2023/03/29 by Divya Kappara, Kappara, Divya, Arup Bose +3
Economics, Econometrics and Finance · Mathematics · Medicine · #62H11 #62H20 (Primary) 62F12 #62M30 (Secondary) #62P10 #92D30 #Applications (stat.AP) #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Spatial and Panel Data Analysis #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2303.16824
openalex publication_date 2023/03/29 · openalex created_date 2023/04/05 · openalex updated_date 2026/07/28
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 ρ. Due to the special properties of ρ, 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.