The Relationship of Dynamic Entropy Maximising and Agent-Based Approaches in Urban Modelling

Chapter

Abstract

Entropy maximising models are well established within the field of urban modelling as a method for predicting flows of people or material within an urban system. The dynamic urban retail model (Harris and Wilson, Environ Plan A 10:371–388, 1978) is one of the most well known applications of this technique and is an example of a BLV (Boltzmann-Lotka-Volterra) model. We define an agent-based model (ABM) of urban retail and explore whether it can be made equivalent to a BLV model. Application of both models to the metropolitan county of South Yorkshire in the UK indicates that both models produce similar outputs. This direct comparison provides some insights into the differences and similarities of each approach, as well as highlighting the relative strengths and weaknesses. The ABM has the potential to be easier to disaggregate, while the entropy maximising model is more computationally efficient.

References

  1. Clarke, M., & Wilson, A. G. (1985). The dynamics of urban spatial structure: The progress of a research programme. Transactions of the Institute of British Geographers, 10, 427–451.CrossRefGoogle Scholar
  2. Clarke, G.P. & Wilson, A.G. (1986). Combining theoretical and empirical research in retail location analysis (Working Paper 468). Leeds: School of Geography, University of Leeds.Google Scholar
  3. Clarke, G. P., Clarke, M., & Wilson, A. G. (1986). Multiple bifurcation effects with a logistic attractiveness function in the supply side of a service system. Systemi Urbani, 7, 43–76.Google Scholar
  4. Epstein, J. M., & Axtell, R. (1996). Growing artificial societies. Cambridge: MIT Press.Google Scholar
  5. Hagen-Zanker, A., Engelen, G., Hurkens, J., Vanhout, R., & Uljee, I. (2006). Map comparison kit 3: User manual. Maastricht: Research Institute for Knowledge Systems.Google Scholar
  6. Harris, B., & Wilson, A. G. (1978). Equilibrium values and dynamics of attractiveness terms in production-constrained spatial-interaction models. Environment and Planning A, 10, 371–388.CrossRefGoogle Scholar
  7. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Reading: Addison Wesley.Google Scholar
  8. Lombardo, S. R. (1986). New developments of a dynamic urban retail model with reference to consumers’ mobility and costs for developers. In D. A. Griffith & R. J. Haining (Eds.), Transformations through space and time. Dordrecht: Martinus Nijhoff.Google Scholar
  9. Lowry, I. S. (1964). A model of metropolis (Memorandum RM.4035-RC). Santa Monica: Rand Corporation.Google Scholar
  10. Wilson, A. G. (1967). A statistical theory of spatial distribution models. Transportation Research, 1, 253–269.CrossRefGoogle Scholar
  11. Wilson, A. G. (1970). Entropy in urban and regional modelling. London: Pion.Google Scholar
  12. Wilson, A. G. (2006). A generalised representation for a comprehensive urban and regional model. Computers, Environment and Urban Systems, 31, 148–161.CrossRefGoogle Scholar
  13. Wilson, A. G. (2008). Boltzmann, Lotka and Volterra and spatial structural evolution: An integrated methodology for some dynamical systems. Journal of the Royal Society, Interface, 5, 865–871.CrossRefGoogle Scholar
  14. Wilson, A. G., & Dearden, J. (2011). Phase transitions and path dependence in urban evolution. Journal of Geographical Systems, 13(1), 1–16.Google Scholar
  15. Wilson, A. G., & Oulton, M. (1983). The corner shop to supermarket transition in retailing: The beginnings of empirical evidence. Environment and Planning A, 15(2), 265–274.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Centre for Applied Spatial Analysis (CASA)University CollegeLondonUK

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