While digital twins hold the promise of anticipating future challenges and enabling planners to develop flexible solutions, some obstacles stand in the way of their widespread adoption. Among the most difficult challenges is the difficulty of developing and maintaining a digital twin simulation. These simulations often require a huge amount of data, which is extracted from a wide range of sources and stored in formats that are not necessarily compatible.
The larger and more complex the area being simulated, the more difficult it becomes to integrate all the necessary data, let alone update it. Additionally, as with smart cities, there is always the concern that not all aspects of the urban landscape can be measured and linked to a model.
The need for human capital
The market for advanced technological tools for urban planning is expected to grow, as has happened with the development of artificial intelligence. Although these technologies may help urban planners, they are unlikely to replace them.
Urban planners are not to be confused with technocrats. Planners are tasked with improving the lives of city dwellers, which requires an interdisciplinary approach that includes not only the basic details of land-use decision-making, but also social science, ethics, and public health. The planning profession is likely to face further technological disruption in the future. To remain relevant, they need to embrace complexity and not settle for low, pending short-term gains in efficiency.
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