The Underlying Technological, Scientific, and Structural Dimensions of Data-Driven Smart Sustainable Cities and Their Socio-Political Shaping Factors and Issues

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


We are moving into an era where instrumentation, datafication, and computation are routinely pervading the very fabric of cities, coupled with the interlinking, integration, and coordination of their systems and domains. As a result, vast troves of contextual and actionable data are being produced and used to operate, regulate, manage, and organize urban life. This data-driven approach to urbanism has recently become the mode of production for smart sustainable cities, which are accordingly becoming knowable, tractable, and controllable in new dynamic ways, responsive to the data generated about them by reacting to the analytical outcome of many domains of urban life in terms of enhancing and optimizing operational functioning, planning, design, development, and governance in line with the goals of sustainable development. However, topical studies tend to deal mostly with data-driven smart urbanism while barely exploring how this approach can improve and advance sustainable urbanism under what is labeled ‘data-driven smart sustainable cities’ as a leading paradigm of urbanism. Having a threefold aim, this chapter first examines how data-driven smart sustainable cities are being instrumented, datafied, and computerized so as to improve, advance, and maintain their contribution to the goals of sustainable development through enhanced practices. Secondly, it highlights and substantiates the real potential of big data technology for enabling such contribution by identifying, synthesizing, distilling, and enumerating the key practical and analytical applications of this advanced technology in relation to multiple urban systems and domains with respect to operations, functions, services, designs, strategies, and policies. Thirdly, it proposes, illustrates, and describes a novel architecture and typology of data-driven smart sustainable cities. This chapter intervenes in the existing scholarly conversation by calling attention to a relevant object of study that previous scholarship has neglected and whose significance for the field of urbanism is well elucidated, as well as by bringing new insights to and informing the ongoing debate on smart sustainable urbanism in light of big data science and analytics. This work serves to bring data-analytic thinking and practice to smart sustainable urbanism, and seeks to promote and mainstream its adoption, in addition to drawing special attention to the crucial role and enormous benefits of big data technology and its novel applications as to transforming the future form of such urbanism.


Data-driven smart sustainable cities Urbanism Big data analytics Big data applications Datafication Urban science Sustainability Innovation labs Urban intelligence functions Cloud and fog computing 


