Advertisement

Microsimulation of Rural Households

  • Eveline S. van Leeuwen
Chapter
Part of the Contributions to Economics book series (CE)

Abstract

Microsimulation (MSM) is a technique that aims at modelling the likely behaviour of individual persons, households, or individual firms. In these models, agents represent members of a population for the purpose of studying how individual (i.e. micro-) behaviour generates aggregate (i.e. macro-) regularities from the bottom-up (e.g. Epstein, Complexity 4: 41–60, 1999). This results in a natural instrument to anticipate trends in the environment by means of monitoring and early warning, as well as to predict and value the short-term and long-term consequences of implementing certain policy measures (Saarloos, A Framework for a Multi-Agent Planning Support System, PhD thesis, Eindhoven University Press Facilities, Eindhoven, 2006). The simulations can be helpful in showing (a bandwidth of) spatial dynamics, especially if linked to geographical information systems. In this chapter, the development of the spatial MSM model SIMtown will be described. This model simulates the total population of Nunspeet and Oudewater, including a large number of household characteristics, several of which are relevant to predict the shopping behaviour. In the second part of the chapter, the simulated micropopulation will be used to show household characteristics which were previously not available and which are useful for local policy makers.

Keywords

Constraint Variable Shopping Behaviour Synthetic Population Young Household Planning Support 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.

References

  1. Ballas, D., Clarke, G., Dorling, D., Rigby, J., & Wheeler, B. (2006). Using geographical information systems and spatial microsimulation for the analysis of health inequalities. Health Informatics Journal, 12(1), 65–79.CrossRefGoogle Scholar
  2. Ballas, D., Clarke, G. P., & Wiemers, E. (2005). Building a dynamic spatial microsimulation model for Ireland. Population, Space and Place, 11, 157–172.CrossRefGoogle Scholar
  3. Ballas, D., Rossiter, D., Thomas, B., Clarke, G. P., & Dorling, D. (2005). Geography matters. Simulating the local impacts of national social policies. Leeds: Joseph Rowntree Foundation.Google Scholar
  4. Bousquet, F. & le Page, C. (2004). Multi-agent simulations and ecosystem management: a review. Ecological Modelling, 176, 313–332.CrossRefGoogle Scholar
  5. Brown, L. & Harding, A. (2002). Social modelling and public policy: application of microsimulation modelling in Australia. Journal of Artificial Societies and Social Simulation, 5(4) (http://jasss.soc.surrey.ac.uk/5/4/6.html).
  6. Clarke, G. P. (1996). Microsimulation: an introduction. In G. P. Clarke (Ed.), Microsimulation for urban and regional policy analysis (pp. 1–9). London: Pion.Google Scholar
  7. Clarke, M. & Holm, E. (1987). Microsimulation methods in spatial analysis and planning. Geografiska Annaler, 69B(2), 145–164.CrossRefGoogle Scholar
  8. Clarke, G. P. & Madden, M. (eds). (2001). Regional science in business. Berlin: Springer.Google Scholar
  9. Cullinan, J. E., O’Donoghue, C., & Hyne, S. (2006). Using spatial microsimulation modelling techniques and geographic information systems to estimate the demand for outdoor recreation in Ireland. Paper presented at the 8th Nordic Seminar on Microsimulation Models. Oslo, Norway, 8–9 June, 2006.Google Scholar
  10. Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.CrossRefGoogle Scholar
  11. Hanaoka, K. & Clarke, G. P. (2007). Spatial microsimulation modelling for retail market analysis at the small-area leve. Computers, Environment and Urban Systems, 31(2), 162–187.CrossRefGoogle Scholar
  12. Hewitt, C. (1977). Viewing control structures as patterns of passing messages. Artificial Intelligence, 8, 323–364.CrossRefGoogle Scholar
  13. Hollenbeck, K. (1995). A review of retirement income policy models. Upjohn Institute Staff Working Paper 95–38.Google Scholar
  14. Isard, W., Azis, I. J., Drennan, M. P., Miller, R. E., Saltzman, S., & Thorbecke, E. (1998). Methods of interregional and regional analysis. Aldershot: Ashgate.Google Scholar
  15. Leontief, W. (1951). The structure of the American economy. New York: Oxford University Press.Google Scholar
  16. Li, X. & Liu, X. (2007). Defining agents’ behaviors to simulate complex residential development using multicriteria evaluation. Journal of Environmental Management, 85(4), 1063–1075.CrossRefGoogle Scholar
  17. Mertz, J. (1991). Microsimulation – A survey of principles developments and applications. International Journal of Forecasting, 7, 77–104.CrossRefGoogle Scholar
  18. Nelissen, J. H. M. (1993). Labour market, income formation and social security in the microsimulation model NEDYMAS. Economic Modelling, 10(3), 225–272.CrossRefGoogle Scholar
  19. Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123.CrossRefGoogle Scholar
  20. Orcutt, G., Caldwell, S., & Wertheimer, R. (1976). Policy exploration through microanalytic simulation. Washington, DC: The Urban Institute.Google Scholar
  21. Rephann, T. J. & Holm, E. (2004). Economic-demographic effects of immigration: Results from a dynamic spatial microsimulation model. International Regional Science Review, 27(4), 379–410.CrossRefGoogle Scholar
  22. Smith, D. M., Clarke, G. P., Ransley, J., & Cade, J. (2006). Food access and health: a microsimulation framework for analysis. Studies in Regional Science, 35(4), 909–927.CrossRefGoogle Scholar
  23. Smith, D. M., Harland, K., & Clarke, G. P. (2007). SimHealth: estimating small area populations using deterministic spatial microsimualtion in Leeds and Bradford. Working paper 07/06. Leeds: University of Leeds.Google Scholar
  24. Sonsbeek, J. M. & Gradus, R. H. J. M. (2005). A microsimulation analysis of the 2006 regime change in the Dutch disability scheme. Economic Modelling, 23(3), 427–456.CrossRefGoogle Scholar
  25. van Leeuwen, E. S., Hagens, J. E., & Nijkamp, P. (2007). Multi-agent systems: a tool in spatial planning. The example of a microsimulation of retail developments. DISP, 170(3), 19–32.Google Scholar
  26. Veldhuizen, J., Timmermans, H., & Kapoen, L. (2000). RAMBLAS: a regional planning model based on the microsimulation of daily activity travel patterns. Environment and Planning A, 32, 427–443.CrossRefGoogle Scholar
  27. Voas, D. & Williamson, P. (2001). Evaluating goodness-of-fit measures for synthetic microdata. Geographical and Environmental Modelling, 5(2), 177–200.CrossRefGoogle Scholar
  28. Zaidi, A. & Rake, K. (2001). Dynamic microsimulation models: a review and some lessons for SAGE. SAGE discussion paper no.2. London: London School of Economics.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Spatial EconomicsVU University AmsterdamAmsterdamThe Netherlands

Personalised recommendations