SPATIAL MICROSIMULATION:
A METHOD FOR SMALL AREA
LEVEL ESTIMATION
Dr Karyn Morrissey
Department of Geography and Planning
University of Liverpool
Research Methods Festival, 2014
Rationale for Microdata
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Much modelling in the social sciences takes an aggregate or mesolevel approach.
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However, all government policy and investment has a spatial
impact, regardless of the initial motivating factor.
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As such, policy level analyses call for individual or household level
analysis at a disaggregated/local spatial scale.
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Particularly Health Policy
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Health is a produce of individual and social factors that vary geographically
Why Simulate?
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Data Issues
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Census data: Available at the small area level does not
offer any information on household income
Survey data often contains detailed micro data, for
example income, pensions and health data that is not
included in the census - aspatial in nature
Spatial Microsimulation offers a means of
synthetically creating large-scale micro-datasets at
different geographical scales.
Aspatial Microdata
Census Outputs at the small area level
Matching Process
Combinational Optimisation Methods, Reweighting, IPF
Open source algorithm
for each of these are
increasingly available
Synthetic Population Data
Validation of unmatched variables
Satisfactory
Unsatisfactory
Calibration through alignment
Objective:
Sum of MSIM Outputs are equal exogenous data target
Estimate variable of interest using regression
Create Alignment Co-efficient
E.g.: SMILE’s Market Income Variables are each
adjusted by multiplying the appropriate estimated
individual earnings by the alignment coefficient
E.g.: Fully calibrated micro-level earnings for Ireland
SMILE
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SMILE is a Spatial Microsimulation Model
My lovechild and sometimes referred to as SLIME
depending on how it is behaving
Using a statistical matching algorithm, simulated
annealing, SMILE merges data from the SAPS and the
Living in Ireland survey (income & health data)
SMILE creates a geo-referenced, attribute rich dataset
containing:
The socio-economic, income distribution & health profile of
individuals at the small area level
Model Components & Analysis to Date
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Components:
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Agricultural/Farm Level Model;
 Family Farm Income Analysis (Hynes et al., 2009)
Environmental Model;
 Conservation & Agri-Environmental Analysis (Hynes et al., 2009)
Recreation Model;
 Walkers Preferences (Cullinan et al, 2008)
Health Model;
 Access to GP Services (Morrissey et al., 2008) & the Spatial Distribution of Depression
(Morrissey et al., 2010), Determinants of LTI (Morrissey et al., 2013)
Income Model
 Labour Force Participation & it’s impact on Income (Morrissey and O’Donoghue, 2011)
Marine Sector analysis
 Impact of the marine sector on incomes at the small area level (Morrissey et al., 2014);
Impact of marine energy on the small area level in Ireland (Farrell et al., forthcoming)
RGS-IBG Edinburgh, 3-5th of July, 2012
Health Application
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The spatial distribution of
demand for acute hospital
services (AHS) (Morrissey et al.,
2009)
It was found that demand for
AHS was highest in the West &
NW of Ireland
Why?
National Level Logit found that
main-drivers of AHU are:
 Medical Card Possession
 Age
 LTI
Is there a Spatial Pattern to
theses Drivers which explains
AHU at the ED Level?
Drivers of AHU at the ED Level
Exogenous Models
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Spatial Microsimulation models may be linked with other
exogenous models
 Models may be either spatial or aspatial
 Linking to these models to a spatial microsimulation
models allows their macro level results to be spatially
disaggregated
Supplementary Models
 Tax-Benefit Model
 Spatial Interaction Model
Income Analysis Application
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Incorporating a TBS into SMILE –
Average Disposable Income was
generated
East of the country - higher levels
disposable income
4 urban centres - higher than average
disposable income
CSO - provides county level estimates of
disposable income
Real value added by SMILE’s Examine
the distribution of income within
counties
• Disposable income - low along the
coastal regions of the West
• Counties with urban centres,
income higher in the in these
counties than in the rural areas
Accessibility Analysis: Health Service Application
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RHS: Access to a GP facility
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Spatial Interaction Model
LHS: Probability of Using a
GP service given one’s SocioEconomic Profile
Logistic Model
A Spatial Microsimulation Model of Comorbidity
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New UK work
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ESRC SDAI Funded
Develop a spatial microsimulation model for
comorbidity
Whilst small area register data on single morbidities
exist and may be accessible to researchers
These only report 1 morbidity
Comorbidity is an increasingly important health issue
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With both demand and supply side implication
Comorbidity at the small area level
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Develop a model of comorbidity between CVD,
diabetes & obesity at a small
area level for England
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East Kent Hospital Trust our
case partner
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The ESRC Secondary Data
Analysis Initiative for funding
this research.
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Post-Doc: Dr Ferran Espuny
Conclusion
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Spatial microsimulation – computationally and data intense
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However, there are now open source software for microsimulation that
offer the shelf models – all you need is to prepare the data
 Harland et al., (2012)
 Comorbidity model presented will be open source
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Always necessary to look at the spatial implication of policy and
investment
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Spatial microsimulation model offers one way to do this
Validation (and calibration) is key if the data is to be used to
inform policy