  Spatial association measures for univariate static spatial data are widely
used. When the data is in the form of a collection of spatial vectors with the
same temporal domain of interest, we construct a measure of similarity between
the regions' series, using Bergsma's correlation coefficient $\rho$. Due to the
special properties of $\rho$, unlike other spatial association measures which
test for spatial randomness, our statistic can account for spatial pairwise
independence. We have derived the asymptotic behavior of our statistic under
null (independence of the regions) and alternate cases (the regions are
dependent). We explore the alternate scenario of spatial dependence further,
using simulations for the SAR and SMA dependence models. Finally, we provide
application to modelling and testing for the presence of spatial association in
COVID-19 incidence data, by using our statistic on the residuals obtained after
model fitting.
