  Spatial-temporal prediction is a fundamental problem for constructing smart
city, which is useful for tasks such as traffic control, taxi dispatching, and
environmental policy making. Due to data collection mechanism, it is common to
see data collection with unbalanced spatial distributions. For example, some
cities may release taxi data for multiple years while others only release a few
days of data; some regions may have constant water quality data monitored by
sensors whereas some regions only have a small collection of water samples. In
this paper, we tackle the problem of spatial-temporal prediction for the cities
with only a short period of data collection. We aim to utilize the long-period
data from other cities via transfer learning. Different from previous studies
that transfer knowledge from one single source city to a target city, we are
the first to leverage information from multiple cities to increase the
stability of transfer. Specifically, our proposed model is designed as a
spatial-temporal network with a meta-learning paradigm. The meta-learning
paradigm learns a well-generalized initialization of the spatial-temporal
network, which can be effectively adapted to target cities. In addition, a
pattern-based spatial-temporal memory is designed to distill long-term temporal
information (i.e., periodicity). We conduct extensive experiments on two tasks:
traffic (taxi and bike) prediction and water quality prediction. The
experiments demonstrate the effectiveness of our proposed model over several
competitive baseline models.
