Geo-Social Simulation Framework
|Fig. 1. Social Network in Geo-Social Simulation|
uman mobility and social networks have received considerable attention from researchers in recent years. What has been sorely missing is a comprehensive data set that not only addresses geometric movement patterns derived from trajectories, but also provides social networks and causal links as to why movement happens in the first place. To some extent, this challenge is addressed by studying location-based social networks (LBSNs). However, the scope of real-world LBSN data sets is constrained by privacy concerns, a lack of authoritative ground-truth, their sparsity, and small size. To overcome these issues we have infused a novel geographically explicit agent-based simulation framework to simulate human behavior and to create synthetic but realistic LBSN data based on human patterns-of-life (i.e., a geo-social simulation). Such data not only captures the location of users over time, but also their motivation, and interactions via temporal social networks.
|Fig. 2. Spatial Network in Geo-Social Simulation|
This framework models human patterns of life in urban environments based on well-established social science theory. We simulated people living and working in places, visiting restaurants to eat, going to bars, coffee shops, and places of worship to socialize and effectively create an evolving social network. Our framework is unique in the sense that we combine temporal social networks and patterns of daily life, whereas many existing frameworks consider only one of them. Figures 1 and 2 show a snapshot of our simulation. In an artificially generated world, 5,000 agents go to work are created at initialization. The spatial network in Figure 2 all agents (blue and red dots depending on whether agents are hungry or not) in their home places outside the virtual city while the social network of agents is shown Figure 1. Each color in Figure 1 represents a social interest. We observe that some groups of agents having the same interest (e.g., blue or yellow) form social clusters. The following video captures animation of the simulation including evolution of social networks.
Once the simulation is started, we observe agents commuting to work, going to restaurants to eat and going to pubs to meet friends. When the simulation begins, there are no agent social networks. However, as the simulation runs, the agents make friendships. We observe social ties quickly emerging between agents. Shortly after the simulation has begun, the agents have self-organized into coherent social networks. According to our model of social interaction, social ties between agents appear and strengthen as they meet in the physical world. Every day at midnight, all social network edges are decreased by a factor, leading to weak links to disappear, leading to friendless agents becoming ejected from the large social network.