  The enormous amount of recently available mobile phone data is providing
unprecedented direct measurements of human behavior. Early recognition and
prediction of behavioral patterns are of great importance in many societal
applications like urban planning, transportation optimization, and health-care.
Understanding the relationships between human behaviors and location's context
is an emerging interest for understanding human-environmental dynamics. Growing
availability of Web 2.0, i.e. the increasing amount of websites with mainly
user created content and social platforms opens up an opportunity to study such
location's contexts. This paper investigates relationships existing between
human behavior and location context, by analyzing log mobile phone data
records. First an advanced approach to categorize areas in a city based on the
presence and distribution of categories of human activity (e.g., eating,
working, and shopping) found across the areas, is proposed. The proposed
classification is then evaluated through its comparison with the patterns of
temporal variation of mobile phone activity and applying machine learning
techniques to predict a timeline type of communication activity in a given
location based on the knowledge of the obtained category vs. land-use type of
the locations areas. The proposed classification turns out to be more
consistent with the temporal variation of human communication activity, being a
better predictor for those compared to the official land use classification.
