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Understanding pedestrian behavior patterns is key for building autonomous agents that can navigate among humans. We seek a learned dictionary of pedestrian behavior to obtain a semantic description of pedestrian trajectories. Supervised methods for dictionary learning are often impractical since pedestrian behaviors may be unknown a priori and manually generating behavior labels is prohibitively time consuming. We utilize a novel, unsupervised framework to create a taxonomy of pedestrian behavior observed in a specific space. First, we learn a trajectory latent space that enables unsupervised clustering to create an interpretable pedestrian behavior dictionary. Then, we show the utility of this dictionary for building pedestrian behavior maps to visualize space usage patterns and for computing distributions of behaviors in a space. We demonstrate a simple but effective trajectory prediction by conditioning on these behavior labels. While many trajectory analysis methods rely on RNNs or transformers, we develop a lightweight, low-parameter approach and show results outperforming SOTA on the ETH and UCY datasets.