Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Evolving Data-Driven Approach to Smart Sustainable Urbanism for Tackling the Conundrums of Sustainability and Urbanization Read chapter Read first chapter

2019 | OriginalPaper | Chapter

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

Author : Simon Elias Bibri

Published in: Big Data Science and Analytics for Smart Sustainable Urbanism

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter The Underlying Technological, Scientific, and Structural Dimensions of Data-Driven Smart Sustainable Cities and Their Socio-Political Shaping Factors and Issues
next chapter On the Sustainability and Unsustainability of Smart and Smarter Urbanism and Related Big Data Technology, Analytics, and Application
Literature
Aldrich, H. E., & Fiol, C. M. (1994). Fools rush in? The institutional context of industry creation. Academy of Management Review, 19(4), 645–670.CrossRef
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.
Bachelard, G. (1986). The formation of the scientific mind: A contribution to a psychoanalysis of objective knowledge. Beacon Press.
Barlow, M. (2013). The culture of big data. O’Reilly Media, Inc.
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.
Bell, G., Hey, T., & Szalay, A. (2009). Computer science: Beyond the data Deluge. Science, 323(5919), 1297–1298.CrossRef
Bettencourt, L. M. A. (2014). The uses of big data in cities. Santa Fe, New Mexico: Santa Fe Institute.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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).
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).
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.CrossRef
Bibri, S. E., & Krogstie, J. (2017a). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183–212.CrossRef
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.
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 3rd Annual International Conference on Smart City Applications, ACM, October 11–12, Tetouan, Morocco.
Bird, A. (2013). In Zalta, E. N. (Ed.). “Thomas Kuhn”. Stanford Encyclopedia of Philosophy. Retrieved 2015-10-26.
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.
Bourdieu, P. (1988). Homo academicus. Stanford, CA: Stanford University Press.
Bourdieu, P., & Wacquant, L. (1992). An invitation to reflexive sociology. Chicago, IL: University of Chicago Press.
Bowker, G. (2005). Memory practices in the sciences. Cambridge, MA: MIT Press.
Brugger, E. C. (2004). In Casebeer, W. D. (Ed.), Natural ethical facts: Evolution, connectionism, and moral cognition. The Review of Metaphysics, 58(2).
Bulger, R. E., Heitman, E., & Reiser, S. J. (2002). The ethical dimensions of the biological and health sciences (2nd ed.). Cambridge University Press.
Burr, V. (1995). An introduction to social constructivism. London: Sage.
Buttimer, A. (1976). Grasping the dynamism of lifeworld. Annals of the Association of American Geographers, 66, 277–292.CrossRef
Chow, S. L. (1997). Introducing statistical methods (Book 1). SAGE Publications Ltd (February 18, 1997). ISBN-10: 0761952055, ISBN-13: 978-0761952053.
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.CrossRef
Collins, F. S., Tabak, L. A. (2014-01-30). NIH plans to enhance reproducibility. Nature, 505 (7485), 612–613.CrossRef
Dawkins, R. (2007). The god delusion, paperback edition (with new preface by Richard Dawkins). Black Swan.
Dawkins, R. (2016). The god delusion, 10th anniversary edition (with new introduction by Richard Dawkins and afterword by Daniel Dennett). Black Swan.
Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64.CrossRef
Donoho, D. (2015). “50 Years of Data Science” (PDF). Based on a talk at Tukey Centennial workshop, Princeton, NJ, September 18, 2015.
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.
Feyerabend, P. (1993). Against method. London: Verso.
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.
Foth, M. (2009). Handbook of research on urban informatics: The Practice and promise of the real-time city. Hershey, PA: Information Science Reference.CrossRef
Foucault, M. (1970). The order of things: An archaeology of the human sciences. New York: Random House.
Foucault, M. (1972). The archaeology of knowledge. London: Routledge.
Foucault, M. (1969). The archaeology of knowledge. New York: Pantheon Books.
Foucault, M. (1966). The order of things: An archaeology of the human sciences. New York: Vintage Books.
Foucault, M. (1980). Truth and power. In C. Gordon (Ed.), Power/knowledge, selected interviews and writings (pp. 1972–1977). Hemel Hempstead: Harvester Wheatsheaf.
Foucault, M. (1984). What is enlightenment? In: P. Rabinow (Ed.), The Foucault Reader (pp. 32–50). New York: Pantheon.
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.
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.
Gauch, H. G., Jr. (2003). Scientific method in practice. Cambridge University Press.
Gitelman, L. (Ed.). (2013). “Raw Data” is an oxymoron. Cambridge: MIT Press.
Gergen, K. (1985). The social constructionist movement in modern social psychology. American Psychologist, 40(3), 266–275.CrossRef
Godfrey-Smith, P. (2003). Theory and reality (p. 272). Chicago: University of Chicago.
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.
Greenfield, A. (2013). Against the smart city. New York, NY: Do Publications.
Hall, S. (Ed.). (1997). Representation: Cultural representations and signifying practices. London: The Open University, Sage Publications.
