Smart Sustainable Urbanism: Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics

  • Simon Elias BibriEmail author
Part of the Advances in Science, Technology & Innovation book series (ASTI)


As a new area of science and technology (S&T), big data science and analytics embodies an unprecedentedly transformative power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, 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 studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach which is based on a computational understanding of city systems that reduces urban life to logical and algorithmic rules and procedures and that employs new scientific methods and principles, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is underpinned by epistemological realism and instrumental rationality, which sustain and are shaped by urban science. However, all knowledge is socially constructed and historically situated, so too are research methods and applied research as related to S&T and as historically produced social formations and practices that circumscribe and produce culturally specific forms of knowledge and reality. This chapter examines the unprecedented paradigmatic, scientific, scholarly, epistemic, and discursive shifts the field of smart sustainable urbanism is undergoing in light of big data science and analytics and the underlying advanced technologies, as well as discusses how these shifts intertwine with and affect one another, and their sociocultural specificity and historical situatedness. I argue that data-intensive science as a new paradigmatic shift is fundamentally changing the scientific and practical foundations of urban sustainability. In specific terms, the new urban science—as underpinned by sustainability science—is increasingly making cities more sustainable, resilient, efficient, livable, and equitable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, and development.


Smart sustainable urbanism Urban science Data science Data-intensive science Paradigm Paradigm shift Episteme Historical a priori Big data computing and the underpinning technologies 


  1. Aldrich, H. E., & Fiol, C. M. (1994). Fools rush in? The institutional context of industry creation. Academy of Management Review, 19(4), 645–670.CrossRefGoogle Scholar
  2. 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.Google Scholar
  3. Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired, 23 June 2008. See–07/pb_theo–ry (accessed October 12, 2012).
  4. Astrom, K. J., & Murray, R. M. (2008). Feedback systems: An introduction for scientists and engineers. Princeton University Press, Princeton. Available online at*murray/amwiki/index.php/Main_Page.
  5. Bachelard, G. (1986). The formation of the scientific mind: A contribution to a psychoanalysis of objective knowledge. Beacon Press.Google Scholar
  6. Baghramian, M. (2004). Relativism. New York: Routledge.CrossRefGoogle Scholar
  7. Barlow, M. (2013). The culture of big data. O’Reilly Media, Inc.Google Scholar
  8. Batty, M. (2013). The new science of cities. Cambridge: MIT Press.CrossRefGoogle Scholar
  9. 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.Google Scholar
  10. Bell, G., Hey, T., & Szalay, A. (2009). Computer science: Beyond the data Deluge. Science, 323(5919), 1297–1298.CrossRefGoogle Scholar
  11. Bettencourt, L. M. A. (2014). The uses of big data in cities. Santa Fe, New Mexico: Santa Fe Institute.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. Bibri, S. E. (2019a). On the sustainability of smart and smarter cities and related big data applications: An interdisciplinary and transdisciplinary review and synthesis. European Journal of Futures Research (in press).Google Scholar
  17. Bibri, S. E. (2019b). A novel model for smart sustainable city of the future: A scholarly backcasting approach to its analysis, investigation, and development. European Journal of Futures Research (in press).Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. Bibri, S. E., & Krogstie, J. (2017a). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183–212.CrossRefGoogle Scholar
  20. 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. ICT of the new wave of computing for sustainable urban forms: Their big data and context-aware augmented typologies and design concepts, 32, 449–474.Google Scholar
  21. 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.Google Scholar
  22. 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 3rd Annual International Conference on Smart City Applications, ACM, October 11–12, Tetouan, Morocco.Google Scholar
