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Population Initialisation for 2001 UK Census: Methods and Results

This seminar highlights the methods and results of population initialisation for the 2001 UK Census using the Individual and Household Level Estimates based on the 2001 UK Human Population Census Data. The focus is on the use of Genetic Algorithms and Iterative Proportional Sampling techniques to select a well-fitting set of records from the Individual and Household Sample of Anonymised Records (ISAR, HSAR) to constitute census areas. The fitness of the selected set is evaluated by comparing aggregate estimates with published Census Aggregate/Area Statistics (CAS). The seminar also discusses future work and the availability of results and methods for researchers and policy makers.

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Population Initialisation for 2001 UK Census: Methods and Results

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  1. Individual and Household Level Estimates Based on 2001 UK Human Population Census Data Andy Turner CSAP Seminar on Microsimulation: Problems and Solutions 2007-04-27

  2. Outline • MoSeS: A Brief Introduction to the Demographic Modelling work. • Focus on Population Initialisation for 2001 based on 2001 UK Population Census data. • Future Work

  3. MoSeS: A Brief Introduction to the Demographic Modelling work. • Population Initialisation for 2001 based on 2001 UK Population Census data. • Dynamic Model for 2001 to 2031. • Enrichment using health and social survey data. • Making the results and methods available for researchers and policy makers.

  4. Focus on Population Initialisation for 2001 based on 2001 UK Population Census data. • The basic task is to select a well fitting set of records from the Individual and Household Sample of Anonymised Records (ISAR, HSAR) to constitute census areas. • Mark Birkin has done some work to find a well fitting set using an Iterative Proportional Sampling (IPS) technique and I have done some work using Genetic Algorithms (GA). • The fitness of a set is evaluated by a fitness function which compares aggregate estimates from the ISAR and HSAR, with those from published Census Aggregate/Area Statistics (CAS).

  5. Current GA work • Select from the HSAR the Household Population (HP) and the ISAR the Communal Establishment Population (CEP) • In theory this is OK, • Although the HSAR is only available for England and Wales. • Control Constrain (CC) and Optimisation Constrain (OC) • CC are measures that *have to* be met in solutions. • OC are measures that are fitted to.

  6. Control Constraints • CC the CEP using CAS001 and CC the HP using CAS003. • CC the HP using CAS003 • Age of Household Reference Person (HRP) • CAS003 age is grouped as 0-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+. • CC on HP age is very hard! • Any Household can contain up to 12 individuals. • Consider the difficulty ensuring that the total number of individuals is correct. • Household records can be ordered by number of individuals. • To order by the exact age breakdown of the individuals is considerably more complex.

  7. GA • An initial set of solutions is generated. • Solutions are bred: • This is done by mutation involving swapping a random number of HSAR and ISAR ensuring CC are met: • CC are met by ensuring any record swapped in has has age in the relevant age group of CC. • Each solution is measured for a goodness of fit. • The best fitting solutions are kept. • Steps 1 to 5 are repeated until convergence or until a fixed number of iterations are completed.

  8. Some Results • http://www.geog.leeds.ac.uk/people/a.turner/projects/MoSeS/documentation/demography/results/2001PopulationInitialisation/ToyModel_SWR_OA_HSARHP_ISARCEP_0_10_500_10_100_12_10/Leeds/OptimisationConstraints/OA.xhtml2.0.html

  9. Future Work • Next Steps • Analyse errors graphically and investigate outliers. • Produce and analyse Geographical Maps of the errors. • Run Belinda's dynamic model based on these data. • Develop a publication comparing the IPS and GA method and results. • Target GeoComp2007 • Reproduce ISAR only results which can be used more readily by others. • Tidy and fully document the code. • The code is written in Java and is open source. • http://www.geog.leeds.ac.uk/people/a.turner/src/andyt/java/projects/MoSeS/ • Enrich the ISAR and HSAR records by incorporating variables from the BHPS and other Survey and Health Databases. • Mark has some Masters students looking into this.

  10. References • MoSeS source code • http://www.geog.leeds.ac.uk/people/a.turner/src/andyt/java/projects/MoSeS/ • MoSeS NCeSS Home Page • http://www.ncess.ac.uk/research/moses/ • Andy Turner’s MoSeS Web Page • http://www.geog.leeds.ac.uk/people/a.turner/projects/MoSeS/

  11. Acknowledgements • Thanks to NCeSS colleagues and the ESRC for supporting this work. • Thanks MoSeS project team for your collaboration. • In particular thanks to Mark Birkin, Martin Clarke and Phil Rees the demography experts. • Thanks to the CCG, the School of Geography and the University of Leeds for your help and support over the years.

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