The power of human vision to synthesize information and recognize pattern is fundamental to the success of visualization as a scientific method. This same power can mislead investigators who use visualization to explore georeferenced data—if data reliability is not addressed directly in the visualization process. Here, we apply an integrated cognitive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use of adjacent maps, one for data and one for reliability. This form of paired representation is compared to two methods in which data and reliability are spatially coincident (on a single map). A novel method for coincident visually separable depiction of data and data reliability on mortality maps (using a color fill to represent data and a texture overlay to represent reliability) is found to be effective in allowing map users to recognize unreliable data without interfering with their ability to notice clusters and characterize patterns in mortality rates. A coincident visually integral depiction (using color characteristics to represent both data and reliability) is found to inhibit perception of clusters that contain some enumeration units with unreliable data, and to make it difficult for users to consider data and reliability independently.