Advertisement

The Theoretical and Disciplinary Underpinnings of Data–Driven Smart Sustainable Urbanism: An Interdisciplinary and Transdisciplinary Perspective

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

Abstract

Interdisciplinarity and transdisciplinarity have become a widespread mantra for research within diverse fields, accompanied by a growing body of academic and scientific publications. The research field of smart sustainable/sustainable smart urbanism is profoundly interdisciplinary and transdisciplinary in nature. It operates out of the understanding that advances in knowledge necessitate pursuing multifaceted questions that can only be resolved from the vantage point of interdisciplinarity and transdisciplinarity. Indeed, related research problems are inherently too complex and dynamic to be addressed by single disciplines. In addition, this field does not have a unitary approach in terms of a uniform set of concepts, theories, and disciplines, as it does not represent a specific direction of research but rather multiple directions. These are analytically quite diverse. Regardless, interdisciplinarity and transdisciplinarity as scholarly perspectives apply, by extension, to any conceptual, theoretical, and/or disciplinary foundations underpinning this field. Such perspectives in this chapter represent a rather topical and organizational approach as justified and determined by the interdisciplinary aid transdisciplinary nature of the research field of smart sustainable urbanism. In this subject, additionally, theories from academic and scientific disciplines constitute a foundation for action—data–driven smart sustainable urbanism and related urban big data development as informed by data science practiced within the fields of urban science and urban informatics, as well as by sustainability science and sustainable development. In light of this, it is of relevance and importance to develop a foundational approach consisting of the relevant concepts, theories, discourses, and academic and scientific disciplines that underpin smart sustainable urbanism as a field for research and practice. With that in regard, this chapter endeavors to systematize this complex field by identifying, distilling, mixing, fusing, and thematically analytically organizing the core dimensions of this foundational approach. The primary intention of setting such approach is to conceptually and analytically relate urban planning and development, sustainable development, and urban science while emphasizing why and the extent to which sustainability and big data computing have particularly become influential in urbanism in modern society. Being interdisciplinary and transdisciplinary in nature, such approach is meant to further highlight that this scholarly character epitomizes the orientation and essence of the research field of smart sustainable urbanism in terms of its pursuit and practice. Moreover, its value lies in fulfilling one primary purpose: to explain the nature, meaning, implications, and challenges pertaining to the multifaceted phenomenon of smart sustainable urbanism. This chapter provides an important lens through which to understand a set of theories that is of high integration, fusion, applicability, and influence potential in relation to smart sustainable urbanism.

Keywords

Smart sustainable urbanism Sustainable urbanism Interdisciplinary Transdisciplinary Sustainability Sustainable development Urban planning and development Big data computing Scientific disciplines  Academic disciplines 

References

  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. 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. 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
  4. Aseem, I. (2013). Designing urban transformation. New York, London: Routledge.Google Scholar
  5. Barlow, M. (2013). The culture of big data. O’Reilly Media, Inc.Google Scholar
  6. Batty, M. (2013). The new science of cities. Cambridge: MIT Press.CrossRefGoogle Scholar
  7. 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
  8. Beatley, T. (2000). Green urbanism: Learning from European cities. Washington, DC: Island Press.Google Scholar
  9. Blei, D., & Smyth, P. (2017, June). Science and data science. Proceedings of the National Academies of Sciences, 114(33), 8689–8692.Google Scholar
  10. Benham-Hutchins, M., & Clancy, T. (2010). Social networks as embedded complex adaptive systems. JONA, 40(9), 352–356.CrossRefGoogle Scholar
  11. Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.CrossRefGoogle Scholar
  12. Bertalanffy, Lv. (1950). An outline of general system theory. The British Journal for the Philosophy of Science, 1, 134–165.CrossRefGoogle Scholar
  13. Bertalanffy, Lv. (1962). General system theory. A critical review, general systems. In: General system theory (vol. 7, pp. 1–20). Available from: http://www.fsc.yorku.ca/york/istheory/wiki/index.php/General_systems_theory.
