  Nowadays, human movement in urban spaces can be traced digitally in many
cases. It can be observed that movement patterns are not constant, but vary
across time and space. In this work,we characterize such spatio-temporal
patterns with an innovative combination of two separate approaches that have
been utilized for studying human mobility in the past. First, by using
non-negative tensor factorization (NTF), we are able to cluster human behavior
based on spatio-temporal dimensions. Second, for understanding these clusters,
we propose to use HypTrails, a Bayesian approach for expressing and comparing
hypotheses about human trails. To formalize hypotheses we utilize data that is
publicly available on the Web, namely Foursquare data and census data provided
by an open data platform. By applying this combination of approaches to taxi
data in Manhattan, we can discover and explain different patterns in human
mobility that cannot be identified in a collective analysis. As one example, we
can find a group of taxi rides that end at locations with a high number of
party venues (according to Foursquare) on weekend nights. Overall, our work
demonstrates that human mobility is not one-dimensional but rather contains
different facets both in time and space which we explain by utilizing online
data. The findings of this paper argue for a more fine-grained analysis of
human mobility in order to make more informed decisions for e.g., enhancing
urban structures, tailored traffic control and location-based recommender
systems.
