  Next place prediction algorithms are invaluable tools, capable of increasing
the efficiency of a wide variety of tasks, ranging from reducing the spreading
of diseases to better resource management in areas such as urban planning. In
this work we estimate upper and lower limits on the predictability of human
mobility to help assess the performance of competing algorithms. We do this
using GPS traces from 604 individuals participating in a multi year long
experiment, The Copenhagen Networks study. Earlier works, focusing on the
prediction of a participant's whereabouts in the next time bin, have found very
high upper limits (>90%). We show that these upper limits are highly dependent
on the choice of a spatiotemporal scales and mostly reflect stationarity, i.e.
the fact that people tend to not move during small changes in time. This leads
us to propose an alternative approach, which aims to predict the next location,
rather than the location in the next bin. Our approach is independent of the
temporal scale and introduces a natural length scale. By removing the effects
of stationarity we show that the predictability of the next location is
significantly lower (~71%) than the predictability of the location in the next
bin.
