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Tracing the Attention of Moving Citizens

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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 × 105 users in a major city of China, we study the interplay between online and offline human behaviors by constructing the mobility network (offline) and the attention network (online). Using the network renormalization technique, we find that they belong to two different classes: the mobility network is small-world, whereas the attention network is fractal. We then divide the city into different areas based on the features of the mobility network discovered under renormalization. Interestingly, this spatial division manifests the location-based online behaviors, for example shopping, dating, and taxi-requesting. Finally, we offer a geometric network model to help us understand the relationship between small-world and fractal networks.

No MeSH data available.


The spatial distribution of base stations and their connections (the movement of users between base stations).(A) The distribution of 14,909 base stations in our dataset. The brightness of the data points is proportional to the mobility traffic to these stations. (B) The movement of users between stations. Base map is from Google Map.
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f7: The spatial distribution of base stations and their connections (the movement of users between base stations).(A) The distribution of 14,909 base stations in our dataset. The brightness of the data points is proportional to the mobility traffic to these stations. (B) The movement of users between stations. Base map is from Google Map.

Mentions: We construct two networks to trace how users move in the virtual world (attention network) and in the physical world (mobility network). In the mobility network, the nodes are mobile base stations (N = 9,899), and the edges (N = 39,083) show how users travel from one base station to another. In the attention network, the nodes (N = 16,476) are websites, and edges (E = 144,909) represent the switch of users between websites. As shown in Fig. 7A, the distribution of the base stations is uneven, as more stations are needed in high population density areas. Figure 7B shows the mobility network, which portrays the collective moving trajectory of city residents.


Tracing the Attention of Moving Citizens
The spatial distribution of base stations and their connections (the movement of users between base stations).(A) The distribution of 14,909 base stations in our dataset. The brightness of the data points is proportional to the mobility traffic to these stations. (B) The movement of users between stations. Base map is from Google Map.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5016808&req=5

f7: The spatial distribution of base stations and their connections (the movement of users between base stations).(A) The distribution of 14,909 base stations in our dataset. The brightness of the data points is proportional to the mobility traffic to these stations. (B) The movement of users between stations. Base map is from Google Map.
Mentions: We construct two networks to trace how users move in the virtual world (attention network) and in the physical world (mobility network). In the mobility network, the nodes are mobile base stations (N = 9,899), and the edges (N = 39,083) show how users travel from one base station to another. In the attention network, the nodes (N = 16,476) are websites, and edges (E = 144,909) represent the switch of users between websites. As shown in Fig. 7A, the distribution of the base stations is uneven, as more stations are needed in high population density areas. Figure 7B shows the mobility network, which portrays the collective moving trajectory of city residents.

View Article: PubMed Central - PubMed

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 × 105 users in a major city of China, we study the interplay between online and offline human behaviors by constructing the mobility network (offline) and the attention network (online). Using the network renormalization technique, we find that they belong to two different classes: the mobility network is small-world, whereas the attention network is fractal. We then divide the city into different areas based on the features of the mobility network discovered under renormalization. Interestingly, this spatial division manifests the location-based online behaviors, for example shopping, dating, and taxi-requesting. Finally, we offer a geometric network model to help us understand the relationship between small-world and fractal networks.

No MeSH data available.