  14. Bertalanffy, Lv. (1968). General systems theory. New York: George Braziller.Google Scholar
  15. Bettencourt, L. M. A. (2014). The uses of big data in cities. Santa Fe, New Mexico: Santa Fe Institute.CrossRefGoogle Scholar
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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, pp. 2 of 27.Google Scholar
  21. 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).Google Scholar
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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.CrossRefGoogle Scholar
  28. Bijker, W. E. (1995). Of bicycles, bakelites, and bulbs: Toward a theory of socio-technical change. Cambridge, MA: MIT Press.Google Scholar
  29. Boeing, G., Church, D., Hubbard, H., Mickens, J., & Rudis, L. (2014). LEED–ND and livability revisited. Berkeley Planning Journal, 27(1), 31–55.CrossRefGoogle Scholar
  30. Bossel, H. (2004). Systeme, dynamik, simulation: Modellbildung, analyze und simulation komplexer systeme. Norderstedt: Books on Demand.Google Scholar
  31. Bourdic, L., Salat, S., & Nowacki, C. (2012). Assessing cities: A new system of cross-scale spatial indicators. Building Research & Information, 40(5), 592–605.CrossRefGoogle Scholar
  32. Bridge, G. (2009). Urbanism, international encyclopedia of human geography (pp. 106–111). Oxford: Elsevier.CrossRefGoogle Scholar
  33. Brown, H. S. (2012). Sustainability science needs to include sustainable consumption. Environment: Science and Policy for Sustainable Development, 54(1), 20–25.Google Scholar
  34. 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.CrossRefGoogle Scholar
  35. Carlsson, B., Jacobsson, S., Holmen, M., & Rickne, A. (2002). Innovation systems: Analytical and methodological issues. Research Policy, 31, 233–245.CrossRefGoogle Scholar
  36. Carlsson, B., & Stankiewicz, R. (1991). On the nature, function, and composition of technological systems. Journal of Evolutionary Economics, 1, 93–118.CrossRefGoogle Scholar
  37. Carlsson-Kanyama, A., Dreborg, K. H., Eenkhorn, B. R., Engström, R., & Falkena, B. (2003). Image of everyday life in the future sustainable city: Experiences of back-casting with stakeholders in five European cities. The Environmental Strategies Research Group (Fms)—report 182, The Royal Institute of Technology, Stockholm, Sweden, 2003. Report available at /react–text www.infra.kth.sereact–text:563. Google Scholar
  38. Clark, W. C. (2007). Sustainability science: A room of its own. Proceedings of the National Academy of Sciences of the United States of America, 104, 1737–1738.CrossRefGoogle Scholar
  39. Clark, W. C., & Dickson, N. M. (2003). Sustainability science: The emerging research program. Proceedings of the National Academy of Sciences of the United States of America, 100(14), 8059–8061.CrossRefGoogle Scholar
  40. Dasgupta, P. (2007). The idea of sustainable development. Sustainability Science, 2(1), 5–11.CrossRefGoogle Scholar
  41. Davidson, M. (1983). Uncommon sense: The life and thought of Ludwig von Bertalanffy. Los Angeles: J. P. Tarcher Inc.Google Scholar
  42. Denning, P. J. (2000). Computer science: The discipline. In: Encyclopedia of computer science.Google Scholar
  43. Denning, P. J., Comer, D. E., Gries, D., Mulder, M. C., Tucker, A., Turner, A. J., et al. (1989). Computing as a discipline. Communications of the ACM, 32(1), 9–23.CrossRefGoogle Scholar
  44. de Vries, B. J. M. (2013). Sustainability science. The Netherlands: Cambridge University Press, Universiteit Utrecht.Google Scholar
  45. Donoho, D. (2015). “50 Years of Data Science” (PDF). Based on a talk at Tukey Centennial workshop, Princeton, NJ, September 18, 2015.Google Scholar
  46. Dreborg, K. H. (1996). Essence of backcasting. Futures, 28(9), 813–828.CrossRefGoogle Scholar
  47. Eden, A. H. (2007). Three paradigms of computer science. Minds and Machines, 17(2), 135–167.CrossRefGoogle Scholar
