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Improving the Synthetic Data Generation Process in Spatial Microsimulation Models

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  • Dianna M Smith

    (Department of Geography, Queen Mary, University of London, Mile End Road, London E1 4NS, England)

  • Graham P Clarke
  • Kirk Harland

Abstract

Simulation models are increasingly used in applied research to create synthetic micro-populations and predict possible individual-level outcomes of policy intervention. Previous research highlights the relevance of simulation techniques in estimating the potential outcomes of changes in areas such as taxation and child benefit policy, crime, education, or health inequalities. To date, however, there is very little published research on the creation, calibration, and testing of such micro-populations and models, and little on the issue of how well synthetic data can fit locally as opposed to globally in such models. This paper discusses the process of improving the process of synthetic micropopulation generation with the aim of improving and extending existing spatial microsimulation models. Experiments using different variable configurations to constrain the models are undertaken with the emphasis on producing a suite of models to match the different sociodemographic conditions found within a typical city. The results show that creating processes to generate area-specific synthetic populations, which reflect the diverse populations within the study area, provides more accurate population estimates for future policy work than the traditional global model configurations.

Suggested Citation

  • Dianna M Smith & Graham P Clarke & Kirk Harland, 2009. "Improving the Synthetic Data Generation Process in Spatial Microsimulation Models," Environment and Planning A, , vol. 41(5), pages 1251-1268, May.
    • Handle: RePEc:sae:envira:v:41:y:2009:i:5:p:1251-1268
    DOI: 10.1068/a4147
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    References listed on IDEAS

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    1. Sutherland, Holly & Piachaud, David, 2001. "Reducing Child Poverty in Britain: An Assessment of Government Policy 1997-2001," Economic Journal, Royal Economic Society, vol. 111(469), pages 85-101, February.
    2. Ann Harding & Neil Warren & Gillian Beer & Ben Phillips & Kwabena Osei, 2002. "The Distributional Impact of Selected Commonwealth Outlays and Taxes and Alternative Commonwealth Grant Allocation Mechanisms," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 35(3), pages 325-334, September.
    3. Moon, Graham & Quarendon, Gemma & Barnard, Steve & Twigg, Liz & Blyth, Bill, 2007. "Fat nation: Deciphering the distinctive geographies of obesity in England," Social Science & Medicine, Elsevier, vol. 65(1), pages 20-31, July.
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    Cited by:

    1. Ian Philips & Graham Clarke & David Watling, 2017. "A Fine Grained Hybrid Spatial Microsimulation Technique for Generating Detailed Synthetic Individuals from Multiple Data Sources: An Application To Walking And Cycling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 167-200.
    2. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    3. Lovelace, Robin & Ballas, Dimitris & Watson, Matt, 2014. "A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels," Journal of Transport Geography, Elsevier, vol. 34(C), pages 282-296.
    4. Kii, Masanobu & Nakanishi, Hitomi & Nakamura, Kazuki & Doi, Kenji, 2016. "Transportation and spatial development: An overview and a future direction," Transport Policy, Elsevier, vol. 49(C), pages 148-158.

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