In 2006, the United States Congress passed the Pandemic All-Hazards Preparedness Act (PAHPA) which mandated that all emergency preparedness planning shall address at-risk populations. Further, in 2013, the reauthorization of this act, known as PAHPRA, defined at-risk individuals as "children, older adults, pregnant women, and individuals who may need additional response assistance." This vague definition leaves emergency managers, planners, and public health officials with the difficult task of understanding what it means to be at-risk. Further, once identified, the geographic location of at-risk individuals must be obtained. This research first uses the concept of social vulnerability to enhance the understanding of what it means to be "at-risk." Then, by comparing two data disaggregation techniques, areal weighted interpolation and dasymetric mapping, I demonstrate how error of estimation is affected by different scenarios of population distribution and service area overlap. The results extend an existing framework of vulnerability by stratifying factors into quantifiable and subjective types. Also, dasymetric mapping was shown to be a superior technique of data disaggregation compared to areal weighted interpolation. However, the difference in error estimates is low, 5 percent or less in 72 percent of the test cases. Only through local collaboration with community entities can emergency planners access the appropriate data to both: 1) understand the nature of at-risk individuals in their service areas and 2) spatially target resources needed to ensure all individuals are planned for in case of a bio-emergency.