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Spatial association measures for univariate static spatial data are widelyused. When the data is in the form of a collection of spatial vectors with thesame temporal domain of interest, we construct a measure of similarity betweenthe regions' series, using Bergsma's correlation coefficient $\rho$. Due to thespecial properties of $\rho$, unlike other spatial association measures whichtest for spatial randomness, our statistic can account for spatial pairwiseindependence. We have derived the asymptotic behavior of our statistic undernull (independence of the regions) and alternate cases (the regions aredependent). We explore the alternate scenario of spatial dependence further,using simulations for the SAR and SMA dependence models. Finally, we provideapplication to modelling and testing for the presence of spatial association inCOVID-19 incidence data, by using our statistic on the residuals obtained aftermodel fitting.

Measuring spatial association and testing spatial independence based on short time course data