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Disruptive events significantly alter spatial and social interactions amongpeople and places. To examine the structural changes in spatial and socialinteraction networks in pre- and post-periods of the COVID-19 pandemic, weemploy the Louvain method to algorithmically detect regions (communities)within the county-to-county networks of the SafeGraph mobility and Facebooksocial connectedness. We then utilize a range of partition similarity metrics,including adjusted Rand, z-Rand, Normalized Mutual Information (NMI), andJaccard indices, to quantitatively measure the similarity of regions betweenthe pre- and post-periods partitions of each network. Our findings reveal thatin the post-pandemic period, spatial interactions led to the formation oflocalized geographic communities or regions characterized by higher modularactivity within each region. In contrast, online social interactions shiftedtowards longer distance connections, resulting in the emergence of largerregions marked by strong friendship ties that often encompassed multiplestates. By understanding these changes, we contribute to a better comprehensionof the pandemic's impact on our interconnected physical-virtual world,providing valuable insights for future research and informing strategies toadapt to the evolving dynamics of human interactions.

The effect of disruptive events on spatial and social interactions: An assessment of structural changes in pre-and post-COVID-19 pandemic networks