SPATIAL MICROSIMULATION: A METHOD FOR SMALL AREA LEVEL ESTIMATIONDr Karyn MorrisseyDepartment of Geography and PlanningUniversity of Liverpool

Research Methods Festival, 2014

Rationale for Microdata

Much modelling in the social sciences takes an aggregate or meso-level approach.

However, all government policy and investment has a spatial impact, regardless of the initial motivating factor.

As such, policy level analyses call for individual or household level analysis at a disaggregated/local spatial scale. Particularly Health Policy Health is a produce of individual and social factors that vary geographically

Why Simulate?

Data Issues 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

Validation of unmatched variables

Calibration through alignment

Objective: Sum of MSIM Outputs are equal exogenous data target

Estimate variable of interest using regression

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

Synthetic Population Data

Satisfactory Unsatisfactory

Create Alignment Co-efficient

Open source algorithm for each

of these are increasingly

available

SMILE

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

RGS-IBG Edinburgh, 3-5th of July, 2012

Model Components & Analysis to Date

Components: 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)

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?

Health Application

Drivers of AHU at the ED Level

Exogenous Models

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

• 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

Income Analysis Application

Accessibility Analysis: Health Service Application

RHS: Access to a GP facility Spatial Interaction Model

LHS: Probability of Using a GP service given one’s Socio-Economic Profile

Logistic Model

A Spatial Microsimulation Model of Comorbidity

New UK work 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

With both demand and supply side implication

Comorbidity at the small area level

Develop a model of co-morbidity between CVD, diabetes & obesity at a small area level for England

East Kent Hospital Trust our case partner

The ESRC Secondary Data Analysis Initiative for funding this research.

Post-Doc: Dr Ferran Espuny

Conclusion Spatial microsimulation – computationally and data intense 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

Always necessary to look at the spatial implication of policy and investment Spatial microsimulation model offers one way to do this

Validation (and calibration) is key if the data is to be used to inform policy