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

• Autor: Bibri, Simon Elias
• Assuntos: Artificial intelligence ; Big Data ; Case studies ; Changing environments ; Cities ; Climate change ; Complex systems ; Complexity ; Computation ; Computer Appl. in Social and Behavioral Sciences ; Data analysis ; Data science ; Decision support systems ; Earth and Environmental Science ; Environmental changes ; Geographical Information Systems/Cartography ; Geography ; Innovations ; Intelligence ; Landscape/Regional and Urban Planning ; Literature reviews ; Optimization ; Original Paper ; Quality of life ; Science ; Simulation ; Simulation models ; Smart cities ; Strategic planning ; Sustainability ; Sustainability science ; Sustainable design ; Urban areas ; Urban planning ; Urbanism
• É parte de: Computational urban science, 2021-05, Vol.1 (1), Article 8
• Descrição: 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.
  
• Editor: Singapore: Springer Singapore
  
• Idioma: Inglês