  Cellular phones are now offering an ubiquitous means for scientists to
observe life: how people act, move and respond to external influences. They can
be utilized as measurement devices of individual persons and for groups of
people of the social context and the related interactions. The picture of human
life that emerges shows complexity, which is manifested in such data in
properties of the spatiotemporal tracks of individuals. We extract from
smartphone-based data for a set of persons important locations such as "home",
"work" and so forth over fixed length time-slots covering the days in the
data-set. This set of typical places is heavy-tailed, a power-law distribution
with an exponent close to -1.7. To analyze the regularities and stochastic
features present, the days are classified for each person into regular,
personal patterns. To this are superimposed fluctuations for each day. This
randomness is measured by "life" entropy, computed both before and after
finding the clustering so as to subtract the contribution of a number of
patterns. The main issue, that we then address, is how predictable individuals
are in their mobility. The patterns and entropy are reflected in the
predictability of the mobility of the life both individually and on average. We
explore the simple approaches to guess the location from the typical behavior,
and of exploiting the transition probabilities with time from location or
activity A to B. The patterns allow an enhanced predictability, at least up to
a few hours into the future from the current location. Such fixed habits are
most clearly visible in the working-day length.
