As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks (GNNs) have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social relationships or user attributes to alleviate the data sparsity problem, such auxiliary information is not always available for privacy reasons. Self-supervised learning gives a new idea to alleviate the data sparsity problem, but most existing self-supervised recommendation methods cannot be directly used in the spatio-temporal graph of POI recommendations. In this paper, we propose a novel heterogeneous spatio-temporal graph contrastive learning method, HestGCL, to compensate for existing GNN-based methods’ shortcomings. To model spatio-temporal information, we generate spatio-temporally specific views and design view-specific heterogeneous graph neural networks to model spatial and temporal information, respectively. To alleviate data sparsity, we propose a cross-view contrastive strategy to capture differences and correlations among views, providing more supervision signals and boosting the overall performance collaboratively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HestGCL, which significantly outperforms existing methods.