  23. Bird, A. (2013). In Zalta, E. N. (Ed.). “Thomas Kuhn. Stanford Encyclopedia of Philosophy. Retrieved 2015-10-26.Google Scholar
  24. Blei, D., & Smyth, P. (2017). Science and Data Science. Proceedings of the National Academies of Sciences, vol. 114, no. 33, June 2017, pp. 8689–8692.Google Scholar
  25. Bourdieu, P. (1988). Homo academicus. Stanford, CA: Stanford University Press.Google Scholar
  26. Bourdieu, P., & Wacquant, L. (1992). An invitation to reflexive sociology. Chicago, IL: University of Chicago Press.Google Scholar
  27. Bowker, G. (2005). Memory practices in the sciences. Cambridge, MA: MIT Press.Google Scholar
  28. Brugger, E. C. (2004). In Casebeer, W. D. (Ed.), Natural ethical facts: Evolution, connectionism, and moral cognition. The Review of Metaphysics, 58(2).Google Scholar
  29. Bulger, R. E., Heitman, E., & Reiser, S. J. (2002). The ethical dimensions of the biological and health sciences (2nd ed.). Cambridge University Press.Google Scholar
  30. Burr, V. (1995). An introduction to social constructivism. London: Sage.Google Scholar
  31. Buttimer, A. (1976). Grasping the dynamism of lifeworld. Annals of the Association of American Geographers, 66, 277–292.CrossRefGoogle Scholar
  32. Chow, S. L. (1997). Introducing statistical methods (Book 1). SAGE Publications Ltd (February 18, 1997). ISBN-10: 0761952055, ISBN-13: 978-0761952053. Google Scholar
  33. Cleveland, W. S. (2001). Data science: An action plan for expanding the technical areas of the field of statistics. International Statistical Review (Revue Internationale de Statistique), 21–26.CrossRefGoogle Scholar
  34. Collins, F. S., Tabak, L. A. (2014-01-30). NIH plans to enhance reproducibility. Nature, 505 (7485), 612–613.CrossRefGoogle Scholar
  35. Dawkins, R. (2007). The god delusion, paperback edition (with new preface by Richard Dawkins). Black Swan.Google Scholar
  36. Dawkins, R. (2016). The god delusion, 10th anniversary edition (with new introduction by Richard Dawkins and afterword by Daniel Dennett). Black Swan.Google Scholar
  37. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64.CrossRefGoogle Scholar
  38. Donoho, D. (2015). “50 Years of Data Science” (PDF). Based on a talk at Tukey Centennial workshop, Princeton, NJ, September 18, 2015.Google Scholar
  39. Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). (2007). Taking science to school: Learning and teaching science in grades k-8. Washington D.C.: National Academy Press.Google Scholar
  40. Feyerabend, P. (1993). Against method. London: Verso.Google Scholar
  41. Flood, J. (2011). The fires: How a computer formula, big ideas, and the best of intentions burned down New York city—and determined the future of cities. New York, NY.Google Scholar
  42. Foth, M. (2009). Handbook of research on urban informatics: The Practice and promise of the real-time city. Hershey, PA: Information Science Reference.CrossRefGoogle Scholar
  43. Foucault, M. (1970). The order of things: An archaeology of the human sciences. New York: Random House.Google Scholar
  44. Foucault, M. (1972). The archaeology of knowledge. London: Routledge. Google Scholar
  45. Foucault, M. (1969). The archaeology of knowledge. New York: Pantheon Books.Google Scholar
  46. Foucault, M. (1966). The order of things: An archaeology of the human sciences. New York: Vintage Books.Google Scholar
  47. Foucault, M. (1980). Truth and power. In C. Gordon (Ed.), Power/knowledge, selected interviews and writings (pp. 1972–1977). Hemel Hempstead: Harvester Wheatsheaf.Google Scholar
  48. Foucault, M. (1984). What is enlightenment? In: P. Rabinow (Ed.), The Foucault Reader (pp. 32–50). New York: Pantheon.Google Scholar
  49. Foucault, M. (1991). Politics and the study of discourse. In G. Burchell, C. Gordon, & P. Miller (Eds.), The Foucault effect: Studies in governmentality (pp. 53–72). Harvester Wheatsheaf, Chicago: The University of Chicago Press.Google Scholar
  50. Fuchs, C. (2005). Sustainability and the information society. ICT&S Center: Advanced Studies and Research in Information and Communication Technologies & Society, University of Salzburg Salzburg.Google Scholar
  51. Gauch, H. G., Jr. (2003). Scientific method in practice. Cambridge University Press.Google Scholar
  52. Gitelman, L. (Ed.). (2013). “Raw Data” is an oxymoron. Cambridge: MIT Press.Google Scholar
  53. Gergen, K. (1985). The social constructionist movement in modern social psychology. American Psychologist, 40(3), 266–275.CrossRefGoogle Scholar
