Uncertainty in data analysis has been a critical topic in numerous fields, such as public health, medicine, civil engineering, ecology and other natural sciences, and many of the social sciences, including geospatial information sciences. It may occur in any step of a study, such as collecting, recording, and analyzing data, and interpreting analysis results. Uncertainty is often propagated to analysis outcomes. The outcomes to which serious uncertainties are transferred likely yield misleading conclusions about a phenomenon, and constitute inaccurate results. Locational uncertainty, which is the difference between a true and a represented location, is a unique source of uncertainty in a spatial data analysis. Furthermore, locational uncertainty may interact with uncertainties from other sources (e.g., measurement, specification, sampling, or stochastic noise), and makes outcomes more unreliable. Propagation of uncertainty has been widely investigated. However, locational uncertainty propagation and combining uncertainties from different sources merit more attention, because the propagation and combination of uncertainties are quite complicated and can seriously corrupt analysis outcomes. This research examines uncertainty in spatial data analysis using two sources of public health data: Florida cancer data and Syracuse blood lead level data. The research 1) presents a study about how locational uncertainty propagates through an analysis involving an urban hierarchy in terms of spatial relationships between poverty and cancer using the Florida cancer data, 2) explores relationships and propagations of location and measurement uncertainties using pediatric blood lead level data for Syracuse, New York, and 3) examines a reverse transformation (i.e., a geometric centerline recovery method) from a kernel density surface to points using the Florida cancer data.