  The theme of human mobility is transversal to multiple fields of study and
applications, from ad-hoc networks to smart cities, from transportation
planning to recommendation systems on social networks. Despite the considerable
efforts made by a few scientific communities and the relevant results obtained
so far, there are still many issues only partially solved, that ask for general
and quantitative methodologies to be addressed. A prominent aspect of
scientific and practical relevance is how to characterize the mobility behavior
of individuals. In this article, we look at the problem from a location-centric
perspective: we investigate methods to extract, classify and quantify the
symbolic locations specified in telco trajectories, and use such measures to
feature user mobility. A major contribution is a novel trajectory summarization
technique for the extraction of the locations of interest, i.e. attractive,
from symbolic trajectories. The method is built on a density-based trajectory
segmentation technique tailored to telco data, which is proven to be robust
against noise. To inspect the nature of those locations, we combine the two
dimensions of location attractiveness and frequency into a novel location
taxonomy, which allows for a more accurate classification of the visited
places. Another major contribution is the selection of suitable entropy-based
metrics for the characterization of single trajectories, based on the diversity
of the locations of interest. All these components are integrated in a
framework utilized for the analysis of 100,000+ telco trajectories. The
experiments show how the framework manages to dramatically reduce data
complexity, provide high-quality information on the mobility behavior of people
and finally succeed in grasping the nature of the locations visited by
individuals.
