Skip to main content

Spatial Microsimulation and Agent-Based Modelling

  • Chapter
  • First Online:
The Practice of Spatial Analysis

Abstract

This chapter critically reviews the state-of-the-art in spatial microsimulation and agent-based modelling approaches with an emphasis on efforts to combine them in order to address applied geography problems. Spatial microsimulation typically involves the merging of census and social survey data to simulate a population of individuals within households (for different geographical units) whose characteristics are as close to the real population as it is possible to estimate (and for small areas for which this information is not available from published sources). Microsimulation is closely linked conceptually to another type of individual-level modelling: agent-based models (ABM). ABM are normally associated with the behaviour of multiple agents in a social or economic system. This chapter offers an overview of the state-of-the-art of both modelling approaches as well as a discussion of attempts to combine them, with an articulation of a relevant research agenda.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Auchincloss, A. H., & Diez Roux, A. V. (2008). A new tool for epidemiology: The usefulness of dynamic-agent models in understanding place effects on health. American Journal of Epidemiology, 168(1), 1–8.

    Article  Google Scholar 

  • Auchincloss, A. H., Riolo, R. L., Brown, D. G., Cook, J., & Diez Roux, A. V. (2011). An agent-based model of income inequalities in diet in the context of residential segregation. American Journal of Preventative Medicine, 40(3), 303–311.

    Article  Google Scholar 

  • Axelrod, R. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton: Princeton University Press.

    Google Scholar 

  • Ballas, D. (2004). Simulating trends in poverty and income inequality on the basis of 1991 and 2001 census data: A tale of two cities. Area, 36, 146–163.

    Article  Google Scholar 

  • Ballas, D., & Clarke, G. P. (2000). GIS and microsimulation for local labour market policy analysis. Computers, Environment and Urban Systems, 24, 305–330.

    Article  Google Scholar 

  • Ballas, D., Clarke, G. P., Dorling, D., Eyre, H., Rossiter, D., & Thomas, B. (2005a). SimBritain: A spatial microsimulation approach to population dynamics. Population, Space and Place, 11, 13–34.

    Article  Google Scholar 

  • Ballas, D., Clarke, G. P., & Wiemers, E. (2005b). Building a dynamic spatial microsimulation model for Ireland. Population, Space and Place, 11, 157–172.

    Article  Google Scholar 

  • Ballas, D., Clarke, G. P., & Wiemers, E. (2006). Spatial microsimulation for rural policy analysis in Ireland: The implications of CAP reforms for the national spatial strategy. Journal of Rural Studies, 22, 367–378.

    Article  Google Scholar 

  • Ballas, D., Kingston, R., Stillwell, J., & Jin, J. (2007a). Building a spatial microsimulation-based planning support system for local policy making. Environment and Planning A, 39(10), 2482–2499.

    Article  Google Scholar 

  • Ballas, D., Clarke, G. P., Dorling, D., & Rossiter, D. (2007b). Using SimBritain to model the geographical impact of national government policies. Geographical Analysis, 39(1), 44–77.

    Article  Google Scholar 

  • Ballas, D., & Clarke, G. P. (2009). Spatial microsimulation. In A. S. Fotheringham & P. A. Rogerson (Eds.), Handbook of spatial analysis (pp. 277–298). Thousand Oaks: Sage.

    Google Scholar 

  • Ballas, D., Clarke, G. P., Hynes, S., Morrissey, K., & O’Donoghue, C. (2012). Introduction. In C. O’Donoghue, D. Ballas, G. P. Clarke, S. Hynes, & K. Morrissey (Eds.), Spatial microsimulation for rural policy analysis (pp. 1–10). New York: Springer.

    Google Scholar 

  • Ballas, D., Clarke, G. P., Hynes, S., Morrissey, K., & O’Donoghue, C. (2013). A review of microsimulation for policy analysis. In C. O’Donoghue, D. Ballas, G. P. Clarke, S. Hynes, & K. Morrissey (Eds.), Spatial microsimulation for rural policy analysis (pp. 35–54). Berlin: Springer.

    Chapter  Google Scholar 

  • Ballas, D., Clarke, G., Franklin, R. S., & Newing, A. (2017). GIS and the social sciences: Theory and applications. London: Routledge.

