In-person surveys often utilize a multi-stage sample design in which households are sampled within geographic areas called segments, improving cost efficiency by restricting the geographic range that data collectors travel. Often, segments are formed by grouping neighboring census blocks until the number of housing units in the segment is large enough to support the household sample to be selected within the segment. A simple method to combine adjacent census blocks is to sort the census block file by the census block ID. Doing so often creates segments that are not contiguous, not complete (contain holes), and not compact. Issues with contiguity and completeness create challenges for data collectors in determining which housing units to include in the sample frame. Less compact segments increase interviewer travel costs. We will review alternative approaches to forming segments with three shape-filling curves – Peano, Hilbert, and Geo-hash, evaluating the segments formed by each sorting method according to contiguity, completeness, compactness, and between-segment variance, and will present segment formation software that utilizes all four sorting methods.