  Real world spatio-temporal datasets, and phenomena related to them, are often
challenging to visualise or gain a general overview of. In order to summarise
information encompassed in such data, we combine two well known statistical
modelling methods. To account for the spatial dimension, we use the intrinsic
modification of the conditional autoregression, and incorporate it with the
hidden Markov model, allowing the spatial patterns to vary over time. We apply
our method into parish register data considering deaths caused by measles in
Finland in 1750-1850, and gain novel insight of previously undiscovered
infection dynamics. Five distinctive, reoccurring states describing spatially
and temporally differing infection burden and potential routes of spread are
identified. We also find that there is a change in the occurrences of the most
typical spatial patterns circa 1812, possibly due to changes in communication
routes after major administrative transformations in Finland.
