  Although there is substantial literature on identifying structural changes
for continuous spatio-temporal processes, the same is not true for categorical
spatio-temporal data. This work bridges that gap and proposes a novel
spatio-temporal model to identify changepoints in ordered categorical data. The
model leverages an additive mean structure with separable Gaussian space-time
processes for the latent variable. Our proposed methodology can detect
significant changes in the mean structure as well as in the spatio-temporal
covariance structures. We implement the model through a Bayesian framework that
gives a computational edge over conventional approaches. From an application
perspective, our approach's capability to handle ordinal categorical data
provides an added advantage in real applications. This is illustrated using
county-wise COVID-19 data (converted to categories according to CDC guidelines)
from the state of New York in the USA. Our model identifies three changepoints
in the transmission levels of COVID-19, which are indeed aligned with the
``waves'' due to specific variants encountered during the pandemic. The
findings also provide interesting insights into the effects of vaccination and
the extent of spatial and temporal dependence in different phases of the
pandemic.
