Spatiotemporal　prediction　is　one　attractive　research　topic　in　urban　computing,　which　is　of　great　significance　to　urban　planning　and　management.　At　present,　there　are　many　attempts　to　predict　the　spatiotemporal　state　of　systems　using　various　deep　learning　models.　However,　most　existing　models　tend　to　improve　prediction　accuracy　with　larger　parameter　scale　and　time　consumption,　but　ignoring　ease　of　use　in　practice.　To　overcome　this　question,　we　propose　a　lightweight　spatiotemporal　graph　dilated　convolutional　network　called　STGDN　with　satisfactory　prediction　accuracy　and　lower　model　complexity.　More　specifically,　we　propose　a　novel　dilated　convolution　operator　and　integrate　it　into　traditional　causal　convolutional　networks　and　graph　convolutional　networks　to　greatly　improve　the　efficiency　of　prediction.　The　proposed　dilated　convolution　operator　can　significantly　reduce　the　depth　of　the　model,　thereby　reducing　the　parameter　scale　and　improving　the　computational　efficiency　of　the　model.　We　conducted　on　multi　experiments　on　three　real-world　spatiotemporal　datasets　(traffic　dataset,　PM2.5　dataset,　and　temperature　dataset)　to　prove　the　effectiveness　and　advantage　of　our　proposed　STGDN.　The　experimental　results　show　that　the　proposed　STGDN　model　outperforms　or　achieves　comparable　prediction　accuracy　of　the　existing　nine　baselines　with　higher　operational　efficiency　and　fewer　model　parameters.　Codes　are　available　at　anonymous　private　link　on　https://doi.org/10.6084/m9.figshare.23935683.