  Mobile phone datasets allow for the analysis of human behavior on an
unprecedented scale. The social network, temporal dynamics and mobile behavior
of mobile phone users have often been analyzed independently from each other
using mobile phone datasets. In this article, we explore the connections
between various features of human behavior extracted from a large mobile phone
dataset. Our observations are based on the analysis of communication data of
100000 anonymized and randomly chosen individuals in a dataset of
communications in Portugal. We show that clustering and principal component
analysis allow for a significant dimension reduction with limited loss of
information. The most important features are related to geographical location.
In particular, we observe that most people spend most of their time at only a
few locations. With the help of clustering methods, we then robustly identify
home and office locations and compare the results with official census data.
Finally, we analyze the geographic spread of users' frequent locations and show
that commuting distances can be reasonably well explained by a gravity model.
