The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest -PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research.
We present VibN, a mobile sensing application deployed at large scale through the Apple App Store and the Android Market. VibN has been built to determine "what's going on" around the user in real-time by exploiting multiple sensor feeds. The application allows its users to explore live points of interest of the city by presenting real-time hotspots from sensor data. Each hotspot is characterized by a demographics breakdown of inhabitants and a list of short audio clips. The audio clips augment traditional microblogging methods by allowing users to automatically and manually provide rich audio data about their locations. VibN also allows one to browse historical points of interest and view how locations in a city evolve over time. Additionally, VibN automatically determines a user's personal points of interest, which are a means for building a user's breadcrumb diary of locations where they have spent significant amount of time. In this paper, we present the design, evaluation, and results from the large scale deployment of VibN through the popular Apple App Store and Android Market.
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