Module 4: Color Concepts & Choropleth Mapping

In this week's lab, I explored using color and selecting color ramps in ColorBrewer, as well as applying these concepts to create meaningful choropleth maps. I created a map of population change from 2010 to 2014 in Georgia counties. I normalized the data and produced a choropleth map of population change using a diverging color scheme with 6 classes divided around a midpoint of 0.

Fig 1. Choropleth map of population change in Georgia counties using a diverging color scheme.



For the Georgia population data set, I chose to divide the data set into 6 classes around a midpoint of 0: 3 showing a decrease in population and 3 showing an increase in the population. The classes were grouped based on a manual classification closely based on the Natural Break classification, where the midpoint was adjusted to 0 with the other groups unchanged. Natural Breaks classification creates classes based on similarities in values in the data, thereby revealing patterns in the data. By adjusting the midpoint to 0 and leaving the others grouped by Natural Breaks, I was able to create two groups of classes, representing decreases and increases in the population that could be distinguished on the map, but also retaining the natural patterns in the data.  



Because I created two groups of classes, one for increases in population and one for decreases in population, I chose a diverging color scheme to distinguish between the two groups. I used ColorBrewer to select a diverging color scheme with 6 classes. I chose a color scheme with increases in population as variations in values of blue hues because blue is usually associated with positive/soothing values and decreases in population as variations in values of red hues because red is usually associated with negative/warning values. By varying the value or lightness of each hue, each class becomes distinguishable but remains associated with each group. In this way, the patterns in the data can be visualized on the map.

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