  48. Fan, W., & Bifet, A. (2013). Mining big data: Current status, and forecast to the future. ACM SIGKDD Explorations Newsletter, 14(2), 1–5.CrossRefGoogle Scholar
  49. Farr, D. (2008). Sustainable urbanism. New York: Wiley.Google Scholar
  50. Foster, J. (2001). Education as sustainability. Environmental Education Research, 7(2), 153–165.CrossRefGoogle Scholar
  51. 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
  52. Foth, M., & Brynskov, M. (2016). Participatory action research for civic engagement. In E. Gordon & P. Mihailidis (Eds.), Civic media: Technology, design, practice (pp. 563–580). Cambridge, MA: MIT Press. ISBN 978-0-262-03427-2.Google Scholar
  53. Foth, M., Choi, J. H., & Satchell, C. (2011). Urban informatics. In Conference on Computer Supported Cooperative Work (CSCW), Hangzhou, China (pp. 1–8).Google Scholar
  54. Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research Policy, 33(6–7), 897–920.CrossRefGoogle Scholar
  55. Geels, F. W. (2005). Technological transitions and system innovations: A co-evolutionary and socio-technical analysis. Cheltenham, UK: Edward Elgar.CrossRefGoogle Scholar
  56. Gregory, D., Johnston, R., & Pratt, G. (Eds.). (2009). Dictionary of human geography (5th ed.). Hoboken, NJ, USA: Wiley-Blackwell.Google Scholar
  57. Handy, S. (1996). Methodologies for exploring the link between urban form and travel behavior. Transportation Research Part D: Transport and Environment, 2(2), 151–165.CrossRefGoogle Scholar
  58. Harvey, D. (1973/2009). Social justice and the city. London, UK: Edward Arnold.Google Scholar
  59. 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
  60. Hearn, G., Tacchi, J., Foth, Mus, & Lennie, J. (2009). Action research and new media: Concepts, methods, and cases. Cresskill, NJ: Hampton Press. ISBN 978-1-57273-866-9.Google Scholar
  61. Hiremath, R. B., Balachandra, P., Kumar, B., Bansode, S. S., & Murali, J. (2013). Indicator-based urban sustainability—A review. Energy for Sustainable Development, 17, 555–563.  https://doi.org/10.1016/j.esd.2013.08.004.CrossRefGoogle Scholar
  62. Höjer, M., & Wangel, S. (2015). Smart sustainable cities: Definition and challenges. In L. Hilty & B. Aebischer (Eds.), ICT innovations for sustainability (pp. 333–349). Berlin: Springer.CrossRefGoogle Scholar
  63. Holmberg, J. (1998). Backcasting: A natural step in operationalizing sustainable development. Greener Management International (GMI), 23, 30–51.Google Scholar
  64. 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.CrossRefGoogle Scholar
  65. Hyland, K. (2000). Disciplinary discourses: Social interactions in academic writing. London: Longman.Google Scholar
  66. Hyland, K., & Bondi, M. (Eds.). (2006). Academic discourse across disciplines. Frankfort: Peter Lang.Google Scholar
  67. International Telecommunications Union (ITU). (2014). Agreed definition of a smart sustainable city. In: Focus group on smart sustainable cities, SSC–0146 version Geneva, 5–6 March.Google Scholar
  68. 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.CrossRefGoogle Scholar
  69. Kates, R., Clark, W., Corell, R., Hall, J., & Jaeger, C. (2001). Sustainability science. Science (Science), 292(5517), 641–642.Google Scholar
  70. 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
  71. Khan, M., Uddin, M. F., Gupta, N. (2014). Seven V’s of big data understanding: Big data to extract value. In American Society for Engineering Education (ASEE Zone 1), 2014 zone 1 Conference of the IEEE (pp. 1–5).Google Scholar
  72. Kieffer, S. W., Barton, P., Palmer, A. R., Reitan, P. H., Zen, E. (2003). Megascale events: Natural disasters and human behavior. The Geological Society of America Abstracts with Programs, 432.Google Scholar
  73. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79, 1–14.CrossRefGoogle Scholar