  1. Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., & Airaksinen, M. (2017). What are the differences between sustainable and smart cities? Cities, 60, 234–245.CrossRefGoogle Scholar
  2. Almirall, E., & Wareham, J. (2011). Living labs: Arbiters of mid- and ground-level innovation. Technology Analysis and Strategic Management, 23(1), 87–102.CrossRefGoogle Scholar
  3. 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
  4. Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired. Accessed October 12, 2012.
  5. 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.Google Scholar
  6. Arkian, H. R., Diyanat, A., & Pourkhalili, A. (2017). MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. Journal of Network and Computer Applications, 82, 152–165.CrossRefGoogle Scholar
  7. Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274–279.CrossRefGoogle Scholar
  8. Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., et al. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214, 481–518.CrossRefGoogle Scholar
  9. Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768.Google Scholar
  10. Bettencourt, L. M. A. (2014). The uses of big data in cities. Santa Fe, New Mexico: Santa Fe Institute.CrossRefGoogle Scholar
  11. 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: Springer.Google Scholar
  12. Bibri, S. E. (2018a). Smart sustainable cities of the future: The untapped potential of big data analytics and context aware computing for advancing sustainability. Berlin, Germany: Springer.CrossRefGoogle Scholar
  13. 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
  14. 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
  15. Bibri, S. E. (2019). 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
  16. 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
  17. 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
  18. 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.CrossRefGoogle Scholar
  19. 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, 4(38), 1–50.Google Scholar
  20. 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, Tetouan, Morocco, October 11–12. ACM.Google Scholar
  21. Bibri, S. E., & Krogstie, J. (2019). 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
  22. Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing (pp. 13–16). ACM.Google Scholar
  23. Bowker, G. (2005). Memory practices in the sciences. Cambridge, MA: MIT Press.Google Scholar
  24. Brogi, A., & Forti, S. (2017). QoS-aware deployment of IoT applications through the fog. IEEE Internet of Things Journal, 99, 1.Google Scholar
  25. Buttimer, A. (1976). Grasping the dynamism of lifeworld. Annals of the Association of American Geographers, 66, 277–292.CrossRefGoogle Scholar
  26. Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Boston: Harvard Business School Press.Google Scholar
  27. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., et al. (2012). Understanding smart cities: An integrative framework. In The 245th Hawaii International Conference on System Science (HICSS) (pp. 2289–2297), Maui, HI.Google Scholar
  28. Cukier, K., & Mayer-Schoenberger, V. (2013). The rise of big data. Foreign Affairs (May/June), 28–40.Google Scholar
  29. DeRen, L., JianJun, C., & Yuan, Y. (2015). Big data in smart cities. Science China-Information Sciences, 58, 1–12.Google Scholar
  30. Dodge, M., & Kitchin, R. (2007). The automatic management of drivers and driving spaces. Geoforum, 38(2), 264–275.CrossRefGoogle Scholar
  31. Feuer, A. (2013, March 23). The mayor’s geek squad. New York Times. Accessed May 9, 2013.
  32. 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
  33. Foucault, M. (1972). The archaeology of knowledge. London: Routledge.Google Scholar
  34. Foucault, M. (1977). Discipline and punish: The birth of the prison. New York: Pantheon Books.Google Scholar
  35. Gitelman, L. (Ed.). (2013). “Raw data” is an oxymoron. Cambridge: MIT Press.Google Scholar
  36. Greenfield, A. (2013). Against the smart city. New York, NY: Do Publications.Google Scholar
  37. Graham, S., & Marvin, S. (2001). Splintering urbanism: Networked infrastructures, technological mobilities and the urban condition. New York, NY: Routledge.Google Scholar
  38. Harvey, D. (1973/2009). Social justice and the city. London, UK: Edward Arnold.Google Scholar
  39. Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., et al. (2016). The role of big data in smart city. International Journal of Information Management, 36, 748–758.CrossRefGoogle Scholar
  40. Höjer, M., & Wangel, S. (2015). Smart sustainable cities: Definition and challenges. In: L. Hilty & B. Aebischer (Eds.), ICT innovations for sustainability (333–349). Berlin: Springer.Google Scholar
  41. Hollands, R. G. (2008). Will the real smart city please stand up? City Anal Urban Trends Cult Theory. Policy Action, 12(3), 303–320.CrossRefGoogle Scholar
  42. Kelling, S., Hochachka, W., Fink, D., Riedewald, M., Caruana, R., Ballard, G., & Hooker, G. (2009). Data-intensive science a new paradigm for biodiversity studies. BioScience, 59, 613–620.Google Scholar
  43. Khan, Z., Anjum, A., Soomro, K., & Tahir, M. A. (2015). Towards cloud based big data analytics for smart future cities. Journal of Cloud Computing: Advances, Systems and Applications, 4(2).Google Scholar
  44. Khanac, Z., Pervaiz, Z., & Abbasi, A. G. (2017). Towards a secure service provisioning framework in a smart city environment. Future Generation Computer Systems, 77, 112–135.CrossRefGoogle Scholar
  45. Kharrazi, A., Qin, H., & Zhang, Y. (2016). Urban big data and sustainable development goals: Challenges and opportunities. Sustainability, 8(1293), 1–8.Google Scholar
  46. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79, 1–14.CrossRefGoogle Scholar
  47. Kitchin, R. (2015a). Data-driven, networked urbanism.
  48. Kitchin, R. (2015b). Making sense of smart cities: Addressing present shortcomings. Cambridge Journal of Regions, Economy and Society, 8(1), 131–136. Scholar
  49. Kitchin, R. (2016a). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A, 374, 20160115.CrossRefGoogle Scholar
  50. Kitchin, R. (2016b). Reframing, reimagining and remaking smart cities (The Programmable City Working Paper 20).Google Scholar
  51. Kitchin, R., & Dodge, M. (2011). Code/space: Software and everyday life. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  52. Kitchin, R., Lauriault, T. P., & McArdle, G. (2015). Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science, 2(1), 6–28.CrossRefGoogle Scholar
