Generating a Two-Layered Synthetic Population for French Municipalities: Results and Evaluation of Four Synthetic Reconstruction Methods
Boyam Fabrice Yameogo (),
Pierre-Olivier Vandanjon (),
Pascal Gastineau () and
Pierre Hankach ()
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Boyam Fabrice Yameogo: https://www.ifsttar.fr/en/welcome/
Pierre-Olivier Vandanjon: https://www.ifsttar.fr/menu-haut/annuaire/fiche-personnelle/personne/vandanjon-pierre-olivier/
Journal of Artificial Societies and Social Simulation, 2021, vol. 24, issue 2, 5
This article describes the generation of a detailed two-layered synthetic population of households and individuals for French municipalities. Using French census data, four synthetic reconstruction methods associated with two probabilistic integerization methods are applied. The paper offers an in-depth description of each method through a common framework. A comparison of these methods is then carried out on the basis of various criteria. Results showed that the tested algorithms produce realistic synthetic populations with the most efficient synthetic reconstruction methods assessed being the Hierarchical Iterative Proportional Fitting and the relative entropy minimization algorithms. Combined with the Truncation Replication Sampling allocation method for performing integerization, these algorithms generate household-level and individual-level data whose values lie closest to those of the actual population.
Keywords: Synthetic Population Generation; Multi-Level; Microsimulation; Simultaneous Control (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2020-65-2
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