  Iterative proportional fitting (IPF) is a widely used method for spatial
microsimulation. The technique results in non-integer weights for individual
rows of data. This is problematic for certain applications and has led many
researchers to favour combinatorial optimisation approaches such as simulated
annealing. An alternative to this is `integerisation' of IPF weights: the
translation of the continuous weight variable into a discrete number of unique
or `cloned' individuals. We describe four existing methods of integerisation
and present a new one. Our method --- `truncate, replicate, sample' (TRS) ---
recognises that IPF weights consist of both `replication weights' and
`conventional weights', the effects of which need to be separated. The
procedure consists of three steps: 1) separate replication and conventional
weights by truncation; 2) replication of individuals with positive integer
weights; and 3) probabilistic sampling. The results, which are reproducible
using supplementary code and data published alongside this paper, show that TRS
is fast, and more accurate than alternative approaches to integerisation.
