Spatial microsimulation: A method for small area level estimation

1. Spatial microsimulation: A method for small area level estimation Research Methods Festival, 2014 Dr Karyn Morrissey Department of Geography and Planning University of Liverpool

2. 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

3. 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.

4. 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 Estimate variable of interest using regression Satisfactory Unsatisfactory Create Alignment Co-efficient • Calibration through alignment • Objective: • Sum of MSIM Outputs are equal exogenous data target 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

5. 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 SAPSand 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

6. 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

7. 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

8. Drivers of AHU at the ED Level

9. 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

10. Income Analysis Application • 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’sExamine 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

11. 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

12. 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

13. Comorbidity at the small area level Develop a model of co-morbidity between CVD, diabetes & obesity at a small area level forEngland East Kent Hospital Trust our case partner The ESRC Secondary Data Analysis Initiative for funding this research. Post-Doc: Dr Ferran Espuny

14. 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