  54. Godfrey-Smith, P. (2003). Theory and reality (p. 272). Chicago: University of Chicago.Google Scholar
  55. Gordon, C. (2000). Introduction. In P. Rabinow (Ed.), Michel Foucault power-essential works of Foucault 1954–1984 (pp. i–xii). New York: The New Press.Google Scholar
  56. Greenfield, A. (2013). Against the smart city. New York, NY: Do Publications.Google Scholar
  57. Hall, S. (Ed.). (1997). Representation: Cultural representations and signifying practices. London: The Open University, Sage Publications.Google Scholar
  58. Hajer, M. A. (1995). The politics of discourse: Ecological modernization and the policy process. Oxford: Clarendon Press.Google Scholar
  59. Handa, M. L. (1986). Transcending liberal and Marxian paradigms. Peace Paradigm: University of Toronto.Google Scholar
  60. Harvey, D. (1973). Social justice and the city. London, UK: Edward Arnold.Google Scholar
  61. Heilbron, J. L. (2003). The Oxford companion to the history of modern science. New York: Oxford University Press.Google Scholar
  62. Heidegger, M. (1962). Being and time. New York: Harper & Row.Google Scholar
  63. Hyland, K. (2000). Disciplinary discourses: Social interactions in academic writing. London: Longman.Google Scholar
  64. Hyland, K., & Bondi, M. (Eds.). (2006). Academic discourse across disciplines. Frankfort: Peter Lang.Google Scholar
  65. Hobbs, M. (2008). On discourse and representation: Reflections on Michel Foucault’s contribution to the study of the mass media. Newcastle: University of Newcastle.Google Scholar
  66. Hubbard, R., Parsa, A. R., & Luthy, M. R. (1997). The spread of statistical significance testing in psychology: The case of the Journal of Applied Psychology. Theory and Psychology, 7(4), 545–554.CrossRefGoogle Scholar
  67. Jessop, R. (2004). Critical semiotic analysis and cultural political economy. Critical Discourse Studies, 1(2), 159–174.CrossRefGoogle Scholar
  68. Keith, D. (1977). Is cultural relativism self-refuting? British Journal of Sociology, 28(1).Google Scholar
  69. 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.CrossRefGoogle Scholar
  70. Krimsky, S. (2003). Science in the private interest: Has the lure of profits corrupted the virtue of biomedical research. Rowman & Littlefield. ISBN 978-0-7425-1479-9. OCLC 185926306.Google Scholar
  71. Kitchin, R. (2014a). The real-time city? Big data and smart urbanism. GeoJournal, 79, 1–14.CrossRefGoogle Scholar
  72. Kitchin, R. (2014b). The data revolution: Big data, open data, data infrastructures and their consequences. London, UK: Sage.Google Scholar
  73. Kitchin, R. (2015). Data-driven, networked urbanism.
  74. Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A, 374, 20160115.CrossRefGoogle Scholar
  75. 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.Google Scholar
  76. Kline, Rex. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington, D.C.: American Psychological Association.CrossRefGoogle Scholar
  77. Kuhn, T. S. (1962/1996). The structure of scientific revolutions. Chicago: University of Chicago Press.Google Scholar
  78. Kuhn, T. S. (1972). Logic of discovery or psychology of research. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge. Cambridge: Cambridge University Press.Google Scholar
  79. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., et al. (2009). Computational social science. Science, 323, 721–733. Scholar
  80. Leeds-Hurwitz, W. (2009). Social construction of reality. In S. Littlejohn & K. Foss (Eds.), Encyclopedia of communication theory (pp. 892–895). Thousand Oaks, CA: SAGE Publications.Google Scholar
  81. Lehmann, E. L. (1992). Introduction to Neyman and Pearson (1933) On the problem of the most efficient tests of statistical hypotheses. In S. Kotz & N. L. Johnson (Eds.), Breakthroughs in statistics (Vol. 1). Berlin: Springer.Google Scholar
  82. Lehmann, E. L., & Romano, J. P. (2005). Testing statistical hypotheses (3E ed.). New York: Springer.Google Scholar
  83. Lemke, J. (1995). Textual politics: Discourse and social dynamics. London: Taylor and Francis.Google Scholar
  84. Lenhard, J. (2006). Models and statistical inference: The controversy between Fisher and Neyman–Pearson. The British Journal for the Philosophy of Science, 57, 69–91.CrossRefGoogle Scholar
  85. Mattei, D. (2001). Paradigms in the social sciences. In International encyclopedia of the social and behavioral sciences (16).Google Scholar
  86. Mattern, S. (2013). Methodolatry and the art of measure: The new wave of urban data science. Design Observer: Places, 5 November 2013. See (accessed November 15, 2013).
