| Abstract |
There has recently been much enthusiasm about the immense opportunities and fascinating possibilities created by the unfolding and soaring deluge of exhaustive, fast, indexical data and its new and extensive sources as to understanding, analyzing, and planning smart sustainable/sustainable smart cities in ways that improve, advance, and maintain their contribution to the goals of sustainable development. This is owing to the underlying power of thinking data-analytically about sustainability in terms of finding answers to challenging questions for addressing the wicked problems and disentangling the intractable issues related to the practice of urbanism: operational functioning, planning, design, and development. In the meantime, as widely acknowledged within the field of smart and sustainable urbanism as regards academic and scientific research, ‘small data’ studies are associated with high cost, infrequent periodicity, quick obsolescence, incompleteness, inaccuracy, as well as inherent subjectivity and biases. In addition, such studies capture a relatively limited sample of data that is tightly focused, less representative, restricted in scope and scale, time and space specific, and relatively expensive to generate and analyze. Indeed, much of our knowledge of urbanism has been gleaned from scholarly studies characterized by data scarcity and involving the use of traditional data collection and analysis methods with inherent limitations and constraints. Therefore, this chapter endeavors to develop, illustrate, and discuss a systematic framework for city analytics and ‘big data’ studies in relation to the domain of smart sustainable/sustainable smart urbanism based on cross-industry standard process for data mining. This endeavor is in response to the emerging paradigm of big data computing and the increasing role of underpinning technologies in operating, organizing, planning, and designing smart sustainable cities as a leading paradigm of urbanism. The intention is to utilize and apply well-informed, knowledge-driven decision-making and enhanced insights to improve and optimize urban operations, functions, services, designs, strategies, and policies in line with the long-term goals of sustainability. I argue that there is tremendous potential for advancing smart sustainable urbanism or transforming the knowledge of smart sustainable cities through creating a data deluge that can, through analytics, provide much more sophisticated, finer-grained, wider-scale, real-time understanding and control of various aspects of urbanity in the undoubtedly upcoming Exabyte/Zettabyte Age. © 2019, Springer Nature Switzerland AG. |
