Human activity type inference has long been the focus for applications ranging from
managing transportation demand to monitoring changes in land use patterns. Today’s ever increasing
volume of mobility data allow researchers to explore a wide range of methodological approaches
for this task. Such data, however, lack reference observations that would allow the validation of
methodological approaches. This research proposes a methodological framework for urban activity
type inference using a Dirichlet multinomial dynamic Bayesian network with an empirical Bayes prior
that can be applied to mobility data of low spatiotemporal resolution. The method was validated
using open source Foursquare data under different isochrone configurations. The results provide
evidence of the limits of activity detection accuracy using such data as determined by the Area
Under Receiving Operating Curve (AUROC), log-loss, and accuracy metrics. At the same time,
results demonstrate that a hierarchical modeling framework can provide some flexibility against the
challenges related to the nature of unsupervised activity classification using trajectory variables and
POIs as input
