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A Review of Spatial Microsimulation Methods

Author

Listed:
  • Robert Tanton

    (ational Centre for Social and Economic Modelling (NATSEM),)

Abstract
This paper outlines a framework for spatial microsimulation models, gives some reasons why someone may want to use a spatial microsimulation model, describes the development of spatial microsimulation over the last 30 years, summarises the different methods currently used for spatial microsimulation, and outlines how the models can be validated. In reviewing the reasons and methods for spatial microsimulation, we conclude that spatial microsimulation provides an alternative to other small area estimation methods, providing flexibility by allowing cross-tabulations to be built, and an ability to link to other models, and derive projections. Spatial microsimulation models also allow demographic changes, like births and deaths, to be included in a dynamic microsimulation model. This also allows what if scenarios to be modelled, for example, what would happen if the birth rate increased over time. Validation of the spatial microsimulation models shows that they are now at the stage where they can provide reliable result

Suggested Citation

  • Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.
    • Handle: RePEc:ijm:journl:v:7:y:2014:i:1:p:4-25
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    File URL: http://microsimulation.org/IJM/V7_1/2-IJM_7_1_Tanton_.pdf
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    References listed on IDEAS

    as
    1. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    2. Alex Anas & Richard Arnott & Kenneth A. Small, 1998. "Urban Spatial Structure," Journal of Economic Literature, American Economic Association, vol. 36(3), pages 1426-1464, September.
    3. Cathal O'Donoghue & John Lennon & Stephen Hynes, 2009. "The Life-Cycle Income Analysis Model (LIAM): a study of a flexible dynamic microsimulation modelling computing framework," International Journal of Microsimulation, International Microsimulation Association, vol. 2(1), pages 16-31.
    4. Ben Phillips & S.F. Chin & Ann Harding, 2007. "Housing Stress Today: Estimates for Statistical Local Areas in 2005," NATSEM Working Paper Series 2006 019, University of Canberra, National Centre for Social and Economic Modelling.
    5. Yogi Vidyattama & Maheshwar Rao & Itismita Mohanty & Robert Tanton, 2014. "Modelling the impact of declining Australian terms of trade on the spatial distribution of income," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 100-126.
    6. Itismita Mohanty & Robert Tanton & Yogi Vidyattama & Marcia Keegan & Robert Cummins, 2013. "‘Small area estimates of Subjective Wellbeing: Spatial Microsimulation on the Australian Unity Wellbeing Index Survey’," NATSEM Working Paper Series 13/23, University of Canberra, National Centre for Social and Economic Modelling.
    7. Robert Tanton & Yogi Vidyattama & Justine McNamara & Quoc Ngu Vu & Ann Harding, 2009. "Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians," Economic Papers, The Economic Society of Australia, vol. 28(2), pages 102-120, June.
    8. repec:bla:ecorec:v:85:y:2009:i:s1:p:s48-s58 is not listed on IDEAS
    9. Yogi Vidyattama & Rebecca Cassells & Ann Harding & Justine Mcnamara, 2013. "Rich or Poor in Retirement? A Small Area Analysis of Australian Private Superannuation Savings in 2006 Using Spatial Microsimulation," Regional Studies, Taylor & Francis Journals, vol. 47(5), pages 722-739, May.
    10. P Williamson & M Birkin & P H Rees, 1998. "The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records," Environment and Planning A, , vol. 30(5), pages 785-816, May.
    11. Yogi Vidyattama & Robert Tanton & Nicholas Biddle, 2013. "‘Small Area Social Indicators for the Indigenous Population: Synthetic data methodology for creating small area estimates of Indigenous disadvantage’," NATSEM Working Paper Series 13/24, University of Canberra, National Centre for Social and Economic Modelling.
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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. Joachim Merz & Lars Rusch, 2015. "MICSIM-4j - A General Microsimulation Model User Guide (Version 1.1)," FFB-Discussionpaper 100, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    4. Mohamed Khachman & Catherine Morency & Francesco Ciari, 2024. "Integrated multiresolution framework for spatialized population synthesis," Transportation, Springer, vol. 51(3), pages 823-852, June.
    5. Frederik Priem & Philip Stessens & Frank Canters, 2020. "Microsimulation of Residential Activity for Alternative Urban Development Scenarios: A Case Study on Brussels and Flemish Brabant," Sustainability, MDPI, vol. 12(6), pages 1-28, March.
    6. Trond Husby & Olga Ivanova & Mark Thissen, 2018. "Simulating the Joint Distribution of Individuals, Households and Dwellings in Small Areas," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 169-190.
    7. M. Esteban Muñoz H. & Ivan Dochev & Hannes Seller & Irene Peters, 2016. "Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 66-88.
    8. Sebastian Dräger & Johannes Kopp & Ralf Münnich & Simon Schmaus, 2021. "Analyse der Grundschulversorgung in Trier mit Hilfe kleinräumiger Mikrosimulationsmodelle," Research Papers in Economics 2021-01, University of Trier, Department of Economics.
    9. Alberto Vitalini & Simona Ballabio & Flavio Verrecchia, 2024. "Rebuilding a pseudo population register for estimating physical vulnerability at the local level: a case study of spatial microsimulation in Sondrio," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 78(1), pages 55-64, January-M.
    10. Dana R. Thomson & Lieke Kools & Warren C. Jochem, 2018. "Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia," Data, MDPI, vol. 3(3), pages 1-19, August.
    11. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    12. Burgard, Jan Pablo & Krause, Joscha & Schmaus, Simon, 2021. "Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    13. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    14. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    15. Sebastian Dräger & Johannes Kopp & Ralf Münnich & Simon Schmaus, 2022. "Die zukünftige Entwicklung der Grundschulversorgung im Kontext ausgewählter Wanderungsszenarien [The future development of primary school demand in the context of selected migration scenarios]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(1), pages 51-77, March.
    16. Jan Weymeirsch & Julian Ernst & Ralf Münnich, 2024. "Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations," Mathematics, MDPI, vol. 12(10), pages 1-25, May.
    17. Jan Pablo Burgard & Joscha Krause & Simon Schmaus, 2019. "Estimation of Regional Transition Probabilities for Spatial Dynamic Microsimulations from Survey Data Lacking in Regional Detail," Research Papers in Economics 2019-12, University of Trier, Department of Economics.
    18. Robert Tanton, 2018. "Spatial Microsimulation: Developments and Potential Future Directions," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 143-161.
    19. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    20. Philips, Ian & Anable, Jillian & Chatterton, Tim, 2022. "E-bikes and their capability to reduce car CO2 emissions," Transport Policy, Elsevier, vol. 116(C), pages 11-23.
    21. Luigi Cannari & Giovanni D�Alessio, 2018. "Wealth inequality in Italy: reconstruction of 1968-75 data and comparison with recent estimates," Questioni di Economia e Finanza (Occasional Papers) 428, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    Keywords

    Spatial microsimulation; Small Area Estimation;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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