Population Synthesis with Quasirandom Integer Sampling

Authored by Mark Birkin, Andrew P Smith, Robin Lovelace

Date Published: 2017

DOI: 10.18564/jasss.3550

Sponsors: No sponsors listed

Platforms: C++ R

Model Documentation: Other Narrative Pseudocode

Model Code URLs: https://github.com/CatchDat/humanleague

Abstract

Established methods for synthesising a population from geographically aggregated data are robust and well understood. However, most rely on the potentially detrimental process of integerisation if a whole-individual population is required, e.g. for use in agent-based modelling (ABM). This paper describes and investigates the use of quasirandom sequences to sample populations from known marginal constraints whilst preserving those marginal distributions. We call this technique Quasirandom Integer Without-replacement Sampling (QIWS) and show that the statistical properties of quasirandomly sampled populations to be superior to those of pseudorandomly sampled ones in that they tend to yield entropies much closer to populations generated using the entropy-maximising iterative proportional fitting (IPF) algorithm. The implementation is extremely efficient, easily outperforming common IPF implementations. It is freely available as an open source R package called humanleague. Finally, we suggest how the current limitations of the implementation can be overcome, providing a direction for future work.

Tags

Microsimulation Sampling Microsynthesis Quasirandom sequences Sequence generator