Tracing the Attention of Moving Citizens

TL;DR

This study analyzes how mobile users' physical movements and online behaviors are interconnected by constructing and examining mobility and attention networks from smartphone data, revealing universal behavioral patterns and community structures.

Contribution

It introduces a combined analysis of mobility and attention networks using renormalisation, uncovering universal properties, community structures, and location-based behaviors with a new geometric network model.

Findings

Mobility network exhibits small-world property with exponential decay.

Attention network shows self-similar power-law behavior.

Identified three location-based behaviors: shopping, dating, taxi-calling.

Abstract

With the widespread use of mobile computing devices in contemporary society, our trajectories in the physical space and virtual world are increasingly closely connected. Using the anonymous smartphone data of $1 \times 10^5$ users in 30 days, we constructed the mobility network and the attention network to study the correlations between online and offline human behaviours. In the mobility network, nodes are physical locations and edges represent the movements between locations, and in the attention network, nodes are websites and edges represent the switch of users between websites. We apply the box-covering method to renormalise the networks. The investigated network properties include the size of box $l_B$ and the number of boxes $N(l_B)$. We find two universal classes of behaviours: the mobility network is featured by a small-world property, $N(l_B) \simeq e^{-l_B}$, whereas the attention network is characterised by a self-similar property $N(l_B) \simeq l_B^{-\gamma}$. In particular, with the increasing of the length of box $l_B$, the degree correlation of the network changes from positive to negative which indicates that there are two layers of structure in the mobility network. We use the results of network renormalisation to detect the community and map the structure of the mobility network. Further, we located the most relevant websites visited in these communities, and identified three typical location-based behaviours, including the shopping, dating, and taxi-calling. Finally, we offered a revised geometric network model to explain our findings in the perspective of spatial-constrained attachment.