Geolocation of social media users relies on the assumption that language carries sufficient geographic information. Preliminarily, we need to identify discrete geographic regions, since location is usually given as continuous latitude/longitude tuples. Most studies use some form of clustering to discretize the continuous coordinates (Han et al., 2016). However, the resulting regions do not always correspond to existing linguistic areas. Consequently, accuracy at 100 miles tends to be good, but degrades for finer-grained distinctions, when different linguistic regions get lumped together. We describe a new algorithm, Point-to-City (P2C), an iterative k-means-based method for clustering geographic coordinates and associating them with towns. We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with k-d tree labels. Even though P2C results in substantially more labels than the baseline, model accuracy increases significantly over using traditional labels at the fine-grained level, while staying comparable at 100 miles. The results suggest that identifying meaningful linguistic areas is crucial for improving geolocation at a fine-grained level.