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A synthetic population for agent-based modelling in Canada.

Manon Prédhumeau1, Ed Manley2

  • 1University of Leeds, School of Geography, Leeds, LS2 9JT, UK. m.predhumeau@leeds.ac.uk.

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|March 21, 2023
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Summary

An open-source synthetic population dataset for Canada is now available, offering detailed socio-demographic attributes for individuals and households. This reliable tool aids in simulating local public policy impacts and exploring future population scenarios.

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Area of Science:

  • Demography
  • Computational Social Science
  • Geographic Information Systems

Background:

  • Synthetic population datasets are crucial for simulating local public policy impacts.
  • Existing datasets are unavailable for Canada, creating a research gap.
  • Open-source tools enhance accessibility and reproducibility in population modeling.

Purpose of the Study:

  • To develop and validate an open-source synthetic population dataset for Canada.
  • To provide fine-grained geographical data for individuals and households.
  • To support 'what if' scenario analysis for present and future populations.

Main Methods:

  • Utilized 2016 Canadian census data and population projections.
  • Generated synthetic individuals with socio-demographic attributes (age, sex, income, education, employment, location).
  • Related synthetic individuals into households and validated against 2021 census data.

Main Results:

  • A reliable open-source synthetic population dataset for Canada (2021, 2023, 2030) was created.
  • Validation confirmed the dataset's accuracy across various geographical areas.
  • The dataset enables users to extract specific zones and extend it with local data.

Conclusions:

  • The developed synthetic population dataset serves as a valuable test bed for local public policy.
  • It empowers researchers and policymakers to explore population dynamics and impacts.
  • The open-source nature promotes wider adoption and further development for future population projections up to 2042.