Learning semantically coherent location embeddings can benefit downstream applications such as human mobility prediction. However, the conflation of geographic and semantic attributes of a location can harm such coherence, especially when semantic labels are not provided for the learning. To resolve this problem, in this paper, we present a novel unsupervised method for learning location embeddings from human trajectories. Our method advances traditional transition-based techniques in two ways: 1) we alleviate the disturbance of geographic attributes on the semantics by disentangling the two spaces; and 2) we incorporate spatio-temporal attributes and regular visiting patterns of trajectories to capture the semantics more accurately. Moreover, we present the first quantitative evaluation on location embeddings by introducing an original query-based metric, and we apply the metric in experiments on two Foursquare datasets, which demonstrate the improvement our model achieves on semantic coherence. We further apply the learned embeddings to two downstream applications, namely next point-of-interest recommendation and trajectory verification. Empirical results demonstrate the advantages of the disentangled embeddings over four state-of-the-art unsupervised location embedding methods.