Smith, AP, Lovelace, R orcid.org/0000-0001-5679-6536 and Birkin, M (2017) Population Synthesis with Quasirandom Integer Sampling. Journal of Artificial Societies and Social Simulation, 40 (4). 14. ISSN 1460-7425
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Population Synthesis, Microsimulation, Quasirandom Numbers, Statistical Sampling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number ESRC ES/L011891/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Nov 2017 14:36 |
Last Modified: | 01 Nov 2017 14:36 |
Status: | Published |
Publisher: | SimSoc Consortium |
Identification Number: | 10.18564/jasss.3550 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123328 |