Small area population projection: inputs to behavioural and demand models

Small area population projection: inputs to behavioural and demand models
UGI-IGU Centennial Congress, Paris, 18-22 June 2022.

Nik Lomax, The Alan Turing Institute, United Kingdom

Individual level population and household data at a fine spatial scale have utility in a wide range of applied contexts: they can be used as an input to spatial demand models, agent based models, disease models, and models of social systems or ‘digital twins’. Further incorporating a time element to produce projections adds another layer of utility, allowing for the modelling of counterfactual trade-offs for different future population size, composition and spatial distribution. However, data at this level of disaggregation are not routinely available and require a bespoke modelling solution.

This paper first describes an open-source modelling framework for producing these spatially detailed, individual level demographic projections. It then goes on to demonstrate, via a collection of case studies, the utility of these data as they are fed into other applied models of different systems. A land use model is used to examine the spatial distribution of population under different urban growth scenarios. A disease spread model is used to examine the impact of interventions on daily activity for COVID-19 transition. Finally the individual level data are used in an Agent Based model which is used to assess different transport development scenarios.

Users are able to download and adapt the demographic model for their own purposes and this talk outlines the open-source principles upon which the model is built. To date the model has delivered utility in the applied case studies outlined but it is also continuing to evolve. The talk will outline the current state of the model, supported by funding from the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.

Keywords: demographic | projection | small area | individual | applied

Transcript

1. Small area population projection:
   inputs to behavioural and
   demand models
   Nik Lomax
   School of Geography | University of Leeds | Alan Turing Institute
   n.m.lomax@leeds.ac.uk
   @NikLomax
   IGU 2022 | Paris | 19 July 2022
   

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2. The problem
   • High resolution population projection data are needed for
   infrastructure, resource allocation and policy decisions
   • Their utility is as an input dataset
   • These are not available from official sources so need to be estimated
   • Modelling teams often do not have the resources available to do this
   

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3. The framework
   • Individual level model
   • People at MSOA
   • Households at OA
   • Assignment algorithm
   • Utility is scenario exploration
   • Generates inputs for other models
   • doi: 10.1111/gean.12320
   

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4. The framework of open source models
   

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8. Use in other models
   

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9. Use in other models: Two examples
   

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10. Scenario Generation
    

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11. Hickford, A., T. Russel, J. Hall, and R. Nicholls. (2020). A Sustainable Oxford-Cambridge Corridor? Spatial Analysis of Options
    and Futures for the Arc. https://www.itrc.org.uk/wp-content/uploads/2020/01/arc-main-report.pdf.
    

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13. Explain SIM
    • DAFNI Video here
    

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14. Population change under four scenarios
    Lomax, N., Smith, A.P., Archer, L., Ford, A. and Virgo, J., 2022. An
    Open-Source Model for Projecting Small Area Demographic and Land-Use
    Change. Geographical Analysis. https://doi.org/10.1111/gean.12320.
    

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15. Population change under four scenarios
    Lomax, N., Smith, A.P., Archer, L., Ford, A. and Virgo, J., 2022. An
    Open-Source Model for Projecting Small Area Demographic and Land-Use
    Change. Geographical Analysis. https://doi.org/10.1111/gean.12320.
    Low density High density
    

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16. Spooner, F., Abrams, J.F., Morrissey, K., et al., 2021. A dynamic microsimulation model for epidemics. Social Science & Medicine, 291,
    p.114461. https://doi.org/10.1016/j.socscimed.2021.114461
    

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17. Disease spread
    

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18. Disease spread
    earlier lockdown estimated to result in a
    lower peak in COVID-19 cases and 47% fewer
    infections overall during the initial COVID-19
    outbreak.
    

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19. https://www.improbable.io/blog/improbable-synthetic-
    environment-technology-accelerates-uk-pandemic-modelling
    

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20. Ongoing work: A probabilistic framework
    https://github.com/alan-turing-institute/daedalus
    

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21. Small area population projection:
    inputs to behavioural and
    demand models
    Nik Lomax
    School of Geography | University of Leeds | Alan Turing Institute
    n.m.lomax@leeds.ac.uk
    @NikLomax
    IGU 2022 | Paris | 19 July 2022
    

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22. Applied Geography Commission meeting
    • Thursday 21 July
    • 12:30 – 14:00
    • Pantheon room 57
    • To join the mailing list: n.m.lomax@leeds.ac.uk
    

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