  74. Kitchin, R. (2015). Data-driven, networked urbanism.  https://doi.org/10.2139/ssrn.2641802.
  75. Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A, 374, 20160115.CrossRefGoogle Scholar
  76. Komiyama, H., & Takeuchi, K. (2006). Sustainability science: Building a new discipline. Sustainability Science, 1, 1–6.CrossRefGoogle Scholar
  77. 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.CrossRefGoogle Scholar
  78. Kumar, A., & Prakash, A. (2014). The role of big data and analytics in smart cities. International Journal of Science and Research (IJSR), 6(14), 12–23.Google Scholar
  79. 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
  80. Jabareen, Y. R. (2006). Sustainable urban forms: Their typologies, models, and concepts. Journal of Planning Education and Research, 26, 38–52.CrossRefGoogle Scholar
  81. Karun, K. A., & Chitharanjan, K. (2013). A review on hadoop—HDFS infrastructure extensions. In: IEEE, information & communication technologies (ICT), pp 132–137.Google Scholar
  82. Laney, D. (2001). 3-D data management: Controlling data volume, velocity and variety. META Group Research Note.Google Scholar
  83. Larice, M., & MacDonald, E. (Eds.). (2007). The urban design reader. New York, London: Routledge.Google Scholar
  84. László, E. (1972). Introduction to systems philosophy: Toward a new paradigm of contemporary thought. Gordon & Breach. Google Scholar
  85. Lemke, J. (1995). Textual politics: Discourse and social dynamics. London: Taylor and Francis.Google Scholar
  86. Lynch, K. (1981). A theory of good city form. Cambridge, MA: MIT Press.Google Scholar
  87. Lytras, M. D., & Visvizi, A. (2018). Who uses smart city services and what to make of them: Toward interdisciplinary smart cities research. Sustainability, 10(10), 1–19.Google Scholar
  88. McCarthy, J. (2007). What is artificial intelligence? Computer Science Department, Stanford University, Stanford.Google Scholar
  89. Max-Neef, M. A. (2005). Foundations of transdisciplinarity. Ecological Economics, 53(1), 5–16.CrossRefGoogle Scholar
  90. Miola, A. (2008). Backcasting approach for sustainable mobility. European Commission, Joint Research Center, Institute for Environment and Sustainability.Google Scholar
  91. Morinière, L. (2012). Environmentally influenced urbanization: Footprints bound for town? Urban Studies, 49(2), 435–450. CrossRefGoogle Scholar
  92. M’Pherson, P. (1974). A perspective on systems science and systems philosophy. Futures, 6, 219–239.CrossRefGoogle Scholar
  93. Nigel, T. (2007). Urban planning theory since 1945. London: Sage.Google Scholar
  94. Nielsen, M. (2011). Reinventing discovery: The new era of networked science. Princeton: Princeton University Press.Google Scholar
  95. O’Connor, T., & Wong, H. Y. (2012). Emergent properties. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (2012th ed.). Berlin: Springer.Google Scholar
  96. 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.CrossRefGoogle Scholar
  97. Phdungsilp, A. (2011). Futures studies’ backcasting method used for strategic sustainable city planning. Futures, 43(7), 707–714.CrossRefGoogle Scholar
  98. Provost, F., & Fawcett, T. (2013). Data science for business. Sebastopol, CA: O’Reilly Media Inc.Google Scholar
  99. Ratti, C., & Offenhuber, D. (2014). Decoding the city: How big data can change urbanism. Basel, Switzerland: Birkhauser Verlag AG.Google Scholar
  100. Ratti, C., & Claudel, M. (2016). The city of tomorrow: Sensors, networks, hackers, and the future of urban life. New Haven, CT: Yale University Press.Google Scholar
  101. Richmond, B. (1991). Systems thinking: Four key questions. Lyme: High Performance Systems Inc.Google Scholar
