  Spatiotemporal learning plays a crucial role in mobile computing techniques
to empower smart cites. While existing research has made great efforts to
achieve accurate predictions on the overall dataset, they still neglect the
significant performance heterogeneity across samples. In this work, we
designate the performance heterogeneity as the reason for unfair spatiotemporal
learning, which not only degrades the practical functions of models, but also
brings serious potential risks to real-world urban applications. To fix this
gap, we propose a model-independent Fairness-aware framework for SpatioTemporal
Graph learning (FairSTG), which inherits the idea of exploiting advantages of
well-learned samples to challenging ones with collaborative mix-up.
Specifically, FairSTG consists of a spatiotemporal feature extractor for model
initialization, a collaborative representation enhancement for knowledge
transfer between well-learned samples and challenging ones, and fairness
objectives for immediately suppressing sample-level performance heterogeneity.
Experiments on four spatiotemporal datasets demonstrate that our FairSTG
significantly improves the fairness quality while maintaining comparable
forecasting accuracy. Case studies show FairSTG can counter both spatial and
temporal performance heterogeneity by our sample-level retrieval and
compensation, and our work can potentially alleviate the risks on
spatiotemporal resource allocation for underrepresented urban regions.