    Book  Google Scholar 

  • Batty, M., & Densham, P. (1996). Decision support, GIS and urban planning. Systemma Terra, 1, 72–76.

    Google Scholar 

  • Batty, M., Desyllas, J., & Duxbury, E. (2003). Safety in numbers? Modelling crowds and designing control for the Notting Hill Carnival. Urban Studies, 40(8), 1573–1590.

    Article  Google Scholar 

  • Batty, M., Barros, J., Alves, S. Jr. (2004). Cities: Continuity, transformation, and emergence. CASA Working Paper—Paper 72. Retrieved from http://discovery.ucl.ac.uk/214/1/paper72.pdf

  • Birkin, M., & Clarke, M. (1988). SYNTHESIS—A synthetic spatial information system for urban and regional analysis: Methods and examples. Environment and Planning A, 20, 1645–1671.

    Article  Google Scholar 

  • Birkin, M., & Clarke, M. (1989). The generation of individual and household incomes at the small area level using synthesis. Regional Studies, 23, 535–548.

    Article  Google Scholar 

  • Birkin, M., & Clarke, M. (2011). Spatial microsimulation models: A review and a glimpse into the future. In J. Stillwell, M. Clarke, & J. Stillwell (Eds.), Population dynamics and projection methods. Understanding population trends and processes (Vol. 4, pp. 193–208). Netherlands: Springer.

    Chapter  Google Scholar 

  • Boman, M., & Holm, E. (2004). Multi-Agent systems, time geography, and microsimulations. In M. Olsson & G. Sjöstedt (Eds.), Systems approaches and their application (pp. 95–118). New York: Springer.

    Google Scholar 

  • Burke, D. S., Epstein, J. M., Cummings, D. A. T., Parker, J. I., Cline, K. C., Singa, R. M., & Chakravarty, S. (2006). Individual-based computational modeling of smallpox epidemic control strategies. Academic Emergency Medicine, 13(11), 1142–1149.

    Article  Google Scholar 

  • Cajka, J. C., Cooley, P. C., & Wheaton, W. D. (2010). Attribute assignment to a synthetic population in support of agent-based disease modeling. Methods Report RTI Press, 19(1009), 1–14.

    Google Scholar 

  • Campbell, M. H. (2011). Exploring the social and spatial inequalities of ill-health in Scotland: A spatial microsimulation approach. PhD thesis, University of Sheffield. Retrieved from http://etheses.whiterose.ac.uk/1942/

  • Campbell, M. H., & Ballas, D. (2013). A spatial micro simulation approach to economic policy analysis in Scotland. Regional Science Policy and Practice, 5(3), 263–288.

    Article  Google Scholar 

  • Campbell, M., & Ballas, D. (2016). SimAlba: A spatial microsimulation approach to the analysis of health inequalities. Frontiers in Public Health, 4(230).

    Google Scholar 

  • Cerda, M., Tracy, M., Ahern, J., & Galea, S. (2014). Addressing population health and health inequalities: The role of fundamental causes. American Journal of Public Health, 104(4), 609–619.

    Article  Google Scholar 

  • Clarke, G. P. (Ed.). (1996). Microsimulation for urban and regional policy analysis. London: Pion.

    Google Scholar 

  • Clarke, G. P., Clarke, C., Birkin, M., Rees, P. H., & Wilson, A. G. (1984). A strategic planning simulation model of a district health service system: The in-patient component and results. Number Bd. 385-389 (p. 1984). Leeds: School of Geography, University of Leeds.

    Google Scholar 

  • Clarke, M., & Wilson, A. (1985). The dynamics of urban spatial structure: The progress of a research programme. Transactions of the Institute of British Geographers, 10(4), 427–451.

    Article  Google Scholar 

  • Crooks, A. T. (2008). Constructing and implementing an agent-based model of residential segregation through vector GIS. UCL Working Paper Series, Paper 133. Retrieved from http://discovery.ucl.ac.uk/15185/1/15185.pdf

  • Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems. London: Springer.

    Google Scholar 

  • Davidsson, P. (2000). Multi agent based simulation: Beyond social simulation. In S. Moss & P. Davidsson (Eds.), Multi agent based simulations. Berlin: Springer.

