Data Quality Issues in Visualization
Recent 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.
This paper was published in the 1994 SPIE proceedings on
Visual Data Exploration and Analysis, vol. 2178, pp. 12-23.
A postscript version of this paper can be obtained by clicking
It is also available via anonymous ftp to
then get /pub/reinas/papers/spie94.quality.ps.gz.
Return to AVIS home page.