  Among various region embedding methods, graph-based region relation learning
models stand out, owing to their strong structure representation ability for
encoding spatial correlations with graph neural networks. Despite their
effectiveness, several key challenges have not been well addressed in existing
methods: i) Data noise and missing are ubiquitous in many spatio-temporal
scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g.,
mobility traces) usually exhibits distribution heterogeneity across space and
time. In such cases, current methods are vulnerable to the quality of the
generated region graphs, which may lead to suboptimal performance. In this
paper, we tackle the above challenges by exploring the Automated
Spatio-Temporal graph contrastive learning paradigm (AutoST) over the
heterogeneous region graph generated from multi-view data sources. Our \model\
framework is built upon a heterogeneous graph neural architecture to capture
the multi-view region dependencies with respect to POI semantics, mobility flow
patterns and geographical positions. To improve the robustness of our GNN
encoder against data noise and distribution issues, we design an automated
spatio-temporal augmentation scheme with a parameterized contrastive view
generator. AutoST can adapt to the spatio-temporal heterogeneous graph with
multi-view semantics well preserved. Extensive experiments for three downstream
spatio-temporal mining tasks on several real-world datasets demonstrate the
significant performance gain achieved by our \model\ over a variety of
baselines. The code is publicly available at https://github.com/HKUDS/AutoST.
