Over the past several decades, urban planning has considered a variety of advanced analysis methods with greater and lesser degrees of adoption. Geographic Information Systems (GIS) is probably the most notable, with others such as database management systems (DBMS), decision support systems (DSS), planning support systems (PSS), and expert systems (ES), having mixed levels of recognition and acceptance (Kontokosta, C. E. (2021). Urban informatics in the science and practice of planning. Journal of Planning Education and Research, 41(4), 382–395. doi:10.1177/0739456X18793716; Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473). Advances in information technologies have moved very slowly in the field of urban planning, more recently concerning ‘smart city’ technologies while revolutionizing other domains, such as consumer goods and services. Baidu, Amazon, Netflix, Google, and many others are using these technologies to gain insights into consumer behaviour and characteristics and improve supply chains and logistics.
This is an opportune time for urban planners to consider the application of AI-related techniques given vast increases in data availability, increased processing speeds, and increased popularity and development of planning related applications. Research on these topics by urban planning scholars has increased over the past few years, but there is little evidence to suggest that the results are making it into the hands of professional planners (Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6; Batty, M. (2021). Planning education in the digital age. Environment and Planning B: Urban Analytics and City Science, 48(2), 207–211). Others encourage planners to leverage the ubiquity of data and advances in computing to enhance redistributive justice in information resources and procedural justice in decision-making among marginalized communities (Boeing, G., Besbris, M., Schachter, A., & Kuk, J. (2020). Housing search in the Age of Big data: Smarter cities or the same Old blind spots? Housing Policy Debate, 31(1), 112–126; Goodspeed, R. (2015). Smart cities: Moving beyond urban cybernetics to tackle wicked problems. Cambridge journal of regions, Economy and Society, 8(1), 79–92). This article highlights findings from a recent literature review on AI in planning and discusses the results of a national survey of urban planners about their perspectives on AI adoption and concerns they have expressed about its broader use in the profession. Currently, the outlook is mixed, matching how urban planners initially viewed the early stages of computer adoption within the profession. And yet today, personal computers are essential to any job.
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Urban Science: Putting the “Smart” in Smart Cities
Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities. Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems. Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize.
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- Award ID(s):
- 1636936
- NSF-PAR ID:
- 10124079
- Date Published:
- Journal Name:
- Urban Science
- Volume:
- 2
- Issue:
- 4
- ISSN:
- 2413-8851
- Page Range / eLocation ID:
- 94
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/urbansci2040094
