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Representing Reliability

Visualizing Georeferenced Data: Representing Reliability of Health Statistics

Alan M. MacEachren
Cynthia A. Brewer

Department of Geography,
302 Walker Building
Penn State University
University Park, PA 16802

Linda W. Pickle
National Center for Health Statistics
6525 Belcrest Road
Hyattsville, MD 20782

Paper Abstract

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 geo-referenced 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, but to make it difficult for users to consider data and reliability independently.

For a complete version of this paper, the reader is referred to MacEachren, A.M., Brewer, C. A., and L.W. Pickle (1998) Visualizing Georeferenced Data: Representing Reliability of Health Statistics. Environment & Planning A, Vol. 30, No. 9, pp. 1547-1561

Sample Figures

Color Legends

Figure 1a (click image to enlarge)
Legends are depicted for the 5-class test maps (matching the three reliability representations to the three color schemes). A color shift (left) is used to create a visually integral form of data/reliability display (hue shift in conjunction with the purple-green diverging scheme and saturation shift in conjunction with the spectral and yellow-red schemes). To create visually separable data and reliability depictions, a color sequence is used to represent data categories while a texture overlay is used to represent data reliability (center). For adjacent display, each data map was paired with a smaller two-category monochrome reliability map (at 50% scale, or one quarter of the area). Shown here is a legend from one data map and from one reliability map (right).

Spectral Diverging
Figure1b (click image to enlarge)
The sample map shown (middle) depicts one of the clustered causes of mortality using the spectral color scheme to represent data categories and the visually separable texture overlay to represent those HSAs in which data are categorized as unreliable.

Seven Class Map

Figure 1c. (click image to enlarge)
For the seven-class maps tested, symbolization schemes are comparable to those of 5-class maps. Shown here is a section from one of the unclustered test maps using the purple-green scheme to depict data and texture overlay to indicate HSAs for which death rates are considered to be unreliable.
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