Creating Synthetic Household Populations

Arentze, TA Theo; Timmermans, Hjp Harry; Hofman, F Frank

doi:10.3141/2014-11

Cited by 71 publications

(50 citation statements)

References 6 publications

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“…In the literature, various methods have been suggested to avoid sampling zero issues, including Guo and Bhat (2007), Auld et al (2009), and others. Recent literature in IPF based tting (Arentze et al, 2007;Ye et al, 2009) has also concentrated on simultaneous tting of CTs for dierent types of agents (for instance, household and persons together). Schafer (1997) proposed a Bayesian procedure for computing the tted CT.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation based population synthesis

Farooq

Bierlaire

Hurtubia

et al. 2013

Transportation Research Part B: Methodological

150

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1Microsimulation of urban systems evolution requires synthetic population as a key input. Currently, the focus is on treating synthesis as a tting problem and thus various techniques have been developed, including Iterative Proportional Fitting (IPF) and Combinatorial Optimization based techniques. The key shortcomings of these procedures include: a) tting of one contingency table, while there may be other solutions matching the available data b) due to cloning rather than true synthesis of the population, losing the heterogeneity that may not have been captured in the microdata c) over reliance on the accuracy of the data to determine the cloning weights d) poor scalability with respect to the increase in number of attributes of the synthesized agents. In order to overcome these shortcomings, we propose a Markov Chain Monte Carlo (MCMC) simulation based approach. Partial views of the joint distribution of agent's attributes that are available from various data sources can be used to simulate draws from the original distribution. The real population from Swiss census is used to compare the performance of simulation based synthesis with the standard IPF. The standard root mean square error statistics indicated that even the worst case simulation based synthesis (SRMSE = 0.35) outperformed the best case IPF synthesis (SRMSE=0.64). We also used this methodology to generate the synthetic population for Brussels, Belgium where the data

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Section: Literature Reviewmentioning

confidence: 99%

Simulation based population synthesis

Farooq

Bierlaire

Hurtubia

et al. 2013

Transportation Research Part B: Methodological

150

View full text Add to dashboard Cite

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“…Raking (also known as raking ratio estimation) is a procedure which applies a proportional adjustment to the sample weights in a survey so that the adjusted weights add up the known population total when only the marginal population totals are known (Deville et al 1991;Lu and Gelman 2003). Although raking is not a maximum likelihood (ML) method under random sampling, the raking estimates are consistent and best asymptotically normal (Arentze et al 2007). Ireland and Kullback (1968) showed that the estimator produced by the IPFP method minimizes the discrimination information criterion (also known as the KullbackLeibler divergence, or relative entropy).…”

Section: Iterative Proportional Fitting Procedures (Ipfp)mentioning

confidence: 99%

“…The generation of an artificial (or synthetic) population that realistically matches the population of interest from such limited tables, or more generally aggregated data, has become an important research area (Arentze et al 2007;Gargiulo et al 2010;Harland et al 2012;Barthélemy and Toint 2013;Lenormand and Deffuant 2013;Geard et al 2013;Huynh et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts

Suesse

Namazi-Rad

Mokhtarian³

et al. 2017

Journal of Official Statistics

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Estimating population counts for multidimensional tables based on a representative sample subject to known marginal population counts is not only important in survey sampling but is also an integral part of standard methods for simulating area-specific synthetic populations. In this article several estimation methods are reviewed, with particular focus on the iterative proportional fitting procedure and the maximum likelihood method. The performance of these methods is investigated in a simulation study for multidimensional tables, as previous studies are limited to 2 by 2 tables. The data are generated under random sampling but also under misspecification models, for which sample and target populations differ systematically. The empirical results show that simple adjustments can lead to more efficient estimators, but generally, at the expense of increased bias. The adjustments also generally improve coverage of the confidence intervals. The methods discussed in this article along with standard error estimators, are made freely available in the R package mipfp. As an illustration, the methods are applied to the 2011 Australian census data available for the Illawarra Region in order to obtain estimates for the desired three-way table for age by sex by family type with known marginal tables for age by sex and for family type.

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“…We will discuss these issues in the following sections. Arentze et al, (2007) proposed a relation matrix as a new solution to multi-level fitting in synthetic population research. The relation matrix is constructed by converting individual marginal distributions to household distributions by assigning individuals to household positions, e.g.…”

Section: Ipfmentioning

confidence: 99%