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The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records

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  • P Williamson

    (Department of Geography, University of Liverpool, Liverpool L69 3BX, England)

  • M Birkin
  • P H Rees

Abstract

Census data can be represented both as lists and as tabulations of household/individual attributes. List representation of Census data offers greater flexibility, as the exploration of interrelationships between population characteristics is limited only by the quality and scope of the data collected. Unfortunately, the released lists of household/individual attributes (Samples of Anonymised Records, SARs) are spatially referenced only to areas (single or merged districts) with populations of 120 000 or more, whereas released tabulations are available for units as small as single enumeration districts (Small Area Statistics, SAS). Intuitively, it should be possible to derive list-based estimates of enumeration district populations by combining information contained in the SAR and the SAS. In this paper we explore the range of solutions that could be adapted to this problem which, ultimately, is presented as a complex combinatorial optimisation problem. Various techniques of combinatorial optimisation are tested, and preliminary results from the best performing algorithm are evaluated. Through this process, the lack of suitable test statistics for the comparison of observed and expected tabulations of population data is highlighted.

Suggested Citation

  • P Williamson & M Birkin & P H Rees, 1998. "The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records," Environment and Planning A, , vol. 30(5), pages 785-816, May.
    • Handle: RePEc:sae:envira:v:30:y:1998:i:5:p:785-816
    DOI: 10.1068/a300785
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    References listed on IDEAS

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    1. S Openshaw, 1991. "A View on the GIS Crisis in Geography, or, Using GIS to Put Humpty-Dumpty Back Together Again," Environment and Planning A, , vol. 23(5), pages 621-628, May.
    2. P Rees, 1994. "Estimating and projecting the populations of urban communities," Environment and Planning A, , vol. 26(11), pages 1671-1697, November.
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    1. David Pritchard & Eric Miller, 2012. "Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously," Transportation, Springer, vol. 39(3), pages 685-704, May.
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    3. Ann Harding & Rachel Lloyd & Anthea Bill & Anthony King, 2004. "Assessing Poverty and Inequality at a Detailed Regional Level: New Advances in Spatial Microsimulation," WIDER Working Paper Series RP2004-26, World Institute for Development Economic Research (UNU-WIDER).
    4. O'Donoghue, Cathal & Grealis, Eoin & Farrell, Niall, 2015. "Modelling the Spatial Distributional Agricultural Incomes," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212654, European Association of Agricultural Economists.
    5. Hanley, Nicholas & Hynes, Stephen & O'Donoghue, Cathal, 2008. "A combinatorial optimisation approach to non-market environmental benefit aggregation," Stirling Economics Discussion Papers 2008-08, University of Stirling, Division of Economics.
    6. Malcolm Campbell & Dimitris Ballas, 2013. "A spatial microsimulation approach to economic policy analysis in Scotland," Regional Science Policy & Practice, Wiley Blackwell, vol. 5(3), pages 263-288, August.
    7. Ann Harding & Robert Tanton, 2014. "Policy and people at the small-area level: using micro-simulation to create synthetic spatial data," Chapters, in: Robert Stimson (ed.), Handbook of Research Methods and Applications in Spatially Integrated Social Science, chapter 25, pages 560-586, Edward Elgar Publishing.
    8. Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.
    9. Lopes, Mafalda Mendes & Moura, Filipe & Martinez, Luis M., 2014. "A rule-based approach for determining the plausible universe of electric vehicle buyers in the Lisbon Metropolitan Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 22-36.
    10. M. Esteban Muñoz H. & Irene Peters, 2014. "Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 127-157.
    11. O’Donoghue, Cathal & McKinstry, Alistair & Green, Stuart & Fealy, Reamonn & Heanue, Kevin & Ryan, Mary & Connolly, Kevin & Desplat, J.C. & Horan, Brendan, 2016. "A Blueprint for a Big Data Analytical Solution to Low Farmer Engagement with Financial Management," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(A), pages 1-24, June.
    12. Loughrey, Jason & O’Donoghue, Cathal & Meredith, David & Murphy, Ger & Shanahan, Ultan & Miller, Corina, 2018. "The Local Impact of Cattle Farming," 166th Seminar, August 30-31, 2018, Galway, West of Ireland 276231, European Association of Agricultural Economists.
    13. 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.
    14. D. H. Judson, 2007. "Information integration for constructing social statistics: history, theory and ideas towards a research programme," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 483-501, March.
    15. Itismita Mohanty & Robert Tanton & Yogi Vidyattama & Marcia Keegan & Robert Cummins, 2013. "‘Small area estimates of Subjective Wellbeing: Spatial Microsimulation on the Australian Unity Wellbeing Index Survey’," NATSEM Working Paper Series 13/23, University of Canberra, National Centre for Social and Economic Modelling.
    16. 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.
    17. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    18. 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.
    19. Kate A Timmins & Kimberley L Edwards, 2016. "Validation of Spatial Microsimulation Models: a Proposal to Adopt the Bland-Altman Method," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 106-122.
    20. Stephen Hynes & Nick Hanley & Cathal O’Donoghue, 2010. "A Combinatorial Optimization Approach to Nonmarket Environmental Benefit Aggregation via Simulated Populations," Land Economics, University of Wisconsin Press, vol. 86(2), pages 345-362.
    21. O'Donoghue, Cathal & Grealis, Eoin & Loughrey, Jason & Donnellan, Trevor & Hanrahan, Kevin & Hennessy, Thia, 2015. "The Spatial Distributional Effect of Common Agricultural Policy Reform," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212656, European Association of Agricultural Economists.
    22. 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.
    23. 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.

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