IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v33y2017i4p1021-1050n9.html
   My bibliography  Save this article

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

Author

Listed:
  • Suesse Thomas

    (National Institute for Applied Statistics Research Australia, School of Mathametics and Applied Statistics, University of Wollongong, NSW 2522, Australia.)

  • Namazi-Rad Mohammad-Reza

    (National Institute for Applied Statistics Research Australia, School of Mathametics and Applied Statistics, University of Wollongong, NSW 2522, Australia.)

  • Mokhtarian Payam

    (Damian Group, Fairfax Media, Sydney2009 NSW, Australia)

  • Barthélemy Johan

    (SMART Infrastructure Facility, University of Wollongong, NSW 2522, Australia)

Abstract

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.

Suggested Citation

  • Suesse Thomas & Namazi-Rad Mohammad-Reza & Mokhtarian Payam & Barthélemy Johan, 2017. "Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1021-1050, December.
    • Handle: RePEc:vrs:offsta:v:33:y:2017:i:4:p:1021-1050:n:9
    DOI: 10.1515/jos-2017-0048
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jos-2017-0048
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jos-2017-0048?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nam Huynh & Johan Barthelemy & Pascal Perez, 2016. "A Heuristic Combinatorial Optimisation Approach to Synthesising a Population for Agent Based Modelling Purposes," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-11.
    2. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    3. Nicholas Geard & James M McCaw & Alan Dorin & Kevin B Korb & Jodie McVernon, 2013. "Synthetic Population Dynamics: A Model of Household Demography," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(1), pages 1-8.
    4. Li‐Chun Zhang & Raymond L. Chambers, 2004. "Small area estimates for cross‐classifications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 479-496, May.
    5. Alan Agresti & David B. Hitchcock, 2005. "Bayesian inference for categorical data analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(3), pages 297-330, December.
    6. Kirk Harland & Alison Heppenstall & Dianna Smith & Mark Birkin, 2012. "Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-1.
    7. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    8. Joseph B. Lang, 2005. "Homogeneous Linear Predictor Models for Contingency Tables," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 121-134, March.
    9. Johan Barthelemy & Philippe L. Toint, 2013. "Synthetic Population Generation Without a Sample," Transportation Science, INFORMS, vol. 47(2), pages 266-279, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Lu & Srinivasan, Sivaramakrishnan, 2016. "An empirical assessment of factors affecting the accuracy of target-year synthetic populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 247-264.
    2. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
    3. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    4. Ian Philips & Graham Clarke & David Watling, 2017. "A Fine Grained Hybrid Spatial Microsimulation Technique for Generating Detailed Synthetic Individuals from Multiple Data Sources: An Application To Walking And Cycling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 167-200.
    5. Jason Hawkins & Khandker Nurul Habib, 2023. "A multi-source data fusion framework for joint population, expenditure, and time use synthesis," Transportation, Springer, vol. 50(4), pages 1323-1346, August.
    6. Jian Liu & Xiaosu Ma & Yi Zhu & Jing Li & Zong He & Sheng Ye, 2021. "Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
    7. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
    8. Lovelace, Robin & Ballas, Dimitris & Watson, Matt, 2014. "A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels," Journal of Transport Geography, Elsevier, vol. 34(C), pages 282-296.
    9. Andrew Bwambale & Charisma F. Choudhury & Stephane Hess & Md. Shahadat Iqbal, 2021. "Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling," Transportation, Springer, vol. 48(5), pages 2287-2314, October.
    10. Farooq, Bilal & Bierlaire, Michel & Hurtubia, Ricardo & Flötteröd, Gunnar, 2013. "Simulation based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 243-263.
    11. Sun, Lijun & Erath, Alexander & Cai, Ming, 2018. "A hierarchical mixture modeling framework for population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 199-212.
    12. Nicholas Fournier & Eleni Christofa & Arun Prakash Akkinepally & Carlos Lima Azevedo, 2021. "Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method," Transportation, Springer, vol. 48(2), pages 1061-1087, April.
    13. Boakye, Jessica & Guidotti, Roberto & Gardoni, Paolo & Murphy, Colleen, 2022. "The role of transportation infrastructure on the impact of natural hazards on communities," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    14. H. Patricia McKenna, 2019. "Innovating Metrics for Smarter, Responsive Cities," Data, MDPI, vol. 4(1), pages 1-26, February.
    15. Trond Husby & Olga Ivanova & Mark Thissen, 2018. "Simulating the Joint Distribution of Individuals, Households and Dwellings in Small Areas," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 169-190.
    16. Claudio Nägeli & Liane Thuvander & Holger Wallbaum & Rebecca Cachia & Sebastian Stortecky & Ali Hainoun, 2022. "Methodologies for Synthetic Spatial Building Stock Modelling: Data-Availability-Adapted Approaches for the Spatial Analysis of Building Stock Energy Demand," Energies, MDPI, vol. 15(18), pages 1-18, September.
    17. Philips, Ian & Anable, Jillian & Chatterton, Tim, 2022. "E-bikes and their capability to reduce car CO2 emissions," Transport Policy, Elsevier, vol. 116(C), pages 11-23.
    18. Yu Han & Changjie Chen & Zhong-Ren Peng & Pallab Mozumder, 2022. "Evaluating impacts of coastal flooding on the transportation system using an activity-based travel demand model: a case study in Miami-Dade County, FL," Transportation, Springer, vol. 49(1), pages 163-184, February.
    19. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
    20. Zheng Wei & Daeyoung Kim & Erin M. Conlon, 2022. "A Bayesian approach to the analysis of asymmetric association for two-way contingency tables," Computational Statistics, Springer, vol. 37(3), pages 1311-1338, July.
    21. María Dolores Esteban & María José Lombardía & Esther López-Vizcaíno & Domingo Morales & Agustín Pérez, 2020. "Small area estimation of proportions under area-level compositional mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 793-818, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:offsta:v:33:y:2017:i:4:p:1021-1050:n:9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.