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Spatial microsimulation is an excellent option to create estimated populations at a range of spatial scales where data may be otherwise unavailable. In this chapter, we outline three common methods of spatial microsimulation, identifying the relative strengths and weaknesses of each approach. We conclude with a worked example using deterministic reweighting to estimate tobacco smoking prevalence by neighborhood in London, UK. This illustrates how spatial microsimulation may be used to estimate not only populations but also behaviors and how this information may then be used to predict the outcomes of policy change at the local level.
KeywordsSimulated annealing Smoking prevalence Constraint variable Microsimulation model Synthetic population
This work was funded by the ESRC funded grant “Modeling Individual Consumer Behavior” (RES-061-25-0030) and MRC Population Health Scientist Fellowship (G0802447). The modeling framework used was developed by Kirk Harland.
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