This is an example of how different classification methods produce different results with the same data. Here we see four possible ways of looking at the percentage of people over 65 in Escambia County, Florida. While there are similarities among them all, each has its own spin on the information.
The Lab required that we create data frames of the same information, presented using the Natural Breaks, Equal Interval, Quantile, and Standard Deviation classification methods, then choose which one best represented the data and explain why.
To begin, I added the Escambia County shapefile to the first dataframe, then created 3 more dataframes and dragged the same shapefile into each one. I renamed them according to each of the 4 classification methods. Then I went to each layer's properties, chose the Symbology tab, and chose the Field Value PCT_65ABV to get the data about the percentage of population over 65. Using Graduated Colors, I chose a ramp that would suit the data, ranging from light to dark. I then selected one of the Classification types we were assigned, making sure there were 5 classes. When the labels were created I formatted them to 2 decimal places.
Once I had all the required data frames, I switched to Layout view and "owned my map" by including the usual required elements, and using the design concepts we have learned over the past few weeks. At this point I realized the color ramp I had chosen for Standard Deviation didn't match the data - it went from light to dark, not reflecting the increased values as you move away from the mean. I went back and chose a different one that has a light color for the mean, and darker colors at either limit.
After looking at all 4 classification methods, I decided the Standard Deviation method suited the data best. The color scheme makes it clear where there is an average percentage of people over 65, and where there are far more and far fewer.
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