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Introduction to Spatial Microsimulation Dr Kirk Harland
Introduction to Spatial Microsimulation
Dr Kirk Harland
What is a Spatial Microsimulation?Static Spatial Microsimulation• Deterministic Reweighting• Conditional Probabilities• Simulated AnnealingDynamic Microsimulation
This Session
What is Spatial Microsimulation
There are two types of Spatial Microsimulation1. Static spatial microsimulation - creates a micro-level
population from aggregate data2. Dynamic spatial microsimulation – moves a population
through space and time
Static Spatial Microsimulation
• Static spatial microsimulation synthesises individual level populations from aggregate information
• Does not move the population through space or time• Alternative approach to joining two datasets spatially
where no join is apparent, many health examples includingobesity (Smith et al., 2009)
diabetes (Smith et al., 2005)
smoking prevalence (Tomintz and Clarke, 2008)
Static Spatial Microsimulation
• Several different static microsimulation methods1. Deterministic reweighting – large iterative proportional
fitting algorithm2. Conditional probabilities – calculates the probability of a
person appearing in a zone give there characteristics3. Simulated annealing – combinatorial optimisation
algorithm originally designed to simulate the cooling properties of metals
Static Spatial Microsimulation• But they all attempt to do the same thing
• Turn a selection of aggregate constraint tables
• Into an individual level population allocated to spatial areas
Static Spatial Microsimulation• While minimising the difference between the distribution
of the constraint table attributes for each zone and the distribution of the attributes aggregated from the synthesised population…
Zones Zones
Male – gender constraint counts
Male – gender synthesised population counts
Static Spatial MicrosimulationFit statistic used is normally Total Absolute Error (TAE)
TAE = ∑i∑j|Tij – Eij|
WhereTij is the sum of the observed counts for the cell ijEij is the sum of the expected counts for the cell ij
Williamson et al 1998
Static Spatial Microsimulation – Deterministic Reweighting
A very big iterative proportional fitting algorithm
Stage 1 – calculate weights for each individual
Smith et al 2009
Static Spatial Microsimulation – Deterministic Reweighting
Stage 2 - proportionally fit each weight to the population
Smith et al 2009
Static Spatial Microsimulation – Deterministic Reweighting
Iterate over the reweighting process until:a. the fit statistic does not improve any furtherb. A threshold set on the fit statistic to indicate
convergence is reachedMove to next zone
This algorithm has been widely used in health studies.
Static Spatial Microsimulation – Conditional Probabilities
Birking and Clarke 1988
Stage 1 – calculate conditional probabilities for all possible combinations of individuals
Static Spatial Microsimulation – Conditional Probabilities
Birking and Clarke 1988
Stage 2 – Assign synthetic characteristics applying conditional probabilities
Static Spatial Microsimulation – Conditional Probabilities
Birking and Clarke 1988
Stage 3 – Constrain weights to constraint table distributions
Static Spatial Microsimulation – Conditional Probabilities
Birking and Clarke 1988
Stage 4 – Calculate TAEStage 5 – Iterate over previous stages until no
further reduction in TAEStage 6 – Move to next zone
Particular strength of the algorithm is that it does not require an input population
Static Spatial Microsimulation – Simulated Annealing
sample population constraint 1 constraint n…
synthetic population
zone x
aggregation 1 aggregation n…
calculate fitness - TAE
Harland et al. 2012
A combinatorial optimisation algorithm well suited to static spatial microsimulation…
Accurate, produces good results because it can take backwards steps
Computationally intensive so care needed when implementing code
Static Spatial Microsimulation – Simulated Annealing
Harland et al. 2012
What do we mean by taking backwards steps?
Crossing the valley between say point A to reach point B
Static Spatial Microsimulation – Simulated Annealing
Comparing the Approaches
Harland et al 2012
Not any more…
Dynamic Spatial MicrosimulationTakes a population, whether synthesised or real world
data, and moves it through space and timeUses derived probabilities to determine outcomes for
individuals at each time-stepIndividuals can typically
DieBe bornMigrateGet marriedGet divorced… and any number of other actions for which probabilities can be derived
Dynamic Spatial Microsimulation
Time step 0
Time step 1
Time step 2
Transition matrices
Transition matrices
Dynamic Spatial MicrosimulationSeems simple…Idea is simple but many complicating factors
1. Number of transitional probabilities dependent on number of attributes
2. Birth, death, migration, etc… not ubiquitous across zones
3. Derivation of probabilities become more complex and burdensome than the modelling process.
4. With large populations over longer time periods models can take time to setup and run, causing difficulties with calibration and evaluation
A Word on Model EvaluationAll too often not dealt with sufficiently in the literature.Williamson and Voas (1998) presented work into model
evaluation and assessmentHarland et al. (2012) examined three different model
approaches evaluating the algorithm performanceEvaluation of large models is very difficult and time
consuming but for reliable results it needs to be doneDifferent levels of statistics provide information about
different areas of the modelCell level – fine grained (often not presented)Attribute level – medium detail (often not presented)Constraint level – high level model assessment
Microsimulation Vs Agent-Based Modelling
Great deal of similarity between the two approachesBoth operate at the individual levelDynamic microsimulation moves individuals through time as
does ABMCould argue for simple behaviour in dynamic microsimulationBoth are very data hungry
Also several differencesABMs are enhanced by interaction of individuals with their
environmentBehaviour in ABM not restricted to simple transitional
probabilitiesABMs cannot handle the volumes of data… yet!
Static spatial microsimulation synthesises an individual level population from aggregate data
A variety of approaches have been used for static spatial microsimulation
-iterative reweighting
-statistical probabilities
-combinatorial optimisation
All have there benefits and there drawbacks…
Summary
Dynamic microsimulation moves a population through time
Has similarities to ABM but also major differences
Static spatial microsimulation may have a role to play with both approaches
One major complicating factor for dynamic microsimulation is the derivation of transitional probabilities…
Summary
ReferencesBallas, D., Clarke, G., Dorling, D., Eyre, H., Thomas, B., and Rossiter, D.(2005) SimBritain: a spatial
microsimulation approach to population dynamics. Population, Space and Place 11, 13–34.
Birkin, M. & Clarke, M. (1988). SYNTHESIS - a synthetic spatial information system for urban and regional analysis: methods and examples'' Environment and Planning A, 20, 1645 -1671.
Harland K., Heppenstall A. J., Smith D., and Birkin, M. (2012) Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques Journal of Artificial Societies and Social Simulation 15 (1) 1
Smith M D, Clarke P G, Ransley J, Cade J. (2005). Food Access and Health : A Microsimulation Framework for Analysis. Studies in Regional Science. 35(4). 909 – 927
Smith D M, Clarke G P, Harland K, (2009), Improving the synthetic data generation process in spatial microsimulation models. Environment and Planning A 41(5) 1251 – 1268
Tomintz MN, GP Clarke, (2008) The geography of smoking in Leeds: estimating individual smoking rates and the implications for the location of stop smoking services. Area 40(3): 341-353
Williamson, P., Birkin, M., & Rees, P.H. (1998). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30, 785-816.
Wu, B., Birkin, M. and Rees P. (2008) A spatial microsimulation model with student agents. Computers, Environment and Urban Systems, 32 (6). pp. 440–453
