This paper looks at identifying the locations of users from
the Nokia MDC dataset throughout the day without taking
into consideration location based data. By looking at a users
habits and idiosyncrasies we determined the likelihood of a
users location within known stay regions which we call habitats. The features used to determine location were extracted
from a users interaction with the smart phone. None of the
features contained a users locations or a users proximity to
objects with known locations. Using a set of structured output support vector learning techniques we found that a users
location with respect to the areas of typical activities is well
predictable solely from daily routines and a smart phone
usage habits
