Public data for transportation planning often undersamples underserved population groups. For example, the margin of error (MOE) for population counts at census tract level for people with disabilities can make up 36% of the estimate. If we are to move towards a more equity-aware profession, the data we use need to account for this. In this work with Bingqing Liu and Farnoosh Namdarpour, we develop an algorithm to aggregate zones together to ensure sufficient data reliability for different underserved groups. We apply this algorithm for NYC to produce a set of 574 zones that result in lower MOEs compared to similarly aggregated zonal structures. We also show that this reduction in error translates to reduced variance in downstream transportation applications, such as population synthesis. The data and algorithm are publicly available and can work well with data standards like MDS.