  Data-driven research in mobility has prospered in recent years, providing
solutions to real-world challenges including forecasting epidemics and planning
transportation. These advancements were facilitated by computational tools
enabling the analysis of large-scale data-sets of digital traces. One of the
challenges when pre-processing spatial trajectories is the so-called stop
location detection, that entails the reduction of raw time series to sequences
of destinations where an individual was stationary. The most widely adopted
solution to this problem was proposed by Hariharan and Toyama (2004) and
involves filtering out non-stationary measurements, then applying agglomerative
clustering on the stationary points. This state-of-the-art solution, however,
suffers of two limitations: (i) frequently visited places located very close
(such as adjacent buildings) are likely to be merged into a unique location,
due to inherent measurement noise, (ii) traces for multiple users can not be
analysed simultaneously, thus the definition of destination is not shared
across users. In this paper, we describe the Infostop algorithm that overcomes
the limitations of the state-of-the-art solution by leveraging the flow-based
network community detection algorithm Infomap. We test Infostop for a
population of $\sim 1000$ individuals with highly overlapping mobility. We show
that the size of locations detected by Infostop saturates for increasing number
of users and that time complexity grows slower than for previous solutions. We
demonstrate that Infostop can be used to easily infer social meetings. Finally,
we provide an open-source implementation of Infostop, written in Python and
C++, that has a simple API and can be used both for labeling time-ordered
coordinate sequences (GPS or otherwise), and unordered sets of spatial points.
