Big Data or No Data: Supporting Urban Decision-Making with a Nested System Model

  • Christian WallothEmail author
Part of the Understanding Complex Systems book series (UCS)


There are always new developments in urban systems. Some may be desired; others may be undesired. This article briefly discusses how Big Data methods could detect such new developments. It then turns to discussing how such new developments could be detected, even where no Big Data is available—which may be the case in most cities—and/or where Big Data methods might fail to detect new developments. An answer is provided by a model of nested systems, where slower-changing systems, such as cultural and economic systems, enclose faster-changing systems, such as political and technical systems. The article further presents a suggested approach for influencing such desired and undesired developments, based on knowledge gained from studying relatively faster and relatively slower systems in a given complex urban system. Thus, the article suggests a way to observe, evaluate, understand, and influence complex urban systems without the need for Big Data.


Urban System Alternative Development Taxi Service Slow System Nest System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Airbnb. Accessed 22 Nov 2015
  2. Aulich, U.: Knapper Wohnraum in Berlin. Berliner vermieten 17.000 illegale Ferienwohnungen. Berliner Zeitung, 6 Aug 2015Google Scholar
  3. Berger, L.: Lviv public transport project: urban transport regulatory improvement. Final Report (2011)Google Scholar
  4. Deacon, T.W.: Incomplete Nature: How Mind Emerged from Matter. Norton & Company, New York (2011)Google Scholar
  5. Holling, C.S., Gunderson, L.H.: Resilience and adaptive cycles. In: Holling, C.S., Gunderson, L.H. (eds.) Panarchy, Understanding Transformations in Human and Natural Systems. Island Press, Washington (2002)Google Scholar
  6. John, R., Aulich, U.: Ferienwohnungen in Berlin. Airbnb wächst und wächst. Berliner Zeitung, 10 Nov 2015Google Scholar
  7. Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)Google Scholar
  8. Keizer, K., Lindenberg, S., Steg, L.: The spreading of disorder. Science 322(12), 1681–1685 (2008)CrossRefGoogle Scholar
  9. Kinsella, C.: Roadblock up ahead. b.inspired, brussels airlines inflight magazine 1, 12 (2015)Google Scholar
  10. Maturana, H., Varela, F.: Autopoiesis and Cognition. The Realization of the Living. Reidel, Dordrecht (1980)Google Scholar
  11. Metcalf, G.S.: A case for system-specific modeling. In: Proceedings of the 53rd Annual Conference, The International Society for the Systems Sciences (2009)Google Scholar
  12. Popper, K., Eccles, J.: The Self and Its Brain. Springer, Berlin (1977)CrossRefGoogle Scholar
  13. Sarasvathy, S.D.: Causation and effectuation: toward a theoretical shift from economic inevitability to entrepreneurial contingency. Acad. Manag. Rev. 26(2), 243–263 (2001)Google Scholar
  14. Sawyer, K.R.: Social Emergence: Societies as Complex Systems. Cambridge University Press, New York (2005)CrossRefGoogle Scholar
  15. Stephan, A.: Emergenz. Von der Unvorhersagbarkeit zur Selbstorganisation. Dresden University Press, Dresden (1999)Google Scholar
  16. Taleb, NN.: The Black Swan: The Impact of the Highly Improbable. Random House (2007)Google Scholar
  17. Uber. Accessed 22 Nov 2015)
  18. Walloth, C.: Emergent Nested Systems. A Theory of Understanding and Influencing Complex Systems as well as Case Studies in Urban Systems. Springer, Cham (2016)Google Scholar
  19. Walloth, C.: Emergence in complex urban systems: blessing or curse of planning efforts? In: Walloth, C., Gurr, J.M., Schmidt, J.A (eds.) Understanding Complex Urban Systems: Multidisciplinary Approaches to Modeling, pp. 121–132. Springer, ChamGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Walloth Urban Advisors SPRLBrusselsBelgium

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