Linear, Adjusted Progression, and Diverging Color Ramps
I did not find much difference between the linear and adjusted progression color ramps. Considering the amount of arithmetic involved in creating the adjusted progression color ramp, I thought that a more drastic difference would have been very apparent. Only a slight variation is seen amongst the three lightest colors by my eyes from the two screenshots (and that’s only after staring at both images for several minutes). This non-evident color variation may be a result of seeing the color ramps in swatches rather than in a map dispersed across a large area of land like counties in a state or states in a country. Based on this exercise, I think adjusting the hue and saturation of outliers in the data will provide better visual contrast to a map reader rather than slight variations to the RGB values of the entire data set.
Linear Progression
I chose the Natural Breaks classification to represent
population gain/loss in Georgia counties.
The range of the population change was about 21 percent (-5.89% -
16.58%) so I didn’t want to use too many classes for the data. With too many classes, the counties with the
biggest and smallest changes would have been washed out with the gradual
variation throughout the state. Because I
wanted to highlight just a few outliers this way, Equal Interval and Quantile
were not the best choice for this choropleth map.
I chose an odd number of classes to have a critical class using
a diverging color ramp which rendered the expected results; major cities like
Savannah and Atlanta saw in increase in population while rural areas saw a
decrease in population. By not having
too many classes, the map reader can easily see which counties saw the most
drastic increase/decrease in population due to their contrast from adjacent
counties. More counties had their
populations decrease (though only slightly) relative to the counties that had
their populations increase and it’s clear with the critical class included in
the diverging color ramp.
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