Abstract
We are moving into an era where instrumentation, datafication, and computation are routinely pervading the very fabric of the city as a complex system and dynamically changing environment, and vast troves of contextual and actionable data are being generated and used to control, manage, regulate, and organize the urban life. At the heart of this emerging era of data-driven urbanism is a computational understanding of urban systems and processes that reduces urban life to a set of logic, calculative, and algorithmic rules and procedures. Such understanding entails drawing together, interlinking, and analyzing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This is being increasingly directed for improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. Indeed, a new era is presently unfolding wherein smart sustainable urbanism is increasingly becoming data-driven. In light of this, smart sustainable urbanism has become even more complex with the very technologies being used to make sense of and deal with it as involving special conundrums, wicked problems, intractable issues, and complex challenges associated mainly with sustainability and urbanization. Consequently, to tackle smart sustainable cities requires, I contend, innovative solutions and sophisticated approaches as to the way they can be monitored, understood, and analyzed so as to be effectively operated, managed, planned, designed, developed, and governed in line with the long-term goals of sustainability. Therefore, this chapter examines and discusses the approach to data-driven smart sustainable urbanism in terms of computerized decision support and making, intelligence functions, simulation models, and optimization and prediction methods. It also documents and highlights the potential of the integration of these advanced technologies for facilitating the synergy between the operational functioning, planning, design, and development of smart sustainable cities. I argue that data-driven urbanism is the mode of production for smart sustainable cities, which are accordingly becoming knowable, tractable, and controllable in new dynamic ways thanks to urban science and complexity science. I conclude that the upcoming developments and innovations in big data computing and the underpinning technologies, coupled with the evolving deluge of urban data, hold great potential for enhancing and advancing the different practices of smart sustainable urbanism. This work contributes to bringing data-analytic thinking and practice to smart sustainable urbanism, in addition to drawing special attention to the crucial role and enormous benefits of the emerging paradigm of big data computing as to transforming the future form of such urbanism.
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References
Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., & Airaksinen, M. (2017). What are the differences between sustainable and smart cities? Cities 60, 234–245.
Albert, R., & Barabasi, A. L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–94.
Al Nuaimi, E., Al Neyadi, H., Nader, M., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(25), 1–15.
Anderson, P. W. (1972). More is different. Science, 177, 393–396.
Angelidou, M., Psaltoglou, A., Komninos, N., Kakderi, C., Tsarchopoulos, P., & Panori, A. (2017). Enhancing sustainable urban development through smart city applications. Journal of Science and Technology Policy Management, 1–25; Aseem, I. (2013). Designing urban transformation. New York, London: Routledge.
Astrom, K. J., & Murray, R. M. (2008). Feedback systems: An introduction for scientists and engineers. Princeton: Princeton University Press. Available online at www.cds.caltech.edu/*murray/amwiki/index.php/Main_Page.
Batty, M. (2013). The new science of cities. Cambridge: MIT Press.
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., et al. (2012). Smart cities of the future. European Physical Journal, 214, 481–518.
Benham-Hutchins, M., & Clancy, T. (2010). Social networks as embedded complex adaptive systems. JONA, 40(9), 352–356.
Bettencourt, L. M. A. (2014). The uses of big data in cities. Santa Fe, New Mexico: Santa Fe Institute.
Bibri, S. E. (2015). The shaping of ambient intelligence and the internet of things: Historico-epistemic, socio-cultural, politico-institutional and eco-environmental dimensions. Berlin, Heidelberg: Springer.
Bibri, S. E. (2018a). Smart sustainable cities of the future: The untapped potential of big data analytics and context aware computing for advancing sustainability. Germany, Berlin: Springer.
Bibri, S. E. (2018b). The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society, 38, 230–253.
Bibri, S. E. (2018c). A foundational framework for smart sustainable city development: Theoretical, disciplinary, and discursive dimensions and their synergies. Sustainable Cities and Society, 38, 758–794.
Bibri, S. E. (2018d). Backcasting in futures studies: A synthesized scholarly and planning approach to strategic smart sustainable city development. European Journal of Futures Research, 2 of 27.
