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AI and data-driven urbanism: The Singapore experience
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Type
Article
Citation
Das, D., & Kwek, B. (2024). AI and data-driven urbanism: The Singapore experience. Digital Geography and Society, 7, Article 100104. https://doi.org/10.1016/j.diggeo.2024.100104
Abstract
This paper presents a deep and critical analysis of Singapore's new wave of state-built digital tools and services and how it connects to its larger smart urbanism project, also known as Smart Nation. The COVID-19 pandemic, and particularly Singapore's response, served as a real-world testing ground for smart urbanist strategies. In particular, we analysed the logic that emanates from these novel digital interventions, how they operate on the complex urban built environment and the population, and their effects on urban and citizenry morphologies. Next, we examined a series of state-led technological implementations that have emerged since the Covid-19 pandemic, providing digital solutions that assist citizens with the changing rhythms of everyday living, data-capturing sensors and gantries to aid authorities in contract tracing efforts and enforce vaccination differentiation measures, geospatial digital mapping of demographic data, in withal robotics for automated policing and cleaning activities; and the use of AI and automated data-driven tools in public health to improve service delivery and care to patients. While we are unable to exhaust every piece of technology for the purpose of this paper, these developments, along with their design thinking and operations, we argue, are helpful in revealing the contemporary conjectures of Singaporean digital urban idealism and the governing strategies of the state. By examining Singapore's response, this study aims to contribute to the ongoing discourse on smart urbanism, offering insights into how cities can leverage technology effectively while balancing technological innovation with privacy and public trust.
Date Issued
2024
Publisher
Elsevier
Journal
Digital Geography and Society
DOI
10.1016/j.diggeo.2024.100104
Description
The open access publication is available at https://doi.org/10.1016/j.diggeo.2024.100104