It's hard to believe that we are at the halfway point of the Spring semester and almost in March of 2019! Time has really flown by since I have started GIS here at the University of West Florida. After a much needed week off to prepare for my 8-week term class finals, this week I came back to hit the ground running with Data Classification. Data classification is when data is taken and combined to create groups that are called classes. These classes are then represented by unique symbols such as color ramps.
In this weeks lab, I was tasked to create two sets of choropleth maps that show the distribution of the population that is 65 years old and older in the Miami Dade area from Census Tract information using four common methods of data classification. The first set of maps was classified by the percentage of the population that is over the age of 65 in each census tract, and the second map series was classified by the number of citizens per census tract that are over the age of 65 and then normalized by the area of square miles to get the density. These classifications can be broken down as follows:
1. Equal Interval - Each class within the data has an equal range. For example, if you have five total classes with 200 data records, each class range would be 40.
2. Quantile - Each class has an equal number of observations within it by equal distribution. If you have 50 data points and you want five classes, each would have 10 points.
3. Standard Deviation - The classification of the data is based on the Standard Deviation bell curve graph with most points falling within the avg of one deviation away from the mean in either direction and fewer point in deviations of 2 or more from the mean.
4. Natural Breaks - Each class in the data is made to be as similar as possible for values, but as unique as possible compared to other classes for illustration distinction.
I believe that the normalized data map set based on the number of citizens over the age of 65 (pictured above) is the better series of maps to use because it shows a better representation of the distribution of senior citizens in the Miami Dade area. The percentage map can be misleading because it is only showing the percentage of the population in each census tract that is above the age of 65 whereas the map of the actual number of senior citizens per census tract normalized by the area illustrates where the heaviest densities of senior citizens are located.
Out of all my assignments so far, this one has to be one of my favorites. Even though it was all created in ArcGIS Pro, I am eager to get back into working with Adobe Illustrator as I have witnessed first hand how much more freedom you have with cartographic design and editing in Illustrator. I look forward to continuing on with an extensive segment on Choropleth maps in next weeks module. In the midst of final exams and cartography, I am grateful for all the encouragement that I have received to keep pushing, to strive for the best maps I can make, and most importantly: to ~Map On!
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