  Revealing the hidden patterns shaping the urban environment is essential to
understand its dynamics and to make cities smarter. Recent studies have
demonstrated that learning the representations of urban regions can be an
effective strategy to uncover the intrinsic characteristics of urban areas.
However, existing studies lack in incorporating diversity in urban data
sources. In this work, we propose heterogeneous urban graph attention network
(HUGAT), which incorporates heterogeneity of diverse urban datasets. In HUGAT,
heterogeneous urban graph (HUG) incorporates both the geo-spatial and temporal
people movement variations in a single graph structure. Given a HUG, a set of
meta-paths are designed to capture the rich urban semantics as composite
relations between nodes. Region embedding is carried out using heterogeneous
graph attention network (HAN). HUGAT is designed to consider multiple learning
objectives of city's geo-spatial and mobility variations simultaneously. In our
extensive experiments on NYC data, HUGAT outperformed all the state-of-the-art
models. Moreover, it demonstrated a robust generalization capability across the
various prediction tasks of crime, average personal income, and bike flow as
well as the spatial clustering task.
