Detecting associated communities in social network and urban activity spaces
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Clusters in social network tell the heterogeneity of people’s connections, and clusters in geographical movement network display the difference in movement pattern. I test whether the two clusters show similar pattern to understand the complexity between social network and movement behaviors for implications on future urban structure that helps maintaining face-to-face social connections. I do community detection simultaneously and independently in both networks drawn from a mobile phone call dataset in Jiamusi, China. I involve distance decay to detect clusters due to long-distance geographical movements. I also do community detection in social network and project the social communities into geographical space by anchor points to examine whether long-distance movement communities are spatially associated with social communities. The result testifies my argument that people still require physical interaction in social life, even in the era of information.