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Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques

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Abstract

There are several established methodologies for generating synthetic populations. These include deterministic reweighting, conditional probability (Monte Carlo simulation) and simulated annealing. However, each of these approaches is limited by, for example, the level of geography to which it can be applied, or number of characteristics of the real population that can be replicated. The research examines and critiques the performance of each of these methods over varying spatial scales. Results show that the most consistent and accurate populations generated over all the spatial scales are produced from the simulated annealing algorithm. The relative merits and limitations of each method are evaluated in the discussion.

Suggested Citation

  • Kirk Harland & Alison Heppenstall & Dianna Smith & Mark Birkin, 2012. "Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-1.
    • Handle: RePEc:jas:jasssj:2010-61-3
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    Cited by:

    1. Mohamed Khachman & Catherine Morency & Francesco Ciari, 2024. "Integrated multiresolution framework for spatialized population synthesis," Transportation, Springer, vol. 51(3), pages 823-852, June.
    2. Colaço, Rui & de Abreu e Silva, João, 2022. "Exploring the e-shopping geography of Lisbon: Assessing online shopping adoption for retail purchases and food deliveries using a 7-day shopping survey," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    3. Suesse Thomas & Namazi-Rad Mohammad-Reza & Mokhtarian Payam & Barthélemy Johan, 2017. "Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1021-1050, December.
    4. Kremmydas, Dimitris & Athanasiadis, Ioannis N. & Rozakis, Stelios, 2018. "A review of Agent Based Modeling for agricultural policy evaluation," Agricultural Systems, Elsevier, vol. 164(C), pages 95-106.
    5. M. Esteban Muñoz H. & Irene Peters, 2014. "Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 127-157.
    6. 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.
    7. 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.
    8. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    9. 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.
    10. 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.
    11. 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.
    12. H. Patricia McKenna, 2019. "Innovating Metrics for Smarter, Responsive Cities," Data, MDPI, vol. 4(1), pages 1-26, February.
    13. Mohammad-Reza Namazi-Rad & Payam Mokhtarian & Pascal Perez, 2014. "Generating a Dynamic Synthetic Population – Using an Age-Structured Two-Sex Model for Household Dynamics," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-16, April.
    14. Dianna M. Smith & Alison Heppenstall & Monique Campbell, 2021. "Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health," J, MDPI, vol. 4(2), pages 1-11, June.
    15. Ma, Lu & Srinivasan, Sivaramakrishnan, 2016. "An empirical assessment of factors affecting the accuracy of target-year synthetic populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 247-264.

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