Bibri, S. E. (2019a). On the sustainability of smart cities of the future and related big data applications: An interdisciplinary and transdisciplinary review and synthesis. Journal of Big Data (in press).
Bibri, S. E. (2019b). A novel model for smart sustainable city of the future: A scholarly backcasting approach to its analysis, investigation, and development. Journal of CITA (in press).
Bibri, S. E., & Krogstie, J. (2016). On the social shaping dimensions of smart sustainable cities: A study in science, technology, and society. Sustainable Cities and Society, 29, 219–246.
Bibri, S. E., & Krogstie, J. (2017a). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183–212.
Bibri, S. E., & Krogstie, J. (2017b). ICT of the new wave of computing for sustainable urban forms: Their big data and context-aware augmented typologies and design concepts. Sustainable Cities and Society, 32, 449–474.
Bibri, S. E., & Krogstie, J. (2017c). The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: A review and synthesis. Journal of Big Data.
Bibri, S. E., & Krogstie, J. (2018). The big data deluge for transforming the knowledge of smart sustainable cities: A data mining framework for urban analytics. In Proceedings of the 3d Annual International Conference on Smart City Applications, October 11–12. Tetouan, Morocco: ACM.
Bifulco, F., Tregua, M., Amitrano, C. C., & D’Auria, A. (2016). ICT and sustainability in smart cities management. International Journal of Public Sector Management, 29(2), 132–147.
Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E., & Havlin, S. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291), 08932.
Campbell, S. (1996). Green cities, growing cities, just cities? Urban planning and the contradictions of sustainable development. Journal of the American Planning Association, 62(3), 296–312.
David, D. (2017). Environment and urbanization. The International Encyclopedia of Geography, 24(1), 31–46. https://doi.org/10.1002/9781118786352.wbieg0623.
Dorogovtsev, S. N., & Mendes, J. F. F. (2003). Evolution of networks: From biological networks to the Internet and WWW, Oxford University Press, ISBN 0-19-851590-1.
Durack, R. (2001). Village vices: The contradiction of new urbanism and sustainability. Places, 14(2), 64–69.
Estevez, E., Lopes, N. V., & Janowski, T. (2016). Smart sustainable cities. Reconnaissance Study, 330.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. Artificial Intelligence Magazine, 17(3), 37–54.
Gao, J., Buldyrev, S. V., Stanley, H. E., & Havlin, S. (2011). Networks formed from interdependent networks. Nature Physics, 8(1), 40–48.
Gianni, D., D’Ambrogio, A., & Tolk, A. (Eds.). (2014). Modeling and simulation-based systems engineering handbook (1st ed.). CRC Press.
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., et al. (2011). Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB of Journal.
Godfrey-Smith, P. (2001). Environmental complexity and the evolution of cognition. In R. C. Sternberg, J. C. Kaufman (Eds.), The evolution of intelligence (pp. 223–250). Mahwah, NJ: Lawrence Erlbaum Associates.
Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG, 189–208.
Han, J., Meng, X., Zhou, X., Yi, B., Liu, M., & Xiang, W.-N. (2016). A long–term analysis of urbanization process, landscape change, and carbon sources and sinks: A case study in China’s Yangtze River Delta region. Journal of Cleaner Production, 141, 1040–1050. https://doi.org/10.1016/j.jclepro.2016.09.177.
Hayek, F. (1978). The results of human action but not of human design. New studies in philosophy, politics, economics (pp. 96–105). Chicago: University of Chicago Press.
Holmberg, J., & Robèrt, K. H. (2000). Backcasting from non-overlapping sustainability principles: A framework for strategic planning. International Journal of Sustainable Development and World Ecology, 7(4), 291–308.
Kahneman, D., & Tversky, A. (Eds.). (2000). Choices, values and frames. New York: Cambridge University Press and the Russell Sage Foundation. The Nature of Computation, Cristopher Moore and Stephan Mertens, Oxford University Press (2011).
