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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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“Where Am I?”: Unraveling Challenges in Smart City Data Cleaning to Establish a Ground Truth Framework
In the growing era of smart cities, data-driven decision-making is pivotal for urban planners and policymakers. Crowd-sourced data is a cost-effective means to collect this information, enabling more efficient urban management. However, ensuring data accuracy and establishing trustworthy “Ground Truth” in smart city sensor data presents unique challenges.Our study contributes by documenting the intricacies and obstacles associated with overcoming MAC randomization, sensor unpredictability, unreliable signal strength, and Wi-Fi probing inconsistencies in smart city data cleaning.We establish a framework for three different types of experiments: Counting, Proximity, and Sensor Range. Our novel approach incorporates the spatial layout of the city, an aspect often overlooked. We propose a database structure and metrics to enhance reproducibility and trust in the system.By presenting our findings, we aim to facilitate a deeper understanding of the nuances involved in handling sensor data, ultimately paving the way for more accurate and meaningful data-driven decision-making in smart cities.
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- Award ID(s):
- 1853953
- PAR ID:
- 10575525
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-0436-7
- Page Range / eLocation ID:
- 649 to 654
- Format(s):
- Medium: X
- Location:
- Biarritz, France
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PerComWorkshops59983.2024.10503068