    Google Scholar 

  • Dawson, R. J., Peppe, R., & Wang, M. (2011). An agent-based model for risk-based flood incident management. Natural Hazards, 59(1), 167–189.

    Article  Google Scholar 

  • Edwards, K. L., & Clarke, G. P. (2009). The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity. Social Science and Medicine, 69(7), 1127–1134.

    Article  Google Scholar 

  • Edwards, K. L., Clarke, G. P., Ransley, J. K., et al. (2010). The neighbourhood matters: Studying exposures relevant to childhood obesity and the policy implications in Leeds, UK. Journal of Epidemiology and Community Health, 64(3). Retrieved from http://jech.bmj.com/content/64/3/194

    Article  Google Scholar 

  • Epstein, J. M., & Axtell, R. L. (1996). Growing artificial societies: Social science from the bottom up. Cambridge: The MIT Press.

    Google Scholar 

  • Farrell, N., Morrissey, K., & O’Donoghue, C. (2013). Creating a spatial microsimulation model of the Irish Local Economy. In Tanton & Edwards (Eds.), Spatial microsimulation: A reference guide for users (pp. 105–125). New York: Springer.

    Google Scholar 

  • Ferguson, M., Maoh, H., Ryan, J., Kanaroglou, P., & Rashidi, T. H. (2012). Transferability and enhancement of a microsimulation model for estimating urban commercial vehicle movements. Journal of Transport Geography, 24, 358–369.

    Article  Google Scholar 

  • Gardner, M. (1970). The fantastic combinations of John Conway’s new solitaire game “Life”. Scientific American, 223, 120–123.

    Article  Google Scholar 

  • Gorman, D. M., Mezic, J., Mezic, I., & Gruenewald, P. J. (2006). Agent-based modelling of drinking behaviour: A preliminary model and potential applications to theory and practice. American Journal of Public Health, 96(11), 2055–2060.

    Article  Google Scholar 

  • Hancock, R., & Sutherland, H. (Eds.). (1992). Microsimulation models for public policy analysis: New frontiers. London: Suntory-Toyota International Centre for Economics and Related Disciplines – LSE.

    Google Scholar 

  • Harding, A. (Eds.) (1996). Microsimulation and public policy, contributions to economic analysis 232, Amsterdam, North Holland.

    Google Scholar 

  • Heppenstall, A. J., Evans, A. J., & Birkin, M. H. (2005). A hybrid multi-agent/spatial interaction model system for petrol price setting. Transactions in GIS, 9(1), 35–51.

    Article  Google Scholar 

  • Heppenstall, A. J., Evans, A. J., & Birkin, M. H. (2006). Application of multi-agent systems to modelling a dynamic, locally interacting retail market. JASSS, 9(3).

    Google Scholar 

  • Heppenstall, A. J., Evans, A. J., & Birkin, M. H. (2007). Genetic algorithm optimisation of a multi-agent system for simulating a retail market. Environment and Planning B, 34, 1051–1070.

    Article  Google Scholar 

  • Johnson, S. D., & Groff, E. R. (2014). Strengthening theoretical testing in criminology using agent-based modeling. Journal of Research in Crime and Delinquency, 51(4), 509–525.

    Article  Google Scholar 

  • Jones, P. (2017). A spatial microsimulation approach to the analysis of health inequalities and health resilience. Unpublished PhD thesis, University of Sheffield.

    Google Scholar 

  • Jones, P. M., Lovelace, R., & Dumont, M. (2017). rakeR: Easy spatial microsimulation (raking) in R. https://doi.org/10.5281/zenodo.821506. Retrieved from https://cran.r-project.org/package=rakeR

  • Kavroudakis, D., & Ballas, D. (2011). Agent-based modelling for labour force analysis, Annual meeting of European Association of Geographers (Eurogeo), Athens, Greece, 2-5 June 2011.

    Google Scholar 

  • Kavroudakis, D., Ballas, D., & Birkin, M. (2013). A spatial microsimulation approach to the analysis of social and spatial inequalities in higher education attainment. Applied Spatial Analysis and Policy, 3, 1–23.

    Article  Google Scholar 

  • Lovelace, R., & Dumont, M. (2016). Spatial microsimulation in R. Boca Raton, FL: CRC Press/Taylor and Francis Group.

