Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this paper, we aim to infer values at non-sensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. Firstly, data exhibits distinct patterns at both long- and short-term temporal scales, which should be analyzed separately. Secondly, short-term patterns contain more delicate relations including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short-term and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long- and short-term relations, achieving state-of-the-art performance against existing methods.

翻译：传感器是环境监测的关键，为智慧城市的多方面应用带来益处，例如通过提供实时空气质量信息辅助人类决策。然而，由于成本高昂，大规模部署传感器并不现实，导致数据采集稀疏。因此，如何实现细粒度数据测量长期以来一直是亟待解决的问题。本文旨在基于可用传感器的观测值推算出非传感器位置的数值（称为时空推理），其中捕捉数据间的时空关系至关重要。我们的研究发现两个此前未被探索的重要洞察。首先，数据在长期和短期时间尺度上呈现出不同模式，应分别进行分析。其次，短期模式包含更精细的关系，包括同时跨越空间和时空维度的联系，而长期模式则涉及高层次的时间趋势。基于这些观察，我们提出解耦短期与长期模式的建模。具体而言，我们引入联合时空图注意力网络，以学习短期模式中跨空间与时间的关系。此外，我们提出一种采用时间跳跃策略的图循环网络，以缓解梯度消失问题并建模长期依赖关系。在四个公开真实数据集上的实验结果表明，我们的方法有效捕捉了长短期关系，实现了相较于现有方法的最优性能。