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dc.contributor.authorPang, Alex
dc.contributor.authorFurman, Jeff
dc.contributor.authorNuss, Wendell
dc.date.accessioned2015-01-12T17:58:37Z
dc.date.available2015-01-12T17:58:37Z
dc.date.issued1994
dc.identifier.urihttp://hdl.handle.net/10945/44293
dc.description.abstractRecent efforts 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 filling 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 differences and make sure the users are aware of the idiosyncrasies of the different methods. This paper compares some of these interpolation techniques on sparse data sets and also discusses how other parameters such as confidence levels and drop off rates may be incorporated into the visual display.en_US
dc.description.sponsorshipThis work is partly funded by ONR grant N00014 92 J 1807.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleData quality issues in visualizationen_US
dc.typeArticleen_US
dc.contributor.departmentMeteorology


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