    Book  Google Scholar 

  • Lovelace, R, Ballas, D, Watson, M (2014), A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels, Journal of Transport Geography, vol. 34, 282–296.

    Article  Google Scholar 

  • Lovelace, R., Birkin, M., Ballas, D., & van Leeuwen, E. (2015). Evaluating the performance of iterative proportional fitting for spatial microsimulation: New tests for an established technique. Journal of Artificial Societies and Social Simulation, 18(2), 21.

    Article  Google Scholar 

  • Lovelace, R., & Ballas, D. (2013). ‘Truncate, replicate, sample’: A method for creating integer weights for spatial microsimulation. Computers, Environment and Urban Systems, 41, 1–11.

    Article  Google Scholar 

  • Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.

    Article  Google Scholar 

  • Malleson, N., Evans, A., Heppenstall, A., & See, L. (2010). Evaluating an agent-based model of burglary. University of Leeds Working Paper. Retrieved from http://www.geog.leeds.ac.uk/fileadmin/downloads/school/research/wpapers/10_1.pdf

  • Malleson, N., Heppenstall, A., See, L., & Evans, A. (2013). Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk. Environment and Planning B: Planning and Design, 40(3), 405–426.

    Article  Google Scholar 

  • Maoh, H., & Kanaroglou, P. (2012). Modelling firm failure: Towards building a firmographic microsimulation model. In F. Pagliara, M. de Bok, D. Simmonds, & A. Wilson (Eds.), Employment location in cities and regions (pp. 243–261). Berlin: Springer.

    Google Scholar 

  • Merler, S., Ajelli, M., Fumanelli, L., & Vespignani, A. (2013). Containing the accidental laboratory escape of potential pandemic influenza viruses. BMC Medicine, 11, 252.

    Article  Google Scholar 

  • Miller, H. (2018). Agent-based activity/travel microsimulation: What’s next? In H. Briassoulis, D. Kavroudakis, & N. Soulakellis (Eds.), The Practice of Spatial Analysis: Essays in memory of Professor Pavlos Kanaroglou. New York: Springer.

    Google Scholar 

  • Mitchell, R., Dorling, D., & Shaw, M. (2002). Population production and modelling mortality—an application of geographic information systems in health inequalities research. Health and Place, 8, 15–24.

    Article  Google Scholar 

  • Mitton, L., Sutherland, H., & Weeks, M. (Eds.). (2000). Microsimulation modelling for policy analysis: Challenges and innovations. Cambridge: Cambridge University Press.

    Google Scholar 

  • Morrissey, K., Clarke, G., Ballas, D., Hynes, S., & O’Donoghue, C. (2008). Examining access to GP services in rural Ireland using microsimulation analysis. Area, 40(3), 354–364.

    Article  Google Scholar 

  • Nakaya, T., Fotheringham, A. S., Hanaoka, K., Clarke, G., Ballas, D., & Yano, K. (2007). Combining microsimulation and spatial interaction models for retail location analysis. Journal of Geographical Systems, 9, 345–369.

    Article  Google Scholar 

  • O’Neil, C. A., & Sattenspiel, L. (2010). Agent-based modeling of the spread of the 1918-1919 flu in three Canadian fur trading communities. American Journal of Human Biology, 22(6), 757–767.

    Article  Google Scholar 

  • Openshaw, S. (1995). Human systems modelling as a new grand challenge area in science. Environment and Planning A, 27, 159–164.

    Article  Google Scholar 

  • Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39, 116–123.

    Article  Google Scholar 

  • Orcutt, G. H., Greenberger, M., Korbel, J., & Rivlin, A. (1961). Microanalysis of socioeconomic systems: A simulation study. New York: Harper and Row.

    Google Scholar 

  • Panori, A., Ballas, D., & Psycharis, Y. (2017). SimAthens: A spatial microsimulation approach to the estimation and analysis of small-area income distributions and poverty rates in Athens, Greece. Computers, Environment and Urban Systems, 63, 15–25.

    Article  Google Scholar 

  • Potter, M. A., Brown, S. T., Cooley, P. C., Sweeney, P. M., Hershey, P. B., Gleason, S. M., Lee, B. Y., Keane, C. R., Grefenstette, J., & Burke, D. (2012). School closure as an influenza mitigation strategy: How variations in legal authority and plan criteria can alter the impact. BMC Public Health, 12, 977.

