This histogram is skewed to the right, meaning that there the majority of the community areas in Chicago have less than 20% of the population made up of those over the age of 65. * The unclassed model shows all the data points in a range from the smallest percentage to the largest percentage visualized with a light-to-dark color scheme. This is not connected to the histogram. * The Quantile classification method breaks the data into three classes with the same number of values, because there are 77 community areas my data is divided into 7 classes with 11 community areas in each. * I chose three classes for natural breaks because the histogram shows two high peaks, the first at 0.12 and the second at 0.16 the values after each of these peaks seemed like a good place for the data to be separated. * I chose five classes for equal intervals because the data ranges from ~0.05 to ~0.30 this means that the values in each class will range from 0.05-0.09, 0.10-0.14, 0.15-0.19, 0.20-0.24, and 0.25-0.30. I think these breaks are nice and easy to interpret while not resulting in too many colors making the map difficult to distinguish. * I chose the breaks 0.12, 0.16, and 0.21 for my manual interval/threshold model because the histogram shows major changes at these points. Therefore they seemed like the natural place for a break between classes to fall.