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Generating a dynamic synthetic population--using an age-structured two-sex model for household dynamics
M Namazi-Rad, Mohammad-Reza Namazi-Rad1, P Mokhtarian
1SMART Infrastructure Facility, University of Wollongong, New South Wales, Australia; National Institute for Applied Statistics Research Australia, University of Wollongong, New South Wales, Australia.
Plos One
|April 16, 2014
Summary
This study presents a new method for creating realistic synthetic populations using combinatorial optimization and micro-simulation. The generated synthetic population accurately reflects Australian demographics and their changes over time.
Area of Science:
- Computational Social Science
- Demographic Modeling
- Geospatial Analysis
Background:
- Accurate synthetic populations are crucial for agent-based modeling and decision-making.
- Existing methods for synthetic population generation have limitations in capturing complex demographic structures.
Purpose of the Study:
- To develop and evaluate a reliable method for generating synthetic populations using synthetic reconstruction (SR) and combinatorial optimization (CO).
- To project population dynamics over five years and validate against census data.
- To provide prediction intervals for population estimates using bootstrapping.
Main Methods:
- A combinatorial optimization (CO) algorithm with a quadratic function was employed.
- Baseline population generated from Confidentialised Unit Record Files (CURFs) and 2006 Australian census data.
- A dynamic micro-simulation model projected demographic transitions for individuals and households.
- Bootstrapping method used for prediction intervals.
Main Results:
- The study successfully generated a synthetic population for an Australian region.
- The projected population dynamics were compared against the 2011 Australian census.
- The method demonstrated the ability to model individual- and household-level demographic changes.
Conclusions:
- The presented CO and micro-simulation approach provides a reliable method for synthetic population generation.
- This technique is valuable for understanding human activity patterns and supporting decision-making in agent-based systems.
- The inclusion of prediction intervals enhances the robustness of population estimates.
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