Title: Urban Analytics: Multiplexed and Dynamic Community Networks,
Abstract: In the past decade, cities have experienced rapid growth, expansion, and
changes in their community structure. Many aspects of critical urban
infrastructure are closely coupled with the human communities that they serve.
Urban communities are composed of a multiplex of overlapping factors which can
be distinguished into cultural, religious, social-economic, political, and
geographical layers. In this paper, we review how increasingly available
heterogeneous mobile big data sets can be leveraged to detect the community
interaction structure using natural language processing and machine learning
techniques. A number of community layer and interaction detection algorithms
are then reviewed, with a particular focus on robustness, stability, and
causality of evolving communities. The better understanding of the structural
dynamics and multiplexed relationships can provide useful information to inform
both urban planning policies and shape the design of socially coupled urban
infrastructure systems.