Background

Mapping data to color has a rich history and several well-tested color schemes have emerged. Most of these, however are defined for scalar data where the scalar value indexes a one-dimensional table that returns an RGB color triple. Schemes assign color to different, usually disjoint materials and then use standard blending functions to handle areas where materials overlap or mix together. This process forces graphical rendering of simulations.

Technology

Framework generalizes straightforward interpolation methods defined for the bivariate and trivariate using generalized barycentric interpolation. Instead of using an N-sided polygon, optimized is the placement of the vertices in terms of the pairwise correlation of the variables, such that factors with similar behavior map to similar primary color. The proposed technology will yield an automatic and data-driven method for visually encoding similarities of variables which is far more accurate. This framework makes use of the HSL color space, innovatively mapping it into the interior of the N-sided polygon. Color space is natural since color is often expressed as HSL triple.

Advantages

Using HSL space users will be able to see the distribution of the data in the context of the colors they are mapped into. Users can paint into the space, using objects selected in the native data space as a reference for the color to be painted

Application

Data occurs frequently in many applications- demographic assessment, environmental monitoring, scientific simulations.