Hajer, M. A. (1995). The politics of discourse: Ecological modernization and the policy process. Oxford: Clarendon Press.
Handa, M. L. (1986). Transcending liberal and Marxian paradigms. Peace Paradigm: University of Toronto.
Harvey, D. (1973). Social justice and the city. London, UK: Edward Arnold.
Heilbron, J. L. (2003). The Oxford companion to the history of modern science. New York: Oxford University Press.
Heidegger, M. (1962). Being and time. New York: Harper & Row.
Hyland, K. (2000). Disciplinary discourses: Social interactions in academic writing. London: Longman.
Hyland, K., & Bondi, M. (Eds.). (2006). Academic discourse across disciplines. Frankfort: Peter Lang.
Hobbs, M. (2008). On discourse and representation: Reflections on Michel Foucault’s contribution to the study of the mass media. Newcastle: University of Newcastle.
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.CrossRef
Jessop, R. (2004). Critical semiotic analysis and cultural political economy. Critical Discourse Studies, 1(2), 159–174.CrossRef
Keith, D. (1977). Is cultural relativism self-refuting? British Journal of Sociology, 28(1).
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.CrossRef
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.
Kitchin, R. (2014a). The real-time city? Big data and smart urbanism. GeoJournal, 79, 1–14.CrossRef
Kitchin, R. (2014b). The data revolution: Big data, open data, data infrastructures and their consequences. London, UK: Sage.
Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A, 374, 20160115.CrossRef
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.
Kline, Rex. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington, D.C.: American Psychological Association.CrossRef
Kuhn, T. S. (1962/1996). The structure of scientific revolutions. Chicago: University of Chicago Press.
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.
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.
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.
Lehmann, E. L., & Romano, J. P. (2005). Testing statistical hypotheses (3E ed.). New York: Springer.
Lemke, J. (1995). Textual politics: Discourse and social dynamics. London: Taylor and Francis.
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.CrossRef
Mattei, D. (2001). Paradigms in the social sciences. In International encyclopedia of the social and behavioral sciences (16).
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.CrossRef
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.CrossRef
Miller, H. J. (2010). The data avalanche is here. Shouldn’t we be digging? Journal of Regional Science, 50(1), 181–201.CrossRef
Morrison, D., Henkel, R. (Eds.). (2006). The significance test controversy. AldineTransaction.
Munafò, M. R., et al. (2017). A manifesto for reproducible science. Nature Human Behavior, 1, 1–9.
Newton-Smith, W. H. (1994). The rationality of science. London: Routledge.
Oakes, M. (1986). Statistical inference: A commentary for the social and behavioral sciences. Chichester, New York: Wiley.
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.CrossRef
Phillips, L., & Jørgensen, M. W. (2002). Discourse analysis as theory and method. London: Sage.CrossRef
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.
Peng, R. D. (2009-07-01). Reproducible research and Biostatistics. Biostatistics, 10(3), 405–408.CrossRef
Ratti, C., & Offenhuber, D. (2014). Decoding the city: How big data can change urbanism. Basel, Switzerland: Birkhauser Verlag AG.
Reitan, P. (2005). Sustainability science–and what’s needed beyond science. Sustainability: Science, Practice and Policy, 1(1), 77–80.
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.
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.CrossRef
Robert Brown, J. (2014, August 12). Thought experiments. In Stanford encyclopedia of philosophy. Retrieved March 27, 2017.
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.
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.
Shatz, D. (2004). Peer review: A critical inquiry. Rowman & Littlefield.
Spradley, J. (1979). The ethnographic interview. Harcourt Brace Jovanovich.
Stuart, A., Ord, K., & Arnold, S. (1999). Classical Inference & the Linear Model: Vol. 2A. Kendall’s advanced theory of statistics (§20.2). Arnold.
Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571–610.CrossRef
Sum, N. L. (2004). From “integral state” to “integral world economic order”: Towards a neo-Gramscian cultural international political economy. CPE Working Paper.
Sum, N. L. (2006). Toward a cultural political economy: Discourses, material power and (counter)hegemony. EU Framework 6, DEMOLOGOS project, workpackage 1.
van Gelder, T. (1999). “Heads I win, tails you lose”: A Foray Into the Psychology of Philosophy” (PDF). University of Melbourne.
Wilson, E. O. (1999). The natural sciences. In Consilience: The unity of knowledge (Reprint ed., pp. 49–71). New York, NY: Vintage.
Yeates, L. B. (2004). Thought experimentation: A cognitive Approach. Graduate Diploma in Arts (By Research) dissertation, University of New South Wales.
Metadata
Title
Smart Sustainable Urbanism: Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics
Author
Simon Elias Bibri
Copyright Year
2019
Book
Big Data Science and Analytics for Smart Sustainable Urbanism

Print ISBN: 978-3-030-17311-1

Electronic ISBN: 978-3-030-17312-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-17312-8_6

Premium Partner

    Image Credits