Seventy percent of the world’s population will live in cities by 2050, and this huge number makes urban planning a bigger challenge. As a result, planners have turned to technology, most recently generative artificial intelligence, to help design, analyze and develop congested areas.
Enthusiasts envision urban planners using artificial intelligence to review development proposals, analyze proposed zoning changes, and develop new city master plans or optimize existing ones.
In one recent test case, Virginia Tech professors used generative AI to determine the walkability of an area by using an AI tool to analyze images for features of the built environment such as benches, street lights, 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 the more complex problems that cities face—problems such as affordable housing, climate change, and the declining office sector.
The integration of generative artificial intelligence into the digitization of urban planning, also known as “PlanTech”, is not without its challenges, however, and the question remains: can artificial intelligence offer enough value to justify its use?
The costs of building and operating an AI infrastructure are huge, both financially and environmentally. If generative artificial intelligence can only solve small problems and not big ones, then municipalities may wonder if the expenditure is worthwhile. Also, in light of their field’s long, complicated history of inequality, urban planners may be particularly sensitive to concerns about biased training data leading to biased generative AI models.
Have previous technological advances improved cities?
Despite the vast improvements in efficiency achieved by PlanTech, it is sometimes seen as part of a constellation of “cool” but jokey apps 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 widespread attempts to integrate cutting-edge technologies into modern urban planning was the rise of “smart cities” in the early 2000s. Smart cities use information and communication 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 optimize waste collection and disposal.
While the use of technology in smart cities has led to increased efficiency, it is unclear whether this will translate into an improved quality of life for their citizens. After the COVID-19 pandemic, academics wanted to find out if the smartest cities were better at managing the pandemic. They they looked at municipalities that are highly ranked according to “smart cities” 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 are also concerns that smart cities’ focus on modeling and algorithms may disadvantage those aspects of urban life that are not easily quantified.
A more recent wave of technological innovation in urban planning includes a concept called “digital twins,” which are real-time virtual models of urban areas, from the building to the entire city. Much like 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 they are implemented.
Municipalities can use digital twins to investigate the impact of natural disasters, such as a 100-year flood or extreme heat, and develop a response. By using a digital twin, it is possible to model new buildings or regions and test them under many different scenarios before the actual development is built.
While digital twins hold the promise of anticipating future challenges and enabling planners to develop resilient solutions, some obstacles stand in the way of widespread adoption. Among the most challenging is the difficulty in developing and maintaining a digital twin simulation. These simulations often require vast amounts of data pulled from a wide range of sources and stored in formats that are not necessarily compatible.
The larger and more complex the region being simulated, the more challenging it is to integrate all the necessary data, let alone keep it up-to-date. Additionally, as with smart cities, there is always the concern that not all aspects of the urban landscape can be quantified and included in the model.
The need for human capital
The market for advanced technology tools for urban planning is expected to grow, as has happened with the development of artificial intelligence. While these technologies can help urban planners, they are unlikely to replace them.
Urban planners should not be confused with technocrats. Planners are tasked with improving the lives of city dwellers, which requires a multidisciplinary approach that encompasses 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 stay relevant, it needs to embrace complexity and not settle for low short-term efficiency gains.
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