  87. Mayo, D. G., & Spanos, A. (2006). Severe testing as a basic concept in a Neyman-Pearson philosophy of induction. The British Journal for the Philosophy of Science, 57(2), 323–357.CrossRefGoogle Scholar
  88. McNutt, M. (2014-01-17). Reproducibility. Science, 343(6168), 229. ISSN 0036-8075. PMID 24436391.CrossRefGoogle Scholar
  89. McPhearson, T., Pickett, S. T. A., Grimm, N., Niemelä, J., Alberti, M., Elmqvist, T., et al. (2016). Advancing urban ecology towards a science of cities. BioScience, 66, 198–212.CrossRefGoogle Scholar
  90. Miller, H. J. (2010). The data avalanche is here. Shouldn’t we be digging? Journal of Regional Science, 50(1), 181–201.CrossRefGoogle Scholar
  91. Morrison, D., Henkel, R. (Eds.). (2006). The significance test controversy. AldineTransaction.Google Scholar
  92. Munafò, M. R., et al. (2017). A manifesto for reproducible science. Nature Human Behavior, 1, 1–9.Google Scholar
  93. Newton-Smith, W. H. (1994). The rationality of science. London: Routledge.Google Scholar
  94. Oakes, M. (1986). Statistical inference: A commentary for the social and behavioral sciences. Chichester, New York: Wiley.Google Scholar
  95. Parsons, W. (2004). Not just steering but weaving: Relevant knowledge and the craft of building policy capacity and coherence. Australian Journal of Public Administration, 63, 43–57.CrossRefGoogle Scholar
  96. Phillips, L., & Jørgensen, M. W. (2002). Discourse analysis as theory and method. London: Sage.CrossRefGoogle Scholar
  97. Peirce, C. S. (1908). A neglected argument for the reality of god (Vol. 7, pp. 90–112). Wikisource, with added notes. Reprinted with previously unpublished part, Collected Papers v. 6, paragraphs 452–85, The Essential Peirce v. 2, pp. 434–50, and elsewhere.Google Scholar
  98. Peng, R. D. (2009-07-01). Reproducible research and Biostatistics. Biostatistics, 10(3), 405–408.CrossRefGoogle Scholar
  99. Ratti, C., & Offenhuber, D. (2014). Decoding the city: How big data can change urbanism. Basel, Switzerland: Birkhauser Verlag AG.Google Scholar
  100. Reitan, P. (2005). Sustainability science–and what’s needed beyond science. Sustainability: Science, Practice and Policy, 1(1), 77–80.Google Scholar
  101. Ribes, D., & Jackson, S. J. (2013). Data bite man: The work of sustaining long-term study. In L. Gitelman (Ed.), ‘Raw data’ is an oxymoron (pp. 147–166). Cambridge, MA: MIT Press.Google Scholar
  102. Rittel, H. W. J. (1969). Panel on policy sciences. American Association for the Advancement of Science, 4, 155.Google Scholar
  103. Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.CrossRefGoogle Scholar
  104. Robert Brown, J. (2014, August 12). Thought experiments. In Stanford encyclopedia of philosophy. Retrieved March 27, 2017.Google Scholar
  105. Robinson, J. D., & Parnell, S. (2017). The global urban: Difference and complexity in urban studies and the science of cities. In S. Hall & R. Burdett (Eds.), The SAGE handbook of the 21st century city (pp. 13–31). London, UK: SAGE.Google Scholar
  106. Sankey, H. (1997). “Kuhn’s ontological relativism,” in issues and images in the philosophy of science: Scientific and philosophical essays in honour of Azarya Polikarov. In D. Ginev & R. S. Cohen (Eds.), Boston studies in the philosophy of science (Vol. 192, pp. 305–20). Dordrecht: Kluwer Academic.Google Scholar
  107. Shatz, D. (2004). Peer review: A critical inquiry. Rowman & Littlefield.Google Scholar
  108. Spradley, J. (1979). The ethnographic interview. Harcourt Brace Jovanovich.Google Scholar
  109. Stuart, A., Ord, K., & Arnold, S. (1999). Classical Inference & the Linear Model: Vol. 2A. Kendall’s advanced theory of statistics (§20.2). Arnold.Google Scholar
  110. Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571–610.CrossRefGoogle Scholar
  111. Sum, N. L. (2004). From “integral state” to “integral world economic order”: Towards a neo-Gramscian cultural international political economy. CPE Working Paper.Google Scholar
  112. Sum, N. L. (2006). Toward a cultural political economy: Discourses, material power and (counter)hegemony. EU Framework 6, DEMOLOGOS project, workpackage 1.Google Scholar
  113. van Gelder, T. (1999). “Heads I win, tails you lose”: A Foray Into the Psychology of Philosophy” (PDF). University of Melbourne.Google Scholar
  114. West, G. F. (2013). Big data needs a big theory to go with it. Scientific American. Viewed November 16, 2016.
  115. Wilson, E. O. (1999). The natural sciences. In Consilience: The unity of knowledge (Reprint ed., pp. 49–71). New York, NY: Vintage.Google Scholar
  116. Yeates, L. B. (2004). Thought experimentation: A cognitive Approach. Graduate Diploma in Arts (By Research) dissertation, University of New South Wales.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Department of Urban Planning and DesignNorwegian University of Science and Technology (NTNU)TrondheimNorway

Personalised recommendations