Summary - "Introduction to Visualizing Data Validity"
M.Goodchild, B.Buttenfield & J. Wood

In "Visualization in Geographical Information Systems", edited by Hilary M, Hearnshaw & David J. Unwin

The paper states that in order to approach issues and display of data validity, the priorities for the initiative include:

• Development of frameworks linking spatial data quality components with visualization methods.
• Implementation of prototype displays.
• Database management to support error tracking and to facilitate graphical depiction of error propagation during GIS operations.

The types of validity that may be discovered or measured in geographical data most commonly include error and accuracy. Accuracy measures the discrepancy from modeled or an assumed value, while error measures the discrepancy from the true value. Because it is impossible to collect a complete dataset, therefore the value may be inferred. For this reason, many standards for data quality assessment include indices of accuracy rather than error. The paper gives an example of root mean square error (RMSE), which is in fact an accuracy measure of perpendicular distance using a model of geometry as reference.

Visualization let us visualize the validity of the data. It also acts as an effective communication medium, conveying the effects of error on modeling the real world.

However, visual representation of validity is controlled by a set of rules provided by statistics. Statistics provide a mathematical framework for describing and manipulating uncertainly. It provides a means of dealing systematically with incomplete information. Statistics also provide a means of handling uncertainly that is responsive to its form but independent of the process by which it was generated. But there are circumstances where errors sufficiently large or unique, and where the generalizations of statistics would have no relevance. The use of statistics also implies that uncertainty is quantifiable, whereas the validity of spatial data is often recorded qualitatively.

There are two modes if visualization can be applied to display the validity of GIS data. They are static mode of display and dynamic cartographic

Static mode of display is basically traditional cartographic displays, they provide static snapshots of data surfaces and objects, affording visualization at specific or selected phases of data processing.

Dynamic cartography is also known as map animation. Dynamic displays provide advantage to viewers and designer of GIS displays, particularly in the context of validity and metadata. The other advantage is that for position accuracy (symbol movement overcomes the problem of viewers attaching too much weight to particular cartographic representations).

• Refined methods for estimating error propagation rates, particularly in environmental monitoring where policy decision are often based upon visual display, such as wetlands protection and climatic change studies.
• The potential for cognitive overload in displays where data and data validity are embedded forms an important area for evaluation.
• The application of innovative graphical techniques to display of validity has only hypermedia may expand GIS viewer capabilities of exploration of data pattern and data validity.

Sample images

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