VISUALIZING GEOREFERENCED DATA - REPRESENTING RELIABILITY OF HEALTH-STATISTICS

The power of human vision to synthesize information and recognize patt ern is fundamental to the success of visualization as a scientific met hod. This same power call mislead investigators who use visualization to explore georeferenced data-if data reliability is not addressed dir ectly in the visualization process. Here, we apply an integrated cogni tive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use o f adjacent maps, one for data and one for reliability. This form of pa ired representation is compared to two methods in which data and relia bility are spatially coincident ton a single map). A novel method for coincident visually separable depiction of data and data reliability o n mortality maps (using a color fill to represent data and a texture o verlay 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 rate s. A coincident visually integral depiction (using color characteristi cs to represent both data and reliability) is found to inhibit percept ion of clusters that contain some enumeration units with unreliable da ta, and to make it difficult for users to consider data and reliabilit y independently.