From tweets to urban data sets, there has been an explosion in the volume of textual data that is associated with both temporal and spatial components. Efficiently evaluating queries over these data is challenging. Previous approaches have focused on the spatial aspect. Some used separate indices for space and text, thus incurring the overhead of storing separate indices and joining their results. Others proposed a combined index that either inserts terms into a spatial structure or adds a spatial structure to an inverted index. These benefit queries with highly-selective constraints that match the primary index structure but have limited effectiveness and pruning power otherwise. We propose a new indexing strategy that uniformly handles text, space and time in a single structure, and is thus able to efficiently evaluate queries that combine keywords with spatial and temporal constraints. We present a detailed experimental evaluation using real data sets which shows that not only our index attains substantially lower query processing times, but it can also be constructed in a fraction of the time required by state-of-the-art approaches.