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An Iterative Approach for Generating Statistically Realistic Populations of Households

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  • Floriana Gargiulo
  • Sônia Ternes
  • Sylvie Huet
  • Guillaume Deffuant

Abstract

Background: Many different simulation frameworks, in different topics, need to treat realistic datasets to initialize and calibrate the system. A precise reproduction of initial states is extremely important to obtain reliable forecast from the model. Methodology/Principal Findings: This paper proposes an algorithm to create an artificial population where individuals are described by their age, and are gathered in households respecting a variety of statistical constraints (distribution of household types, sizes, age of household head, difference of age between partners and among parents and children). Such a population is often the initial state of microsimulation or (agent) individual-based models. To get a realistic distribution of households is often very important, because this distribution has an impact on the demographic evolution. Usual techniques from microsimulation approach cross different sources of aggregated data for generating individuals. In our case the number of combinations of different households (types, sizes, age of participants) makes it computationally difficult to use directly such methods. Hence we developed a specific algorithm to make the problem more easily tractable. Conclusions/Significance: We generate the populations of two pilot municipalities in Auvergne region (France) to illustrate the approach. The generated populations show a good agreement with the available statistical datasets (not used for the generation) and are obtained in a reasonable computational time.

Suggested Citation

  • Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
  • Handle: RePEc:plo:pone00:0008828
    DOI: 10.1371/journal.pone.0008828
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    References listed on IDEAS

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    1. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
    2. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
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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. Lenormand, Maxime & Huet, Sylvie & Gargiulo, Floriana, 2014. "Generating French virtual commuting networks at the municipality level," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(1), pages 43-55.
    4. Jason Hawkins & Khandker Nurul Habib, 2023. "A multi-source data fusion framework for joint population, expenditure, and time use synthesis," Transportation, Springer, vol. 50(4), pages 1323-1346, August.
    5. 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).
    6. 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.
    7. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
    8. Claudio Nägeli & Liane Thuvander & Holger Wallbaum & Rebecca Cachia & Sebastian Stortecky & Ali Hainoun, 2022. "Methodologies for Synthetic Spatial Building Stock Modelling: Data-Availability-Adapted Approaches for the Spatial Analysis of Building Stock Energy Demand," Energies, MDPI, vol. 15(18), pages 1-18, September.
    9. 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.
    10. Querini, Florent & Benetto, Enrico, 2014. "Agent-based modelling for assessing hybrid and electric cars deployment policies in Luxembourg and Lorraine," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 149-161.

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