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  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 SatisfactoryUnsatisfactory 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

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

 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