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Colormaps for visualization

Colormap types

GeoHub offers the user to enhance their visualization even further with the ability to change to the color ramp they prefer and suitable to display the data. It offers the user to select from three panels containing 10-17 color ramps of their choice.


Sequential color schemes

Sequential data classes are logically arranged from high to low, and this stepped sequence of categories should be represented by sequential lightness steps.

Low data values are usually represented by light colors and high values represented by dark colors.

Transitions between hues may be used in a sequential scheme, but the light-to-dark progression should dominate the scheme.

Terrain slope categories or population densities, for example, are well represented by sequential color schemes.


Diverging color schemes

Diverging/diverging schemes are the only two-variable schemes that depart from the idea of a direct overlay of the component one-variable schemes.

Place a different moderately dark hue at each of the four corners of the legend.

These four hues represent categories that are extremes for both variables.

Place a very light or white color at the center of the legend, creating an appropriately light color for the class that contains the critical value or midpoint of both variables.

The remaining colors are lighter than the corners, because they contain the midpoint of one of the two variables, and they are transitional hues that lie between their adjacent hues.

The color circle is essentially stretched around the perimeter of the legend and lightness adjusted in response to critical values within the data ranges of both variables.


Qualitative color schemes

Qualitative schemes use differences in hue to represent nominal differences, or differences in kind.

The lightness of the hues used for qualitative categories should be similar but not equal.

Assign the lightest, darkest, and most saturated hues in the scheme to categories that want emphasis on the map.

This is suitable to represent categorical data such as land use cover, for example.

Next step

We are going to know an important concept of classification method for better visualization in the next section.