Urban Analytics: Multiplexed and Dynamic Community Networks

TL;DR

This paper reviews how heterogeneous mobile big data, combined with NLP and machine learning, can be used to analyze the complex, evolving community structures in urban environments, informing urban planning and infrastructure design.

Contribution

It introduces a comprehensive review of algorithms for detecting multiplexed and dynamic urban community networks using big data, emphasizing robustness and causality.

Findings

Effective community detection algorithms are identified.

Insights into the stability and evolution of urban communities.

Potential applications in urban planning and infrastructure design.

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.