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Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices

Author

Listed:
  • Cathal O'Donoghue

    (Teagasc Rural Economy and Development Programme,)

  • Karyn Morrissey

    (University of Liverpool)

  • John Lennon

    (Teagasc Rural Economy and Development Programme)

Abstract

Spatial microsimulation modelling has developed for over a half century and is now a mainstream analytical tool within the microsimulation community accounting for a very significant proportion of papers at conferences and within the journal. There have been a number of recent surveys of mainstream spatial microsimulation models and associated methodologies. The contribution of this paper relates mainly in extending these surveys by considering other micro based simulation models that incorporate a spatial or geographic dimension. We feel this is important as in many areas of microsimulation modelling, there are parallel literatures that have developed that apply simulation techniques to micro units that are not labelled microsimulation or in the case of the papers reviewed in this paper not labelled spatial microsimulation. The paper reviews a number of different application areas of spatially focused microsimulation models, including demography, welfare, health, regional development, transport planning, agri-environmental analysis, crisis planning, land use and planning. We also review a number of the methodological choices made by modellers including scope, and spatial disaggregation, data sources, data creation methodology, validation and calibration and simulating change

Suggested Citation

  • 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.
  • Handle: RePEc:ijm:journl:v:7:y:2014:i:1:p:26-75
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    References listed on IDEAS

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    2. Wallentin, Gudrun, 2017. "Spatial simulation: A spatial perspective on individual-based ecology—a review," Ecological Modelling, Elsevier, vol. 350(C), pages 30-41.
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    4. Ricardo Crespo & Ignacio Hernandez, 2020. "On the spatially explicit Gini coefficient: the case study of Chile—a high-income developing country," Letters in Spatial and Resource Sciences, Springer, vol. 13(1), pages 37-47, April.
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    6. Huang, Charlotte & Elsland, Rainer, 2019. "A survey-based approach to estimate residential electricity consumption at municipal level in Germany," Working Papers "Sustainability and Innovation" S10/2019, Fraunhofer Institute for Systems and Innovation Research (ISI).
    7. Martin Johnsen & Oliver Brandt & Sergio Garrido & Francisco C. Pereira, 2020. "Population synthesis for urban resident modeling using deep generative models," Papers 2011.06851, arXiv.org.
    8. 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.
    9. Kate A Timmins & Kimberley L Edwards, 2016. "Validation of Spatial Microsimulation Models: a Proposal to Adopt the Bland-Altman Method," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 106-122.
    10. 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.
    11. Loughrey, Jason & O’Donoghue, Cathal & Meredith, David & Murphy, Ger & Shanahan, Ultan & Miller, Corina, 2018. "The Local Impact of Cattle Farming," 166th Seminar, August 30-31, 2018, Galway, West of Ireland 276231, European Association of Agricultural Economists.

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    More about this item

    Keywords

    Spatial Microsimulation; Survey; Methodology;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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