One of our tasks this week was to use a diverging color scheme to represent positive and negative population change in the counties of our chosen state. We first needed to apply a suitable projection for our state. In my case, I chose UTM Zone 13N for Colorado. This UTM zone covers the majority of Colorado, leaving only a strip along the westernmost border outside the zone and thus mildly distorted. If we were required to work with detailed spatial statistics this week or were focused on that section of Colorado, I would have chosen a different projection or created a custom one.
The next step was to normalize the data. Rather than use the normalization options in the layer properties, we created a new attribute in which we used the field calculator to calculate the percentage of population change by county from 2010 to 2014. This was the field we used as the basis for our choropleth maps.
We then faced three main tasks: Decide on the classification details (method and number of classes), how to symbolize the classes, and how to design the legend. I began by using the Natural Breaks classification method with seven classes. Seven classes would allow for one class representing minimal population change and three classes each for population increase and decrease. Natural Breaks was a good start, but, for this general reference map without any other specified purpose, I wanted to create a more symmetrical and directly comparable classification scheme to more easily see how counties compared. I manually adjusted the population increase and decrease classes to mirror each other.
For symbolizing these classes, I based the diverging color scheme on one included in ArcMap. I did adjust the HSV values, primarily saturation, to even out the contrast somewhat. The orange to red color ramp seems natural to represent a decline and is easily distinguished from the green color ramp. The yellow representing the middle class is distinguishable from both ramps while fitting in to both color schemes.
The final piece of the layout was the legend. My original intention was to use the legend property options to create the legend, but these were too limited. The data naturally fall into three categories (as I have classified them), so I converted the legend to graphics in order to split the legend into three categories. This created a legend that was more legible and intuitive to interpret.
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