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An introduction to spatial microsimulation with R

May 08, 2014

An introduction to spatial microsimulation with R

These are the slides to accompany a two day course with the same name. See https://github.com/Robinlovelace/smsim-course for more information.


May 08, 2014

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  1. An introduction to spatial
    microsimulation with R
    A Leeds Short Course funded by the CDRC
    Robin Lovelace, University of Leeds
    See http://robinlovelace.net/smsim-course/
    28th May 2015

    View Slide

  2. About this course

    Aims to introduce both theory and practice of
    spatial microsimulation

    Divided into 3 main parts:
    – Foundations
    – Creating spatial microdata with R
    – Applying the method


    View Slide

  3. The agenda... and the handout

    9:30 – 11:00 Introduction
    – What is it? What it does.

    11:15 – 13:00 Smsim in R
    – Loading the data, IPF in R

    13:30 – 16:30 An example
    – Preparing input data

    9:30 – 11:00 Analysis
    – Validation

    11:15 – 13:30 Extensions
    – Integerisation
    1) Foundations
    2) IPF in R
    3) CakeMap /

    View Slide

  4. Why do you need spatial
    microsimulation? I

    To tackle the modifiable areal unit problem
    (MAUP) (Openshaw 1983)
    The world is like this: complex
    Administrative zones
    (“areal units”) look like this
    - a little arbitrary!
    Making it
    look (a bit)
    like this:

    View Slide

  5. Why do you need spatial
    microsimulation? II
    As an input into
    agent based
    models (image is
    in the context of
    modelling –
    reweighting =
    To create a
    population, also
    known as synthetic
    spatial microdata

    View Slide

  6. What is spatial
    microsimulation? I

    A method for
    synthesising spatial
    microdata based on
    survey and areal input
    data (Lovelace and
    Ballas 2013)

    View Slide

  7. What is spatial microsimulation? II

    A procedure to translate
    from 'wide' to 'long' data

    Restrictive data anonymity

    View Slide

  8. Applications – some examples

    To estimate local smoking rates (Tomintz et al.

    To investigate commuter patterns and model
    distributional impact of future scenarios
    (Lovelace et al. 2014)

    Farmer participation in agri-environment
    schemes (Hynes et al. 2008)

    View Slide

  9. Microsimulation and ABMs

    View Slide

  10. Validation

    It's tricky – you'll generally only simulate things
    you do not know

    But very important: possible to mislead with this

    Internal validation: compare with prior
    expectations – is the model working OK?

    External validation: compare with the real world

    View Slide

  11. References

    Edwards, K. L., Clarke, G. P., Thomas, J., & Forman, D. (2011). Internal and external
    validation of spatial microsimulation models: Small area estimates of adult obesity.
    Applied Spatial Analysis and Policy, 4(4), 281-300. (The importance of validation).

    Hynes, S., Farrelly, N., Murphy, E., & O'Donoghue, C. (2008). Modelling habitat
    conservation and participation in agri-environmental schemes: a spatial microsimulation
    approach. Ecological economics, 66(2), 258-269. (Agricultural application).

    Lovelace, R., Ballas, D., & Watson, M. (2013). A spatial microsimulation approach for the
    analysis of commuter patterns: from individual to regional levels. Journal of Transport
    Geography. (Policy-relevant application).

    Lovelace, Robin, and Dimitris Ballas. ‘Truncate, replicate, sample’: A method for creating
    integer weights for spatial microsimulation. Computers, Environment and Urban Systems
    41 (2013): 1-11. (Method).

    Openshaw, S. (1983). The modifiable areal unit problem (Vol. 38). Norwich: Geo Books.

    Tomintz, M. N., Clarke, G. P., & Rigby, J. E. (2008). The geography of smoking in Leeds:
    estimating individual smoking rates and the implications for the location of stop smoking
    services. Area, 40(3), 341-353. (Health application).

    View Slide