Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

Abstract

Sustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

Data-Driven Smart Eco-Cities of the Future: An Empirically Informed Integrated Model for Strategic Sustainable Urban Development

“…This is owing to the core enabling and driving technologies of the IoT and big data analytics offered by smart cities in relation to sustainability (e.g. Angelidou et al, 2018;Bibri, 2019bBibri, , 2020dBibri, , 2021aBibri, , 2021bBibri, , 2021cBibri, , 2021dBibri & Krogstie, 2020b;Nikitin et al, 2016;Noori et al, 2020;Perera et al, 2017;Silva et al, 2018;St€ ubinger & Schneider, 2020;Trencher, 2019;Zawieska & Pieriegud, 2018;Zhuravleva et al, 2019). In particular, the key role of smart cities lies in connecting the aforementioned infrastructures of existing eco-cities.…”

5G Networks Towards Smart and Sustainable Cities: A Review of Recent Developments, Applications and Future Perspectives

“…The reason is that 5G systems are superior to 4G systems in terms of data rate, latency, mobility, energy efficiency, spectrum efficiency, area traffic capacity, and connection density. However, 5G performance suffers from some drawbacks such as the issues of the small packet size [127], which is considered as a limiting element for increased data rates. Further, the aid for sensing things and communications is confined in 5G networks and additional enhancements are required in this domain [128].…”

Evidence-Based Public Policy Decision-Making in Smart Cities: Does Extant Theory Support Achievement of City Sustainability Objectives?

“…Benefit is achieved because the rationality inherent in evidence-based decisions avoids, or at least reduces, the policy failures which are rooted in the ideological dimension of the policy process [14]. The evidence-based approach is held to ensure that policies respond to the real needs of citizens, that issues and problems requiring immediate attention are addressed, and that information sharing amongst different stakeholders is enabled [17]. In the context of public administration, the benefits of making policy decisions using evidence are held to be wide, direct, and indirect.…”

“…Smart cities are smart, not only in the way in which their governments harness technology [17,20] but in the way that they monitor, analyze, plan [2], and govern the city [21,22]. Evidence-based decision-making is identified by Gil-Garcia et al (2016) as a key dimension of smartness, being enabled and supported by real-time data collection and sharing, analytics, and city business processes [23].…”

“…Going beyond big data to the wider context of utilizing data-driven technologies presents perils and pitfalls as to the actual planning and decision-making where the choice and application of data can potentially remain selective, flawed, biased, normative, and politically inflected [17]. These complexities require a range of other instruments, policies, and practices that are sensitive to the diverse ways in which each city is structured and functions [4].…”