Spatial Microsimulation

  • Alison J. Heppenstall
  • Dianna M. Smith
Reference work entry


Spatial microsimulation is an excellent option to create estimated populations at a range of spatial scales where data may be otherwise unavailable. In this chapter, we outline three common methods of spatial microsimulation, identifying the relative strengths and weaknesses of each approach. We conclude with a worked example using deterministic reweighting to estimate tobacco smoking prevalence by neighborhood in London, UK. This illustrates how spatial microsimulation may be used to estimate not only populations but also behaviors and how this information may then be used to predict the outcomes of policy change at the local level.


Simulated Annealing Smoking Prevalence Constraint Variable Microsimulation Model Synthetic Population 
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.



This work was funded by the ESRC funded grant “Modeling Individual Consumer Behavior” (RES-061-25-0030) and MRC Population Health Scientist Fellowship (G0802447). The modeling framework used was developed by Kirk Harland.


  1. Anderson B (2007) Creating small-area income estimates: spatial microsimulation modeling. Department for Communities and Local Government. Communities and Local Government, LondonGoogle Scholar
  2. Ballas D, Rossiter D, Thomas B, Clarke G, Dorling D (2005) Geography matters. Simulating the local impacts of national social policies. Joseph Rowntree Foundation, YorkGoogle Scholar
  3. Beckman RJ, Baggerly KA, McKay MD (1996) Creating synthetic baseline populations. Transport Res Part A 30(6):415–429Google Scholar
  4. Birkin M, Clarke M (1988) SYNTHESIS – a synthetic spatial information system for urban and regional analysis: methods and examples. Environ Plann A 20:1645–1671CrossRefGoogle Scholar
  5. Birkin M, Clarke M (1989) The generation of individual and household incomes at the small area level using SYNTHESIS. Reg Stud 23(6):535–548CrossRefGoogle Scholar
  6. Birkin M, Wu B (2012) A review of microsimulation and hybrid agent-based models. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Dordrecht, pp 51–68CrossRefGoogle Scholar
  7. Brown L, Harding A (2002) Social modeling and public policy: application of microsimulation modeling in Australia. Jasss J Artif Soc Soc Simul 5:4Google Scholar
  8. Congdon P (2006) Estimating diabetes prevalence by small area in England. J Pub Health 28(1):71–81CrossRefGoogle Scholar
  9. Crooks A, Heppenstall A (2012) Introduction to agent-based modeling. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Dordrecht, pp 85–108CrossRefGoogle Scholar
  10. Davies L (1987) Genetic algorithms and simulated annealing: research notes in artificial intelligence. Pitman, LondonGoogle Scholar
  11. Gilbert N, Troitzsch KG (2005) Simulation for the social scientist. Open University Press, BerkshireGoogle Scholar
  12. Harland K, Heppenstall AJ, Smith DM, Birkin MH (2012) Creating realistic synthetic populations at varying spatial scales: a comparative critique of population synthesis techniques. J Artif Soc Soc Simul 15:1Google Scholar
  13. Kennell DL, Sheils JF (1990) PRISM: dynamic simulation of pension and retirement income. In: Lewis GH, Michel RC (eds) Microsimulation techniques for tax and transfer analysis. The Urban Institute Press, Washington, DCGoogle Scholar
  14. Lambert S, Percival R, Schofield D, Paul S (1994) An introduction to STINMOD: a static microsimulation Model, NATSEM Technical Paper No 1. University of Canberra, CanberraGoogle Scholar
  15. Liu R (2005) The DRACULA dynamic network microsimulation model. In: Kitamura R, Kuwahara M (eds) Simulation approaches in transportation analysis: recent advances and challenges. Springer, pp. 23–56. ISBN0-387-24108-6Google Scholar
  16. Moon G, Quarendon G, Barnard S, Twigg L, Blyth B (2007) Fat nation: deciphering the distinctive geographies of obesity in England. Soc Sci Med 65(1):25–31Google Scholar
  17. O’Donoghue C (2001) Dynamic microsimulation: a methodological survey. Brazilian Elect J Econ 4:2Google Scholar
  18. Openshaw S (1995) Developing automated and smart spatial pattern exploration tools for geographical information systems applications. Statistician 44:3–16CrossRefGoogle Scholar
  19. Openshaw S, Rao L (1995) Algorithms for reengineering 1991 census geography. Environ Plann A 27:425–446CrossRefGoogle Scholar
  20. Otten RHJM, van Ginneken LPPP (1989) The annealing algorithm. The Springer Int Ser Engin Comp Sci 72(1):5–17Google Scholar
  21. Redmond G, Sutherland H, Wilson M (1998) The arithmetic of tax and social security reform: a user’s guide to microsimulation: methods and analysis. Cambridge University Press, CambridgeGoogle Scholar
  22. Rephann TJ (1999) The education module for SVERIGE: Documentation V 1.0. Available at:
  23. Smith DM, Clarke GP, Harland K (2009) Improving the synthetic data generation process in spatial microsimulation models. Environ Plann A 41(5):1251–1268CrossRefGoogle Scholar
  24. Smith DM, Pearce JR, Harland K (2011) Can a deterministic spatial microsimulation model provide reliable small-area estimates of health behaviors? An example of smoking prevalence in New Zealand. Health Place 17:618–624CrossRefGoogle Scholar
  25. Voas D, Williamson P (2000) An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata. Int J Popul Geogr 6:349–366CrossRefGoogle Scholar
  26. Voas D, Williamson P (2001) Evaluating goodness-of-fit measures for synthetic microdata. Geograph Environ Model 5:177–200CrossRefGoogle Scholar
  27. Williamson P, Clarke GP (1996) Estimating small-area demands for water with the use of microsimulation. In: Clarke GP (ed) Microsimulation for urban and regional policy analysis. Pion, London, pp 117–148Google Scholar
  28. Williamson P, Birkin M, Rees P (1998) The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environ Plann A 30:785–816CrossRefGoogle Scholar
  29. Wu BM, Birkin MH (2012) Agent-based extensions to a spatial microsimulation model of demographic change. In Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Dordrecht, pp 347–360CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of GeographyUniversity of LeedsLeedsUK
  2. 2.Queen Mary UniversityLondonUK

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