Extracting human mobility and social behavior from location-aware traces

The concepts of location and community are rapidly becoming key points in the design of new communication paradigms and in deploying emerging mobile computing services. The need of reliable and quantitative knowledge and predictions of some relevant information, such as which locations are enjoyed by people in their daily lives and how people aggregate within communities, advocates a realistic mobility model able to describe both the human mobility throughout locations and the human attitude to socialize within communities. Unfortunately, so far, neither the concept of location nor the concept of community has been univocally defined. In this paper, we approach the problem from the most basic of starting points, namely by analyzing the real Global Positioning System datasets of human mobility traces. On this elementary basis, the paper provides a few relevant contributions. We firstly derive a deep understanding of the term “location” and at the same time of the notion of community strictly related to it. Secondly, we merge the two concepts into what we call geo-community. By proceeding from real spatial data rather than from a priori reasonings, we are able to quantitatively describe geo-communities and infer the probability distributions of all the features of human behavior. Finally, not to lose social implications, we present the method to derive people sociality from geo-communities.