  102. Reitan, P. (2005). Sustainability science—And what’s needed beyond science. Sustainability: Science, Practice and Policy, 1(1), 77–80.Google Scholar
  103. Salat, S., & Bourdic, L. (2012). Systemic resilience of complex urban systems. TeMATrimestrale del Laboratorio Territorio Mobilità e Ambiente–TeMALab, 5(2), 55–68.Google Scholar
  104. Satchell, C. (2008). Cultural theory and design: Identifying trends by looking at the action in the periphery. ACM Interactions, 15(6), 23.CrossRefGoogle Scholar
  105. Senge, P. M. (1990). The fifth discipline: The art & practice of the learning organization. New York: Doubleday Business.Google Scholar
  106. Sharifi, A. (2016). From Garden City to Eco-urbanism: The quest for sustainable neighborhood development. Sustainable Cities and Society, 20, 1–16.CrossRefGoogle Scholar
  107. Shepard, M. (Ed.). (2011). Sentient city: Ubiquitous computing, architecture, and the future of urban space. Cambridge, MA: MIT Press.Google Scholar
  108. Singh, J., & Singla, V. (2015). Big data: Tools and technologies in big data. International Journal of Computer Applications (0975–8887) 112(15).Google Scholar
  109. Thrift, N. (2014). The promise of urban informatics: Some speculations. Environment and Planning A, 46(6), 1263–1266.CrossRefGoogle Scholar
  110. Townsend, A. (2013). Smart cities—Big data, civic hackers and the quest for a new utopia. New York: Norton & Company.Google Scholar
  111. United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. New York, NY. Available at: https://sustainabledevelopment.un.org/post2015/transformingourworld.
  112. Unsworth, K., Forte, A., & Dilworth, R. (Eds.). (2014). Urban informatics: The role of citizen participation in policy making. Special issue of the Journal of Urban Technology, 21(4).Google Scholar
  113. Van Assche, K., Beunen, R., Duineveld, M., & de Jong, H. (2013). Co-evolutions of planning and design: Risks and benefits of design perspectives in planning systems. Plan Theory, 12(2), 177–198.CrossRefGoogle Scholar
  114. Van Bueren, E., van Bohemen, H., Itard, L., & Visscher, H. (2011). Sustainable Urban Environments: An ecosystem approach. Springer International Publishing.Google Scholar
  115. Van der Ryn, S., & Cowan, S. (1996). Ecological design. Island Press.Google Scholar
  116. Warleigh-Lack, A. (2011). Greening the European Union for legitimacy? A cautionary reading of Europe 2020. Innovation: The European Journal of Social Science Research, 23, 297–311.Google Scholar
  117. Wegner, P. (1976). Research paradigms in computer science. In: Proceedings of the 2nd International Conference on Software Engineering. Los Alamitos, San Francisco, CA, United States: IEEE Computer Society Press.Google Scholar
  118. Wheeler, S. M., & Beatley, T. (Eds.). (2010). The sustainable urban development reader. London, New York: Routledge.Google Scholar
  119. Williams, K. (2009). Sustainable cities: Research and practice challenges. International Journal of Urban Sustainable Development, 1(1), 128–132.Google Scholar
  120. Williams, K., Burton, E., & Jenks, M. (Eds.). (2000). Achieving sustainable urban form. London: E & FN Spon. Google Scholar
  121. World Commission on Environment and Development (WCED). (1987). Our common future (The Brundtland report). Oxford/New York: Oxford University Press.Google Scholar
  122. Wirth, L. (1938). Urbanism as a way of life. American Journal of Sociology, 44(1), 1–24.CrossRefGoogle Scholar
  123. Yaneer, B.-Y. (2002). General features of complex systems. In Encyclopedia of life support systems. Oxford, UK: EOLSS UNESCO Publishers.Google Scholar
  124. Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 1–55.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