The first classification was the Equal Intervals, which breaks down the nominal data with the same difference between the categories. This works well for presenting percentages, and in this case the first set of maps demonstrate the percentages of population over 65 years of age in different regions of the county.
The second classification was the Stand Deviation, which has extremely similar results to the Natural Breaks classification. Neither of these maps were ideal because the data is not represented well by averages, as the similar values between regions are not connected to each other, and the gaps between values in the data may cause misleading categories.
The Quantile classification would be better for a map utilizing ordinal data with a ranking, versus percentages of a population. The map I created is below, to demonstrate these classifications:
It is useful to be experimenting with these different classifications. Sometimes the data is easy to sort and visualize, but other times results can be misleading and cause the data to be misinterpreted by an audience. The concepts of these classifications can seem upfront, but it can be challenging to predict how these calculations will affect different sets of data. Making and comparing these maps has gotten me more familiar with the different interpretations of results, and provides a great reference for future efforts.
Good news: I did not get stuck on this map! Although, if you want to change anything in the Legend after you have finished adjusting the font and text color, it will reset back to the software's default. Nothing too difficult to fix, but good to note!
More cartography to come! Stay safe and well.
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