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A greater understanding of human dynamics as they play out in both physical space and through interpersonal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight into the space-time distribution of population and their activity patterns, while interpersonal communication can now be measured at the population scale through digital interactions. In this work, we propose a novel method to discover these dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We compare these results to US census regions and economic and infrastructural networks. We find that the broad spatial delineation of communities and sub-communities is consistent with United States regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of social regions that can be consistently applied anywhere there is a sufficiently rich data source. A deeper understanding of urban social structure measured by spatially embedded communication networks can enable a better understanding of the interactions between urban social and physical contexts. This, in turn, may enable urban managers and policy makers to identify strategies for supporting urban resilience.

Using Digital Trace Data to Identify Regions and Cities