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 creative AI, to help design, analyze and develop crowded areas.
Fans envision urban planners using AI to review development proposals, analyze proposed zoning changes, and develop new urban master plans or optimize existing ones.
In a recent test, professors at Virginia Tech used creative AI to determine the walkability of an area using AI tools to analyze images for features of the built environment, such as benches, streetlights, and sidewalks. As AI takes on simple but labor-intensive tasks, urban planners could perhaps increase their bandwidth to work on the more complex problems cities face: the problems of affordable housing, climate change and the decline of the office sector.
Integrating generative AI into the digitization of urban planning, also known as “PlanTech,” is not without its challenges, and the question remains: Can AI provide enough value to justify its use?
The cost of building and running AI infrastructure is enormous, both financially and environmentally. If the AI creator only solves small problems, not big ones, then municipalities may question whether these expenses are worth it. Also, given their field’s long and complicated history of disparities, urban planners may be particularly sensitive to concerns about biased training data that could lead to biased AI models.
Have previous technological advances improved cities?
Despite PlanTech’s dramatic efficiency gains, it is sometimes perceived as part of a constellation of “cold” but juicy applications that improve certain aspects of city life but don’t solve real problems, such as public health crises and rising housing costs.
One of the first attempts to incorporate cutting-edge technologies into modern urban planning was the rise of “smart cities” in the early 2000s. Smart cities use information and communication technologies (ICT), such as 3D imaging and information modeling, to improve the quality of city services. San Francisco, for example, has implemented a smart waste management system that uses sensors and internet-connected devices to optimize waste collection and disposal.
Although the use of technology by smart cities has led to efficiency gains, it is not clear that this improves the quality of life for citizens. In the wake of the COVID-19 pandemic, academics wanted to know whether the smartest cities performed better in managing the pandemic. They It analyzed the municipalities that ranked first in the “smart city” indicators such as the environment, mobility, urban planning and transport, and concluded that the top-ranked cities did not necessarily manage the pandemic better.
There is also concern that smart cities’ focus on modeling and algorithms may harm aspects of urban life that are not easy to measure quantitatively.
A recent wave of technological innovation in urban planning involves the concept of “digital twins”, which are real-time virtual models of urban areas, from a building to an entire city. Similar to how NASA uses digital spacecraft simulators to train astronauts and mission control crews, these digital twin simulations allow urban planners to test their designs and land use plans before implementation.
Municipalities can use digital twins to analyze the impact of natural disasters, such as a 100-year flood or extreme heat events, and develop a response. Using a digital twin, new buildings or regions can be modeled and tested in various scenarios before the actual development is built.
While digital twins promise to anticipate future challenges and enable planners to develop resilient solutions, some obstacles stand in the way of widespread adoption. Among the biggest challenges is the difficulty of developing and maintaining a digital twin simulation. These simulations often require large amounts of data, drawn from many sources and stored in formats that are not necessarily compatible.
The larger and more complex the region being simulated, the more difficult it is to integrate all the necessary data, let alone keep it up-to-date. Also, as with smart cities, there is always the concern that not all aspects of the urban landscape can be quantified and plugged into a model.
The need for human capital
The market for advanced technological tools for urban planning is expected to continue to grow, as has happened with the development of AI. While these technologies can help urban planners, they are unlikely to replace them.
Urban planners are not confused with technocrats. Planners are tasked with improving the lives of city dwellers, which requires a multidisciplinary approach that includes not only the nuts and bolts of land use decision-making, but also social science, ethics, and public health. The planning profession is likely to face more technological disruptions in the future. To remain relevant, it must embrace complexity and not settle for low short-term efficiency gains.
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