Systematic generalization is the ability of a machine learning model to perform well on a family of test examples that are out-of-distribution with respect to the training examples in a systematic way. To succeed, compositionality of useful information learned from the training data is required. One well-studied problem instance is single path relational reasoning where a model is provided with small relational graphs and is tasked with predicting the relation between a head and target node. Crucially, this task can be solved by identifying a single resolution path between the head and the target and then using rules to sequentially compose relations until a relationship between the head and target node can be inferred. Previous work has shown that graph-based transformers and text-based large language models perform poorly on single path reasoning tasks, while some rule-based and neuro-symbolic methods can solve them with near-perfect accuracy. In this paper, we propose a Spatio-Temporal Reasoning benchmark (STaR) based on classic relational calculi, which generalizes the single path relational reasoning problem to require the aggregation of partial information from multiple paths between the head and target node. Our experiments demonstrate that many state-of-the-art neuro-symbolic, transformer and graph neural network methods perform poorly on STaR. Our data and code are available at "https://github.com/erg0dic/gnn-sg"