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 to 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 networks after major administrative transformations in Finland.

翻译：现实世界的时空数据集及相关现象往往难以可视化或获得总体概览。为总结此类数据所蕴含的信息，我们结合了两种成熟的统计建模方法。针对空间维度，我们采用条件自回归的内在修正形式，并将其与隐马尔可夫模型相结合，使空间模式随时间动态变化。我们将该方法应用于1750-1850年芬兰因麻疹死亡的教区登记数据，从而获得了对先前未知感染动态的新见解。研究识别出五种独特的、反复出现的状态，这些状态描述了时空上不同的感染负担及潜在传播路径。我们还发现，约在1812年前后，最典型空间模式的出现发生了转变，这可能是由于芬兰经历重大行政变革后通信网络的变化所致。