  Predicting when and where events will occur in cities, like taxi pick-ups,
crimes, and vehicle collisions, is a challenging and important problem with
many applications in fields such as urban planning, transportation optimization
and location-based marketing. Though many point processes have been proposed to
model events in a continuous spatio-temporal space, none of them allow for the
consideration of the rich contextual factors that affect event occurrence, such
as weather, social activities, geographical characteristics, and traffic. In
this paper, we propose \textsf{DMPP} (Deep Mixture Point Processes), a point
process model for predicting spatio-temporal events with the use of rich
contextual information; a key advance is its incorporation of the heterogeneous
and high-dimensional context available in image and text data. Specifically, we
design the intensity of our point process model as a mixture of kernels, where
the mixture weights are modeled by a deep neural network. This formulation
allows us to automatically learn the complex nonlinear effects of the
contextual factors on event occurrence. At the same time, this formulation
makes analytical integration over the intensity, which is required for point
process estimation, tractable. We use real-world data sets from different
domains to demonstrate that DMPP has better predictive performance than
existing methods.
