Spatial Microsimulation

Generic microsimulation models the evolution or behaviour of a population of individuals or households, often without explicit geography, to study policy or dynamics over time. Spatial microsimulation adds the geographic dimension: its defining task is to synthesize individual-level populations for many small areas so that each area's totals match local census constraints, attaching a location to every synthetic individual. In practice spatial microsimulation is the population-synthesis front end that produces the georeferenced agents which a behavioural or dynamic microsimulation then operates on.

Iterative proportional fitting reweights survey individuals, repeatedly rescaling continuous weights until the weighted totals match every census margin, and then integerises those weights into whole people. Combinatorial optimisation instead searches directly for a set of whole survey individuals — using simulated annealing, genetic algorithms, or generalized regression weighting like GREGWT — whose aggregate matches the constraints. IPF is fast and deterministic but needs integerisation, whereas combinatorial methods yield integer populations natively and handle complex constraints, at higher computational cost.

Because the individual-level small-area data being estimated are unobserved, validation relies on indirect checks. Internal validation confirms that the synthetic population reproduces the constraints used to build it; external validation compares aggregated synthetic outputs against tables held out of the fitting or against an independent dataset for the same area. Analysts also examine plausibility of joint distributions and, where any ground-truth microdata exist for a subset of areas, benchmark against them. No single test is decisive, so credible studies report several complementary validation measures.