spatial microsimulation: a method for small area level estimation
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Spatial microsimulation: A method for small area level estimation. Research Methods Festival, 2014. Dr Karyn Morrissey Department of Geography and Planning University of Liverpool. Rationale for Microdata. Much modelling in the social sciences takes an aggregate or meso -level approach. - PowerPoint PPT PresentationTRANSCRIPT
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