    Article  Google Scholar 

  • Procter, K. L., Clarke, G. P., Ransley, J. K., et al. (2008). Micro-level analysis of childhood obesity, diet, physical activity, residential and social capital variables: Where are the obesogenic environments in Leeds? Area, 40(3), 323–340.

    Article  Google Scholar 

  • Ryan, J., Maoh, H., & Kanaroglou, P. S. (2009). Population synthesis for microsimulating urban residential mobility, Transportation Research Board 89th Annual Meeting.

    Google Scholar 

  • Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.

    Article  Google Scholar 

  • Svinterikou, M., & Kanaroglou, P. (2006). A microsimulation approach to the modelling of urban population and housing markets within an object-oriented framework, ERSA conference papers, European Regional Science Association.

    Google Scholar 

  • Tanton, R., & Edwards, K. L. (2013). Spatial microsimualtion: A reference guide for users. London: Springer.

    Book  Google Scholar 

  • Teweldemedhin, E., Marwala, T., & Mueller, C. (2004). Agent-based modelling: A case study in HIV epidemic. In Fourth International Conference on Hybrid Intelligent Systems (pp. 154–159).

    Google Scholar 

  • The Logo Foundation. (2016). Retrieved from http://el.media.mit.edu/logo-foundation/

  • Tisue, S., & Wilensky, U. (2004). NetLogo: Design and implementation of a multi-agent modelling environment. Center for Connected Learning and Computer-Based Modeling Northwestern University, Evanston, IL. Retrieved from https://ccl.northwestern.edu/papers/2013/netlogo-agent2004c.pdf.

  • Tomintz, M. N., Clarke, G. P., & Rigby, J. E. (2008). The geography of smoking in Leeds: Estimating individual smoking rates and the implications for the location of stop smoking services. Area, 40(3), 341–353.

    Article  Google Scholar 

  • Torrens, P. M., & McDaniel, A. W. (2013). Modeling geographic behaviour in riotous crowds. Annals of the Association of America Geographers, 103(1), 20–46.

    Article  Google Scholar 

  • Voas, D. W., & Williamson, P. (2000). An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata. International Journal of Population Geography, 6, 349–366.

    Article  Google Scholar 

  • Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston, IL. Retrieved from https://ccl.northwestern.edu/netlogo/.

  • Williamson, P. (1992). Community care policies for the elderly: A microsimulation approach. Unpublished PhD Thesis, School of Geography, University of Leeds, Leeds.

    Google Scholar 

  • Williamson, P. (1999). Microsimulation: An idea whose time has come?. Paper Presented at the 39th European Regional Science Association Congress, University College Dublin, Dublin, Ireland, 23–27 August 1999.

    Google Scholar 

  • Williamson, P. (2001). Modelling alternative domestic water demand scenarios. In G. Clarke & M. Madden (Eds.), Regional science in business (pp. 243–268). Berlin: Springer.

    Chapter  Google Scholar 

  • Williamson, P., Birkin, M., & Rees, P. (1998). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30, 785–816.

    Article  Google Scholar 

  • Wilson, A. G. (2000). Complex spatial systems: The modelling foundations of urban and regional analysis. London: Prentice Hall.

    Google Scholar 

  • Wilson, A., & Pownall, C. E. (1976). A new representation of the urban system for modelling and for the study of micro-level interdependence. Area, 8, 246–254.

    Google Scholar 

  • Wu, B. M., & Birkin, M. H. (2012). Agent-based extensions to a spatial microsimulation model of demographic change. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems. London: Springer.

    Google Scholar 

  • Wu, B., Birkin, M., & Rees, P. (2008). A spatial microsimulation model with student agents. Computers Environment and Urban Systems, 32, 440–453.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dimitris Ballas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ballas, D., Broomhead, T., Jones, P.M. (2019). Spatial Microsimulation and Agent-Based Modelling. In: Briassoulis, H., Kavroudakis, D., Soulakellis, N. (eds) The Practice of Spatial Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-89806-3_4

Download citation

Publish with us

Policies and ethics