  Human mobility analysis is an important issue in social sciences, and
mobility data are among the most sought-after sources of information in ur-
Data ban studies, geography, transportation and territory management. In
network sciences mobility studies have become popular in the past few years,
especially using mobile phone location data. For preserving the customer
privacy, datasets furnished by telecom operators are anonymized. At the same
time, the large size of datasets often makes the task of calculating all
observed trajectories very difficult and time-consuming. One solution could be
to sample users. However, the fact of not having information about the mobile
user makes the sampling delicate. Some researchers select randomly a sample of
users from their dataset. Others try to optimize this method, for example,
taking into account only users with a certain number or frequency of locations
recorded. At the first glance, the second choice seems to be more efficient:
having more individual traces makes the analysis more precise. However, the
most frequently used CDR data (Call Detail Records) have location generated
only at the moment of communication (call, SMS, data connection). Due to this
fact, users mobility patterns cannot be precisely built upon their
communication patterns. Hence, these data have evident short-comings both in
terms of spatial and temporal scale. In this paper we propose to estimate the
correlation between the users communication and mo- bility in order to better
assess the bias of frequency based sampling. Using technical GSM network data
(including communication but also independent mobility records), we will
analyze the relationship between communication and mobility patterns.
