Place labeling is the process of giving semantic labels to locations, such as home, work, and school. For a particular person, these labels can be computed automatically based on features of that person’s visits to these locations. A previous system called Placer used the person’s demographic data and the timing of their visits to label places with a learned decision tree. We developed Placer++ as a more accurate labeler, augmenting Placer’s features of individual visits with (1) labeled visits from other people and (2) features about the sequence of the individual’s visits. In processing sequences, we adopt structural learning techniques to take into account the relationships between visits. Accuracy increased by 8.85 percentage points over the baseline of Placer. We describe and justify the features and present our experiments on government diary data.