Kärrholm, M. (2011). The scaling of sustainable urban form: Some scale-related problems in the context of a Swedish urban landscape. European Planning Studies, 19(1), 97–112.
Kelling, S., Hochachka, W., Fink, D., Riedewald, M., Caruana, R., Ballard, G., et al. (2009). Data-intensive science: A new paradigm for biodiversity studies. BioScience, 59, 613–620.
Kitchin, R. (2014a). The real-time city? Big data and smart urbanism. Geo J, 79, 1–14.
Kitchin, R. (2014b). The data revolution: Big data, open data, data infrastructures and their consequences. London, UK: Sage.
Kitchin, R. (2015). Data-driven, networked urbanism. https://doi.org/10.2139/ssrn.2641802.
Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A, 374, 20160115.
Kitchin, R., Lauriault, T. P., & McArdle, G. (2015). Knowing and governing cities through urban indicators, city benchmarking & real-time dashboards. Regional Studies, Regional Science, 2, 1–28.
Kramers, A., Höjer, M., Lövehagen, N., & Wangel, J. (2014). Smart sustainable cities: Exploring ICT solutions for reduced energy use in cities. Environmental Modelling and Software, 56, 52–62.
Lazer, D., et al. (2009). Computational social science. Science, 323, 721–733. https://doi.org/10.1126/science.1167742.
Majdandzic, A., Podobnik, B., Buldyrev, S. V., Kenett, D. Y., Havlin, S., & Eugene Stanley, H. (2013). Spontaneous recovery in dynamical networks. Nature Physics, 10(1), 34–38.
Majdandzic, A., Braunstein, L. A., Curme, C., Vodenska, I., Levy-Carciente, S., Eugene Stanley, H., et al. (2016). Multiple tipping points and optimal repairing in interacting networks. Nature Communications, 7, 10850.
Nam, T., & Pardo, T. A. (2011). Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Conference on Digital Government Research.
Newman, M. (2010). Networks: An introduction. Oxford: Oxford University Press.
Nielsen, M. (2011). Reinventing discovery: The new era of networked science. Princeton: Princeton University Press.
Padilla, J., Diallo, S. Y., & Tolk, A. (2011). Do we need M&S science? SCS M&S Magazine, 4, 161–166.
Pagliara, F., de Bok, M., Simmonds, D., & Wilson, A. (Eds.). (2013). Employment location in cities and regions: Models and applications. Heidelberg: Springer.
Paley, J., & Gail, E. (2011). Complexity theory as an approach to explanation in healthcare: A critical discussion. International Journal of Nursing Studies, 48, 269–279.
Power, D. J. (2000). Web-based and model-driven decision support systems: Concepts and issues. In: Proceedings of the Americas Conference on Information Systems. Long Beach, California.
Portugali, Y. (2011). Complexity, cognition and the city. Heidelberg: Springer.
Prigogine, I. (1997). The end of certainty. New York: The Free Press.
Rittel, H. W. J. (1969). Panel on policy sciences. American Association for the Advancement of Science, 4, 155.
Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.
Simon, H. A. (1965). The new science of management decisions. In H. A. Simon (Ed.), The Shape of automation for men and management. New York: Harper and Row.
Simon, H. A. (1976). Administrative behavior (3rd ed.). New York: The Free Press.
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 13–22.
Sprague, R. (1980). A framework for the development of decision support systems. MIS Quarterly, 4(4), 1–25.
Sprague, R. H., & Carlson, E. D. (1982). Building effective decision support systems. N.J., Prentice-Hall: Englewood Clliffs.
Townsend, A. (2013). Smart cities-big data, civic hackers and the quest for a new utopia. New York: Norton & Company.
Yaneer, B.-Y. (2002). General features of complex systems. In Encyclopedia of life support systems. Oxford, UK: EOLSS UNESCO Publishers.
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Bibri, S.E. (2019). Novel Intelligence Functions for Data–driven Smart Sustainable Urbanism: Utilizing Complexity Sciences in Fashioning Powerful Forms of Simulations Models. In: Big Data Science and Analytics for Smart Sustainable Urbanism. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-17312-8_10
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