  An increasing number of related urban data sources have brought forth novel
opportunities for learning urban region representations, i.e., embeddings. The
embeddings describe latent features of urban regions and enable discovering
similar regions for urban planning applications. Existing methods learn an
embedding for a region using every different type of region feature data, and
subsequently fuse all learned embeddings of a region to generate a unified
region embedding. However, these studies often overlook the significance of the
fusion process. The typical fusion methods rely on simple aggregation, such as
summation and concatenation, thereby disregarding correlations within the fused
region embeddings.
  To address this limitation, we propose a novel model named HAFusion. Our
model is powered by a dual-feature attentive fusion module named DAFusion,
which fuses embeddings from different region features to learn higher-order
correlations between the regions as well as between the different types of
region features. DAFusion is generic - it can be integrated into existing
models to enhance their fusion process. Further, motivated by the effective
fusion capability of an attentive module, we propose a hybrid attentive feature
learning module named HALearning to enhance the embedding learning from each
individual type of region features. Extensive experiments on three real-world
datasets demonstrate that our model HAFusion outperforms state-of-the-art
methods across three different prediction tasks. Using our learned region
embedding leads to consistent and up to 31% improvements in the prediction
accuracy.

Machine Learning;Databases