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      The Sciences Underlying Smart Sustainable Urbanism: Unprecedented Paradigmatic and Scholarly Shifts in Light of Big Data Science and Analytics

      Smart Cities
      MDPI AG

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          Abstract

          As a new area of science and technology (S&T), big data science and analytics embodies an unprecedentedly transformative power—which is manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are understood, studied, planned, operated, and managed to improve and maintain sustainability in the face of expanding urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach that is based on a computational understanding of city systems that reduces urban life to logical and algorithmic rules and procedures, as well as employs a new scientific method based on data-intensive science, while also harnessing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This paper examines the unprecedented paradigmatic and scholarly shifts that the sciences underlying smart sustainable urbanism are undergoing in light of big data science and analytics and the underlying enabling technologies, as well as discusses how these shifts intertwine with and affect one another in the context of sustainability. I argue that data-intensive science, as a new epistemological shift, is fundamentally changing the scientific and practical foundations of urban sustainability. In specific terms, the new urban science—as underpinned by sustainability science and urban sustainability—is increasingly making cities more sustainable, resilient, efficient, and livable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, and development.

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          Dilemmas in a general theory of planning

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            A manifesto for reproducible science

            Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research.
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              Smart cities of the future

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                Author and article information

                Journal
                Smart Cities
                Smart Cities
                MDPI AG
                2624-6511
                June 2019
                May 23 2019
                : 2
                : 2
                : 179-213
                Article
                10.3390/smartcities2020013
                55339924-d1e5-4181-a219-92ddf20ee06d
                © 2019

                https://creativecommons.org/licenses/by/4.0/

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