Data Quality Issues in Visualization

Recent eeorts in visualization have concentrated on high volume data sets from numerical simulations and medical imaging. There is another large class of data, characterized by their spatial sparsity with noisy and possibly missing data points, that also need to be visualized. Two places where these type of data sets can be found are in oceanographic and atmospheric science studies. In such cases, it is not uncommon to have on the order of one percent of sampled data available within a space volume. Techniques that attempt to deal with the problem of lling-in-the-holes range in complexity from simple linear interpolation to more sophisticated multiquadric and optimal interpolation techniques. These techniques will generally produce results that do not fully agree with each other. To avoid misleading the users, it is important to highlight these diierences and make sure the users are aware of the idiosyncrasies of the diierent methods. This paper compares some of these interpolation techniques on sparse data sets and also discusses how other parameters such as conndence levels and drop-oo rates may be incorporated into the visual display.