  Spatio-temporal prediction is challenging attributing to the high
nonlinearity in temporal dynamics as well as complex dependency and
location-characterized pattern in spatial domains, especially in fields like
geophysics, traffic flow, etc. In this work, a novel graph-based directed
convolution is proposed to capture the spatial dependency. To model the
variable local pattern, we propose conditional local filters for convolution on
the directed graph, parameterized by the functions on local representation of
coordinate based on tangent space. The filter is embedded in a Recurrent Neural
Network (RNN) architecture for modeling the temporal dynamics with an explainer
established for interpretability of different time intervals' pattern. The
methods are evaluated on real-world datasets including road network traffic
flow, earth surface temperature \& wind flows and disease spread datasets,
achieving the state-of-the-art performance with improvements.
