  With recent advances in sensing technologies, a myriad of spatio-temporal
data has been generated and recorded in smart cities. Forecasting the evolution
patterns of spatio-temporal data is an important yet demanding aspect of urban
computing, which can enhance intelligent management decisions in various
fields, including transportation, environment, climate, public safety,
healthcare, and others. Traditional statistical and deep learning methods
struggle to capture complex correlations in urban spatio-temporal data. To this
end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed,
achieving great promise in recent years. STGNNs enable the extraction of
complex spatio-temporal dependencies by integrating graph neural networks
(GNNs) and various temporal learning methods. In this manuscript, we provide a
comprehensive survey on recent progress on STGNN technologies for predictive
learning in urban computing. Firstly, we provide a brief introduction to the
construction methods of spatio-temporal graph data and the prevalent
deep-learning architectures used in STGNNs. We then sort out the primary
application domains and specific predictive learning tasks based on existing
literature. Afterward, we scrutinize the design of STGNNs and their combination
with some advanced technologies in recent years. Finally, we conclude the
limitations of existing research and suggest potential directions for future
work.