  53. Kitchin, R., Coletta, C., Evans, L., Heaphy, L., & MacDonncha, D. (2017). Smart cities, urban technocrats, epistemic communities and advocacy coalitions (The Programmable City Working Paper 26). Retrieved from
  54. Kloeckl, K., Senn, O., & Ratti, C. (2012). Enabling the real-time city: LIVE Singapore! Journal of Urban Technology, 19(2), 89–112.CrossRefGoogle Scholar
  55. Konugurthi, P. K., Agarwal, K., Chillarige, R. R., & Buyya, R. (2016). The anatomy of big data computing. Software: Practice and Experience (SPE), 46(1), 79–105.Google Scholar
  56. Kourtit, K., Nijkamp, P., & Arribas-Bel, D. (2012). Smart cities perspective—A comparative European study by means of self-organizing maps. Innovation, 25(2), 229–246.Google Scholar
  57. Kramers, A., Höjer, M., Lövehagen, N., & Wangel, J. (2014). Smart sustainable cities: Exploring ICT solutions for reduced energy use in cities. Environmental Modelling & Software, 56, 52–62.Google Scholar
  58. Kusiak, A. (2007). Innovation: The living laboratory perspective. Computer-Aided Design and Applications, 4(6), 863–876.CrossRefGoogle Scholar
  59. Lacinák, M., & Ristvej, J. (2017). Smart city, safety and security. Procedia Engineering, 192, 522–527.CrossRefGoogle Scholar
  60. Laney, D. (2001). 3-D data management: Controlling data volume, velocity and variety. META Group Research Note.Google Scholar
  61. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., et al. (2009). Computational social science. Science, 323, 721–733. Scholar
  62. Marsal-Llacuna, M. -L. (2016). City indicators on social sustainability as standardization technologies for smarter (citizen–centered) governance of cities. Social Indicators Research, 128(3), 1193–1216. Scholar
  63. Marvin, S., Luque-Ayala, A., & McFarlane, C. (Eds.). (2016). Smart urbanism: Utopian vision or false dawn? London, UK: Routledge. Google Scholar
  64. Marz, N., & Warren, J. (2012). Big data: Principles and best practices of scalable realtime data systems. Manning: MEAP Edition.Google Scholar
  65. Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A revolution that will change how we live, work and think. London: John Murray.Google Scholar
  66. Miller, H. J. (2010). The data avalanche is here. Shouldn’t we be digging? Journal of Regional Science, 50, 181–201.CrossRefGoogle Scholar
  67. Niitamo, V.-P., Kulkki, S., Eriksson, M., & Hribernik, K. A. (2006). State-of-the-art and good practice in the field of living labs. In Proceedings of the 12th International Conference on Concurrent Enterprising: Innovative Products and Services through Collaborative Networks (pp. 349–357), Milan, Italy.Google Scholar
  68. Numhauser, P., & Jonathan, B.-M. (2012). Fog computing introduction to a new cloud evolution. In Escrituras silenciadas: paisaje como historiografía (pp. 111–126). Spain: University of Alcala. ISBN: 978-84-15595-84-7.Google Scholar
  69. Ostberg, P. O., Byrne, J., Casari, P., Eardley, P., Anta, A. F., Forsman, J., et al. (2017). Reliable capacity provisioning for distributed cloud/edge/fog computing applications. In 2017 European Conference on Networks and Communications (EuCNC).Google Scholar
  70. 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
  71. Rathore, M. M., Paul, A., Hong, W.-H., Seo, H. C., Awan, I., & Saeed, S. (2018). Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data. Sustainable Cities and Society, 40, 600–610.CrossRefGoogle Scholar
  72. 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
  73. Rotmans, J., Kemp, R., & van Asselt, M. (2001). More evolution than revolution: Transition management in public policy. Foresight, 3(1).Google Scholar
  74. Schumacher, J., & Feurstein, K. (2007). Living labs—A new multi-stakeholder approach to user integration. Presented at the 3rd International Conference on Interoperability of Enterprise Systems and Applications (I-ESA’07), Funchal, Madeira, Portugal.Google Scholar
  75. Singer, N. (2012, March 3). Mission control, built for cities: IBM takes ‘smarter cities’ concept to Rio de Janeiro. New York Times. Accessed May 9, 2013.
  76. Smith, A. (2003). Transforming technological regimes for sustainable development: A role for alternative technology niches? Science and Public Policy, 30(2), 127–135.CrossRefGoogle Scholar
  77. Townsend, A. (2013). Smart cities—Big data, civic hackers, and the quest for a new utopia. New York: Norton & Company.Google Scholar
  78. United Nations. (2015a). Transforming our world: The 2030 agenda for sustainable development. New York, NY. Available at:
  79. United Nations. (2015b). Habitat III issue papers, 21—Smart cities (V2.0). New York, NY. Available at: Accessed May 2, 2017.
  80. United Nations. (2015c). Big data and the 2030 agenda for sustainable development. Prepared by A. Maaroof. Available at:
  81. United Nations. (2016). Paris agreement. United Nations treaty collection, reference C.N. 63.2016. TREATIES–XXVII.7.d. Agreement adopted at the twenty-first session of the Conference of the Parties to the United Nations Framework Convention on Climate Change, Paris, November 30–December 13, 2015. Accessed January 21, 2017.
  82. van Zoonen, L. (2016). Privacy concerns in smart cities. Government Information Quarterly, 33(3), 472–480.CrossRefGoogle Scholar
  83. Vinod Kumar, T. M., & Dahiya, B. (2017). Smart economy in smart cities. In T. M. Vinod Kumar (Ed.), Smart economy in smart cities: International collaborative research: Ottawa, St. Louis, Stuttgart, Bologna, Cape Town, Nairobi, Dakar, Lagos, New Delhi, Varanasi, Vijayawada, Kozhikode, Hong Kong (pp. 3–76). Singapore: Springer Singapore.
  84. Von Hippel, E. (1986). Lead users: A source of novel product concepts. Management Science, 32, 791–805.CrossRefGoogle Scholar
  85. Yao, L., & Rabhi, F. A. (2014). Building architectures for data-intensive science using the ADAGE framework. Concurrency and Computation: Practice and Experience. Scholar
  86. Zhang, C. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal.Google Scholar
  87. Zikopoulos, P. C., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G. (2012). Understanding big data. New York: McGraw Hill.Google Scholar

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Authors and Affiliations

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

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