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Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples

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Abstract

Synthetic population is a fundamental input to dynamic micro-simulation in social applications. Based on the review of current major approaches, this paper presents a new sample-free synthesis method by inferring joint distribution of the total target population. Convergence of multivariate Iterative Proportional Fitting used in our method is also proved theoretically. The method, together with other existing ones, is applied to generate a nationwide synthetic population database of China by using its overall cross-classification tables as well as a sample from census. Marginal and partial joint distribution consistencies of each database are compared and evaluated quantitatively. Final results manifest sample-based methods have better performances on marginal indicators while the sample-free ones match partial distributions more precisely. Among the five methods, our proposed method significantly reduces the computational cost for generating synthetic population in large scale. An open source implementation of the population synthesizer based on C# used in this research is available at https://github.com/PeijunYe/PopulationSynthesis.git.

Suggested Citation

  • Peijun Ye & Xiaolin Hu & Yong Yuan & Fei-Yue Wang, 2017. "Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-16.
    • Handle: RePEc:jas:jasssj:2016-116-3
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    Cited by:

    1. Rachid Belaroussi & Younes Delhoum, 2024. "Forecasting Daily Activity Plans of a Synthetic Population in an Upcoming District," Forecasting, MDPI, vol. 6(2), pages 1-26, May.
    2. 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.

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