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The “Thermodynamics” of the City

Evolution and Complexity Science in Urban Modelling

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Complexity and Spatial Networks

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

The primary objectives of this chapter are twofold: first, to offer a review of progress in urban modelling using the methods of statistical mechanics; and second, to explore the possibility of using the thermodynamic analogy in addition to statistical mechanics. We can take stock of the “thermodynamics of the city” not in the sense of its physical states – interesting though that would be – but in terms of its daily functioning and its evolution over time. We will show that these methods of statistical mechanics and thermodynamics illustrate the contribution of urban modelling to complexity science and form the basis for understanding the evolution of urban structure.

It is becoming increasingly recognised that the mathematics underpinning thermodynamics and statistical mechanics have wide applicability. This is manifesting itself in two ways: broadening the range of systems for which these tools are relevant; and seeing that there are new mathematical insights that derive from this branch of Physics. Examples of these broader approaches are provided by Beck and Schlagel (1993) and Ruelle (1978, 2004). The recognition of the power of the method and its wider application goes back at least to the 1950s (Jaynes, 1957, for example) but understanding its role in complexity science is much more recent. However, these methods are now being seen as offering a major contribution. In general, the applications have mainly been in fields closely related to the physical sciences. The purpose of this chapter is to demonstrate the relevance of the methods in a field that has had less publicity but which is obviously important: the development of mathematical models of cities. The urban modelling field can be seen, in its early manifestation, as a precursor of complexity science; and, increasingly, as an important application within it (Wilson 2000).

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Notes

  1. 1.

    The detailed justification for this is well known and not presented here.

  2. 2.

    There are many possible definitions of entropy that can be used here, but for present purposes, they can all be considered to be equivalent.

  3. 3.

    For simplicity, we will henceforth drop the quotation marks and let them be understood when concepts are being used through analogies.

  4. 4.

    This model, in more detailed form, has been widely and successfully applied.

  5. 5.

    We should explore whether we can determine a measure of A from the topology of the {c ij }.

  6. 6.

    Note that P appears to have the dimensions of “density” x’money’.

  7. 7.

    Can we take A i B j as an ij partition function? Can we work backwards and ask what we would like the free energy be for this system? If (2.11) specifies the energy and β (=1/kT) the temperature, then F = UTS becomes F = CS/? Then if F = NkT log Z, what is Z?

  8. 8.

    ter Haar (1995, p. 202) does show that each subsystem within an ensemble can itself be treated as an ensemble provided there is a common β value.

  9. 9.

    The following equations can be derived from (2.31) with A substituted for V and T = 1/kβ.

  10. 10.

    What does this produce for U? And is it possible to do all the calculations implied by (2.40)–(2.46)?

  11. 11.

    It is possible to introduce a β i rather than a β which reinforces this idea.

  12. 12.

    We elaborate the notion of phase changes in the next section. Essentially, in this case, they would be discrete “jumps” in the {T ij } or {S ij } arrays at critical values of parameters such as β.

  13. 13.

    K could be j-dependent as K j (and indeed, usually would be) but we retain K for simplicity of illustration.

  14. 14.

    Clarke and Wilson (1985).

  15. 15.

    It would be interesting to calculate the derivatives of the free energy – the F-derivatives – to see whether there is a way of constructing N[W j  > x] out of F. Are we looking at first or second order phase transitions?

  16. 16.

    I am grateful to Aura Reggiani for this suggestion.

  17. 17.

    It can be shown that we can carry out an entropy maximizing calculation on {S ij } simultaneously and that leads to a conventional a spatial interaction model and the same model for {W j }. The implication of this argument is that if we obtain a {W j } model with the method given here, we should then recalculate {S ij } from an spatial interaction model and then iterate with {W j }.

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Wilson, A. (2009). The “Thermodynamics” of the City. In: Reggiani, A., Nijkamp, P. (eds) Complexity and Spatial Networks. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01554-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-01554-0_2

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